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Quantum computing

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Modular, scalable hardware architecture for a quantum computer

A new quantum-system-on-chip enables the efficient control of a large array of qubits, moving toward practical quantum computing.

May 29, 2024

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ACM Transactions on Quantum Computing

Volume 5, Issue 2

ACM Transactions on Quantum Computing publishes high-impact, original research papers and selected surveys on topics in quantum computing and quantum information science. The journal targets the quantum computer science community with a focus on the theory and practice of quantum computing including but not limited to: models of quantum computing, quantum algorithms and complexity, quantum computing architecture, principles and methods of fault-tolerant quantum computation, design automation for quantum computing, quantum programming languages and systems, distributed quantum computing, quantum networking, quantum security and privacy, and applications (e.g. in machine learning and AI) of quantum computing.

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TQC Accepted for Scopus and Clarivate ESCI Coverage

ACM  Transactions on Quantum Computing  (TQC) has been accepted for coverage in Elsevier’s Scopus and Clarivate’s ESCI. Similar to Web of Science, Scopus is an extensive yet selective abstract and citation database that provides comprehensive coverage of peer-reviewed journals, books, conference abstracts, and patents across the natural sciences, social sciences, arts, and humanities. By having its content included in Scopus, TQC's content will be discoverable at 7,000 of the world's top research institutions.

ACM Updates Its Peer Review Policy

ACM is pleased to announce that its Publications Board has approved an updated  Peer Review Policy . If you have any questions regarding the update, the associated  FAQ  addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications,  Scott Delman .

New ACM Policy on Authorship ACM has a new Policy on Authorship , covering a range of key topics, including the use of generative AI tools.  Please familiarize yourself with the new policy and the associated list of Frequently Asked Questions .

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• Volume 5, Issue 2 June 2024 Issue-in-Progress EISSN: 2643-6817 View Table of Contents

On the Success Probability of Quantum Order Finding

KTH Royal Institute of Technology, Stockholm, Sweden and Swedish Armed Forces, Swedish NCSA, Stockholm, Sweden

An Optimal Linear-combination-of-unitaries-based Quantum Linear System Solver

Université Paris Cité, Paris, France

Université Paris Cité and QC Ware, Paris, France

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Major Breakthrough As Quantum Computing in Silicon Hits 99% Accuracy

By University of New South Wales January 19, 2022

L-R Asaad, Morello, Madzik (composite image): Serwan Asaad, Andrea Morello, and Mateusz Mądzik are lead authors of the UNSW paper which demonstrated 99 percent error-free quantum operations. Credit: Kearon de Clouet / UNSW

UNSW Sydney-led research paves the way for large silicon-based quantum processors for real-world manufacturing and application.

Australian researchers have proven that near error-free quantum computing is possible, paving the way to build silicon-based quantum devices compatible with current semiconductor manufacturing technology.

“ Today’s publication in Nature shows our operations were 99 percent error-free,” says Professor Andrea Morello of UNSW, who led the work.

The silicon nanoelectronic device used to hold the quantum processor was built using methods compatible with industry standards for existing computer chips. (The authors demonstrated universal quantum logic operations using a pair of ion-implanted 31P nuclei in a silicon nanoelectronic device. The device is manufactured using methods compatible with the industry-standard processes used for all existing computer chips.) Credit: Tony Melov / UNSW

“When the errors are so rare, it becomes possible to detect them and correct them when they occur. This shows that it is possible to build quantum computers that have enough scale, and enough power, to handle meaningful computation.”

This piece of research is an important milestone on the journey that will get us there,” Prof. Morello says.

Quantum computing in silicon hits the 99% threshold

Morello’s paper is one of three published today in Nature that independently confirm that robust, reliable quantum computing in silicon is now a reality. This breakthrough features on the front cover of the journal.

• Morello et al achieved 1-qubit operation fidelities up to 99.95 percent, and 2-qubit fidelity of 99.37 percent with a three-qubit system comprising an electron and two phosphorous atoms, introduced in silicon via ion implantation.
• A Delft team in the Netherlands led by Lieven Vandersypen achieved 99.87 percent 1-qubit and 99.65 percent 2-qubit fidelities using electron spins in quantum dots formed in a stack of silicon and silicon-germanium alloy (Si/SiGe).
• A RIKEN team in Japan led by Seigo Tarucha similarly achieved 99.84 percent 1-qubit and 99.51 percent 2-qubit fidelities in a two-electron system using Si/SiGe quantum dots.

A visualization of UNSW’s three-qubit system, which can perform quantum logic operations with over 99% accuracy. (Quantum operation fidelities above 99% were obtained in a three-qubit silicon quantum processor. The first two qubits (Q1, Q2) are the nuclear spins of individually-implanted phosphorus atoms (red spheres). The third qubit (Q3) is the spin of an electron that wraps around both nuclei (shiny ellipse).) Credit: Tony Melov / UNSW

The UNSW and Delft teams certified the performance of their quantum processors using a sophisticated method called gate set tomography, developed at Sandia National Laboratories in the U.S. and made openly available to the research community.

Morello had previously demonstrated that he could preserve quantum information in silicon for 35 seconds, due to the extreme isolation of nuclear spins from their environment.

The three qubits can be prepared in a quantum entangled state, which unlocks the exponential power of quantum computers. (Nuclear spins are exceptionally good qubits, because of their exceptional isolation from the environment. This same feature, however, makes it difficult for them to interact and perform quantum logic operations. The team’s breakthrough consists in using a common electron to mediate the interaction, leading to high-fidelity universal quantum logic operations. Furthermore, the electron itself is a high-quality qubit, and can be placed in a fully quantum-entangled state with the two nuclei.) Credit: Tony Melov / UNSW

“In the quantum world, 35 seconds is an eternity,” says Prof. Morello. “To give a comparison, in the famous Google and IBM superconducting quantum computers the lifetime is about a hundred microseconds – nearly a million times shorter.”

But the trade-off was that isolating the qubits made it seemingly impossible for them to interact with each other, as necessary to perform actual computations.

Nuclear spins learn to interact accurately

Today’s paper describes how his team overcame this problem by using an electron encompassing two nuclei of phosphorus atoms.

“If you have two nuclei that are connected to the same electron, you can make them do a quantum operation,” says Dr. Mateusz Madzik, one of the lead experimental authors.

The three-qubit system paves the way to scaling up the quantum processor in the future, because the electron can be easily entangled with other electrons or moved across the chip. (The three-qubit entangled state of nuclei and electron paves the way to scaling up the quantum processor in the future. The electron can be easily entangled with other electrons, or physically moved across the chip. In this way, the UNSW team will be able to manufacture and operate large arrays of qubits capable of robust and useful computations.) Credit: Tony Melov / UNSW

“While you don’t operate the electron, those nuclei safely store their quantum information. But now you have the option of making them talk to each other via the electron, to realize universal quantum operations that can be adapted to any computational problem.”

“This really is an unlocking technology,” says Dr. Serwan Asaad, another lead experimental author. “The nuclear spins are the core quantum processor. If you entangle them with the electron, then the electron can then be moved to another place and entangled with other qubit nuclei further afield, opening the way to making large arrays of qubits capable of robust and useful computations.”

Serwan Asaad, one of the lead authors. Credit: UNSW

David Jamieson, research leader at the University of Melbourne, adds: “The phosphorous atoms were introduced in the silicon chip using ion implantation, the same method used in all existing silicon computer chips. This ensures that our quantum breakthrough is compatible with the broader semiconductor industry.”

All existing computers deploy some form of error correction and data redundancy, but the laws of quantum physics pose severe restrictions on how the correction takes place in quantum computer. Prof. Morello explains: “You typically need error rates below 1 percent, to apply quantum error correction protocols. Having now achieved this goal, we can start designing silicon quantum processors that scale up and operate reliably for useful calculations.”

About the three papers

Semiconductor spin qubits are well positioned to become the platform of choice for reliable quantum computers. They are stable enough to store quantum information over extended periods of time and can be scaled up using techniques familiar from modern semiconductor manufacturing technologies.

Mateusz Mądzik, one of the lead authors. Credit: UNSW

“Until now, however, the challenge has been performing quantum logic operations with sufficiently high accuracy ,” Prof. Morello says.

“Each of the three papers published today shows how this challenge can be overcome to such a degree that errors can be corrected faster than they appear.”

• The UNSW team led by Andrea Morello created two-qubit universal quantum logic operations between two nuclear spins formed by phosphorous donors, introduced in silicon via the industry-standard method of ion implantation. The quantum operations involved an electron, whose probability wave is spread across both nuclei. The individual nuclei operated with fidelities up to 99.95%, and two-qubit operations with 99.37% fidelity, as certified by gate set tomography (GST). The electron spin is itself a qubit, which can be entangled with the two nuclei to create a three-qubit quantum entangled state, with fidelity of 92.5%. Paper: https://www.nature.com/articles/s41586-021-04292-7 ; DOI: https://doi.org/10.1038/s41586-021-04292-7
• The Delft team led by Lieven Vandersypen created a two-qubit system in a material made from a carefully grown stack of silicon and silicon-germanium alloy (Si/SiGe). The quantum information is encoded in the spins of electrons confined in quantum dots. They applied gate set tomography not only to quantify, but also to improve the accuracy of the quantum operations and reached 99.5 percent fidelity on the two-qubit logic gate. “Pushing the two-qubit gate fidelity well beyond 99 percent required improved materials and specially designed qubit control and calibration methods,” Xiao Xue, lead author of the publication in Nature , said. Paper: https://doi.org/10.1038/s41586-021-04273-w
• The RIKEN group in Tokyo , led by Seigo Tarucha, one of the founders of the field of quantum dots, took a similar path, creating two electron quantum bits in Si/SiGe using the same material stack produced by the Delft group. They achieved single-qubit fidelities of 99.8% and two-qubit fidelity of 99.5 percent with very fast electrical operations. They measured the fidelity using randomized benchmarking. Paper: https://www.nature.com/articles/s41586-021-04182-y ; DOI: https://doi.org/10.1038/s41586-021-04182-y

Collaborations and exchanges

While the three papers report independent results, they illustrate the benefits that arise from free academic research, and the free circulation of ideas, people and materials. For instance, the silicon and silicon-germanium material used by the Delft and RIKEN groups was grown in Delft and shared between the two groups. The isotopically purified silicon material used by the UNSW group was provided by Kohei Itoh, from Keio University in Japan.

The gate set tomography (GST) method, which was key to quantifying and improving the quantum gate fidelities in the UNSW and Delft papers, was developed at Sandia National Laboratories in the US, and made publicly available. The Sandia team worked directly with the UNSW group to develop methods specific for their nuclear spin system, but the Delft group was able to independently adopt it for its research too.

There has also been significant sharing of ideas through the movement of people between the teams, for example:

• Mateusz Madzik, an author on the UNSW paper, is now a postdoctoral researcher with the Delft team.
• Serwan Asaad, an author on the UNSW paper, was formerly a student at Delft.
• Lieven Vandersypen, the leader of the Delft team, spent a five-month sabbatical leave at UNSW in 2016, hosted by Andrea Morello.
• The leader of the material growth team, Giordano Scappucci, is a former UNSW researcher.

The UNSW-led paper is the result of a large collaboration, involving researchers from UNSW itself, University of Melbourne (for the ion implantation), University of Technology Sydney (for the initial application of the GST method), Sandia National Laboratories (Invention and refinement of the GST method), and Keio University (supply of the isotopically purified silicon material).

Mateusz T. Madzik 1,2 , Serwan Asaad 1,2 , Akram Youssry 3,4, Benjamin Joecker 1,2 , Kenneth M. Rudinger 5 , Erik Nielsen 5 , Kevin C. Young 5 , Timothy J. Proctor 5 , Andrew D. Baczewski 6 , Arne Laucht 1,2 , Vivien Schmitt 1,2 , Fay E. Hudson 1 , Kohei M. Itoh 7 , Alexander M. Jakob 8,2 , Brett C. Johnson 8,2 , David N. Jamieson 8,2 , Andrew S. Dzurak 1 , Christopher Ferrie 3 , Robin Blume-Kohout 5 , and Andrea Morello 1,2

Organisations

1 School of Electrical Engineering and Telecommunications, UNSW Sydney, Australia 2 Centre for Quantum Computation and Communication Technology, Australia 3 University of Technology Sydney 4 Ain Shams University, Cairo, Egypt 5 Sandia National Laboratories, Albuquerque and Livermore, USA 6 Center for Computing Research, Sandia National Laboratories, Albuquerque, NM 87185, USA 7 School of Fundamental Science and Technology, Keio University, Kohoku-ku, Yokohama, Japan 8 School of Physics, University of Melbourne, Melbourne, VIC 3010, Australia

Funding acknowledgment

The UNSW-UTS consortium was created as part of the AUSMURI Project, a multi-university Australia-US initiative, funded in Australia through the Defence Department’s Next Generation Technologies Fund. The AUSMURI project aims at using multi-qubit systems to reduce the overall quantum gate errors. This result is a key breakthrough in the direction of high-fidelity quantum processors in silicon.

Further funding came from the US Army Research Office, whose silicon quantum computing initiative supports UNSW, Melbourne, and Sandia National labs.

The ARC Centre of Excellence for Quantum Computation and Communication Technology supported the work at UNSW and Melbourne. The quantum device was fabricated using facilities in the UNSW node of the Australian National Fabrication Facility (ANFF).

Work at Delft was supported by the Dutch Government, and by the US Army Research Office, through the same scheme supporting the UNSW work.

The work at RIKEN was funded through several Japan government grants.

More on SciTechDaily

World’s Fastest 2-Qubit Gate: Breakthrough for the Realization of Ultrafast Quantum Computers

Engineers Develop a Radical New Architecture for Quantum Computing

Key Elements Achieved for Fault-Tolerant Quantum Computation in Silicon Spin Qubits

Artificial Atoms in Silicon “Quantum Dot” Create Stable Qubits for Quantum Computing

Physicists Show That Precision Atom Qubits Can “Talk” to Each Other

Quantum Computing Breakthrough: Silicon Qubits Interact at Long-Distance

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Researchers Perform Logic Operation and Error Correction in a Quantum Register

6 comments on "major breakthrough as quantum computing in silicon hits 99% accuracy".

Let’s take a look at the new document discussing parts of the original system and the analogy of looking at the problem in a nutshell.

One or two qubits? C’mon man.

I searched this article for the words: Temperature, cryogenics, cooling ect. The only time superconductivity is used is in reference to the Google quantum computer. Does this think-box use cryo?

How do you access fifth and sixth dimensional physics. You use an electron quantum physics Matrix. By going small you jump up

If these quantum dots are built in planar systems, its seems the interactions are along the linear interfaces between dots – does this 1-d topology limit the number of qubits that can be in play at once? jr

Simply fantastic seeing science being used as a noncompetitive collaboration between all sciences and educational institutes this has been a dream come true and if more academics were to collaborate ( without one sightedness ) we can move our understandings along much faster, spread the wealth to all representative academics, adding to the silicon structure with what I have read on this forum the filtering of noise in the quantum qubit by elimination of background errors, threw the wave function of the qubit work and the silicon nonelectronic device to hold the processor seem to be complimentary.

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• New Technique Could Help Build Quantum Computers of the Future

Key Takeaways

• Berkeley Lab researchers have reported a major advancement that could bring us closer to a scalable quantum computer.
• Using a femtosecond laser during experiments which explore the role of hydrogen in qubit formation, the researchers developed a method that programs the formation of telecom-band optical qubits in silicon for large-scale manufacturing.
• The technique could enable scalable quantum computers of the future by building on current silicon-based computing infrastructure.

Quantum computers have the potential to solve complex problems in human health, drug discovery, and artificial intelligence millions of times faster than some of the world’s fastest supercomputers. A network of quantum computers could advance these discoveries even faster. But before that can happen, the computer industry will need a reliable way to string together billions of qubits – or quantum bits – with atomic precision.

Connecting qubits, however, has been challenging for the research community. Some methods form qubits by placing an entire silicon wafer in a rapid annealing oven at very high temperatures. With these methods, qubits randomly form from defects (also known as color centers or quantum emitters) in silicon’s crystal lattice. And without knowing exactly where qubits are located in a material, a quantum computer of connected qubits will be difficult to realize.

But now, getting qubits to connect may soon be possible. A research team led by Lawrence Berkeley National Laboratory (Berkeley Lab) says that they are the first to use a femtosecond laser to create and “annihilate” qubits on demand, and with precision, by doping silicon with hydrogen.

The advance could enable quantum computers that use programmable optical qubits or “spin-photon qubits” to connect quantum nodes across a remote network. It could also advance a quantum internet that is not only more secure but could also transmit more data than current optical-fiber information technologies.

“This could carve out a potential new pathway for industry to overcome challenges in qubit fabrication and quality control.” – Thomas Schenkel, senior scientist, Accelerator Technology & Applied Physics Division

“To make a scalable quantum architecture or network, we need qubits that can reliably form on-demand, at desired locations, so that we know where the qubit is located in a material. And that’s why our approach is critical,” said Kaushalya Jhuria, a postdoctoral scholar in Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) Division. She is the first author on a new study that describes the technique in the journal Nature Communications . “Because once we know where a specific qubit is sitting, we can determine how to connect this qubit with other components in the system and make a quantum network.”

“This could carve out a potential new pathway for industry to overcome challenges in qubit fabrication and quality control,” said principal investigator Thomas Schenkel , head of the Fusion Science & Ion Beam Technology Program in Berkeley Lab’s ATAP Division. His group will host the first cohort of students from the University of Hawaii in June as part of a DOE Fusion Energy Sciences-funded RENEW project on workforce development where students will be immersed in color center/qubit science and technology.

Forming qubits in silicon with programmable control

The new method uses a gas environment to form programmable defects called “color centers” in silicon. These color centers are candidates for special telecommunications qubits or “spin photon qubits.” The method also uses an ultrafast femtosecond laser to anneal silicon with pinpoint precision where those qubits should precisely form. A femtosecond laser delivers very short pulses of energy within a quadrillionth of a second to a focused target the size of a speck of dust.

Spin photon qubits emit photons that can carry information encoded in electron spin across long distances – ideal properties to support a secure quantum network. Qubits are the smallest components of a quantum information system that encodes data in three different states: 1, 0, or a superposition that is everything between 1 and 0.

With help from Boubacar Kanté, a faculty scientist in Berkeley Lab’s Materials Sciences Division and professor of electrical engineering and computer sciences (EECS) at UC Berkeley, the team used a near-infrared detector to characterize the resulting color centers by probing their optical (photoluminescence) signals.

What they uncovered surprised them: a quantum emitter called the Ci center. Owing to its simple structure, stability at room temperature, and promising spin properties, the Ci center is an interesting spin photon qubit candidate that emits photons in the telecom band. “We knew from the literature that Ci can be formed in silicon, but we didn’t expect to actually make this new spin photon qubit candidate with our approach,” Jhuria said.

An artistic depiction of a new method to create high-quality color-centers (qubits) in silicon at specific locations using ultrafast laser pulses (femtosecond, or one quadrillionth of a second). The inset at the top-right shows an experimentally observed optical signal (photoluminescence) from the qubits, with their structures displayed at the bottom. (Credit: Kaushalya Jhuria/Berkeley Lab)

The researchers learned that processing silicon with a low femtosecond laser intensity in the presence of hydrogen helped to create the Ci color centers. Further experiments showed that increasing the laser intensity can increase the mobility of hydrogen, which passivates undesirable color centers without damaging the silicon lattice, Schenkel explained.

A theoretical analysis performed by Liang Tan, staff scientist in Berkeley Lab’s Molecular Foundry, shows that the brightness of the Ci color center is boosted by several orders of magnitude in the presence of hydrogen, confirming their observations from laboratory experiments.

“The femtosecond laser pulses can kick out hydrogen atoms or bring them back, allowing the programmable formation of desired optical qubits in precise locations,” Jhuria said.

The team plans to use the technique to integrate optical qubits in quantum devices such as reflective cavities and waveguides, and to discover new spin photon qubit candidates with properties optimized for selected applications.

“Now that we can reliably make color centers, we want to get different qubits to talk to each other – which is an embodiment of quantum entanglement – and see which ones perform the best. This is just the beginning,” said Jhuria.

“The ability to form qubits at programmable locations in a material like silicon that is available at scale is an exciting step towards practical quantum networking and computing,” said Cameron Geddes, Director of the ATAP Division.

Theoretical analysis for the study was performed at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab with support from the NERSC QIS@Perlmutter program.

The Molecular Foundry and NERSC are DOE Office of Science user facilities at Berkeley Lab.

This work was supported by the DOE Office of Fusion Energy Sciences.

Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to delivering solutions for humankind through research in clean energy, a healthy planet, and discovery science. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 16 Nobel Prizes. Researchers from around the world rely on the Lab’s world-class scientific facilities for their own pioneering research. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science .

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Gupta, R.S., Sundaresan, N., Alexander, T. et al. | Nature 625, 259-263 (2024)

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Kim, Y., Eddins, A., Anand, S. et al. | Nature 618, 500–505 (2023)

Quantum-enhanced Markov chain Monte Carlo

Layden, D., Mazzola, G., Mishmash, R.V. et al. | Nature 619, 282–287 (2023)

Partner publications

Simulating large-size quantum spin chains on cloud-based superconducting quantum computers

Yu, H., Zhao, Y., & Wei, T. C. | Physical Review Research, 5(1), 013183 (2023)

Scalable circuits for preparing ground states on digital quantum computers: the Schwinger model vacuum on 100 qubits

Farrell, R. C., Illa, M., Ciavarella, A. N., & Savage, M. J. | arXiv:2308.04481 (2023)

Quantum algorithm for high energy physics simulations

Bauer, C. W., De Jong, W. A., Nachman, B., & Provasoli, D. | arXiv:1904.03196 (2019)

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What’s next for quantum computing

Companies are moving away from setting qubit records in favor of practical hardware and long-term goals.

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This story is a part of MIT Technology Review’s  What’s Next series , where we look across industries, trends, and technologies to give you a first look at the future

In 2023, progress in quantum computing will be defined less by big hardware announcements than by researchers consolidating years of hard work, getting chips to talk to one another, and shifting away from trying to make do with noise as the field gets ever more international in scope.

For years, quantum computing’s news cycle was dominated by headlines about record-setting systems. Researchers at Google and IBM have had spats over who achieved what—and whether it was worth the effort. But the time for arguing over who’s got the biggest processor seems to have passed: firms are heads-down and preparing for life in the real world. Suddenly, everyone is behaving like grown-ups.

As if to emphasize how much researchers want to get off the hype train, IBM is expected to announce a processor in 2023 that bucks the trend of putting ever more quantum bits, or “qubits,” into play. Qubits, the processing units of quantum computers, can be built from a variety of technologies, including superconducting circuitry, trapped ions, and photons, the quantum particles of light. 

IBM has long pursued superconducting qubits, and over the years the company has been making steady progress in increasing the number it can pack on a chip. In 2021, for example, IBM unveiled one with a record-breaking 127 of them. In November, it debuted  its 433-qubit Osprey processor , and the company aims to release a 1,121-qubit processor called Condor in 2023. 

But this year IBM is also expected to debut its Heron processor, which will have just 133 qubits. It might look like a backwards step, but as the company is keen to point out, Heron’s qubits will be of the highest quality. And, crucially, each chip will be able to connect directly to other Heron processors, heralding a shift from single quantum computing chips toward “modular” quantum computers built from multiple processors connected together—a move that is expected to help quantum computers scale up significantly. 

Heron is a signal of larger shifts in the quantum computing industry. Thanks to some recent breakthroughs, aggressive roadmapping, and high levels of funding, we may see general-purpose quantum computers earlier than many would have anticipated just a few years ago, some experts suggest. “Overall, things are certainly progressing at a rapid pace,” says Michele Mosca, deputy director of the Institute for Quantum Computing at the University of Waterloo. 

Here are a few areas where experts expect to see progress.

Stringing quantum computers together

IBM’s Heron project is just a first step into the world of modular quantum computing. The chips will be connected with conventional electronics, so they will not be able to maintain the “quantumness” of information as it moves from processor to processor. But the hope is that such chips, ultimately linked together with quantum-friendly fiber-optic or microwave connections, will open the path toward distributed, large-scale quantum computers with as many as a million connected qubits. That may be how many are needed to run useful, error-corrected quantum algorithms. “We need technologies that scale both in size and in cost, so modularity is key,” says Jerry Chow, director at IBM Quantum Hardware System Development.

Other companies are beginning similar experiments. “Connecting stuff together is suddenly a big theme,” says Peter Shadbolt, chief scientific officer of PsiQuantum , which uses photons as its qubits. PsiQuantum is putting the finishing touches on a silicon-based modular chip. Shadbolt says the last piece it requires—an extremely fast, low-loss optical switch—will be fully demonstrated by the end of 2023. “That gives us a feature-complete chip,” he says. Then warehouse-scale construction can begin: “We’ll take all of the silicon chips that we’re making and assemble them together in what is going to be a building-scale, high-performance computer-like system.” 

The desire to shuttle qubits among processors means that a somewhat neglected quantum technology will come to the fore now, according to Jack Hidary , CEO of SandboxAQ, a quantum technology company that was spun out of Alphabet last year . Quantum communications, where coherent qubits are transferred over distances as large as hundreds of kilometers, will be an essential part of the quantum computing story in 2023, he says.

“The only pathway to scale quantum computing is to create modules of a few thousand qubits and start linking them to get coherent linkage,” Hidary told MIT Technology Review. “That could be in the same room, but it could also be across campus, or across cities. We know the power of distributed computing from the classical world, but for quantum, we have to have coherent links: either a fiber-optic network with quantum repeaters, or some fiber that goes to a ground station and a satellite network.”

Many of these communication components have been demonstrated in recent years. In 2017, for example, China’s Micius satellite showed that coherent quantum communications could be accomplished between nodes separated by 1,200 kilometers. And in March 2022, an international group of academic and industrial researchers demonstrated a quantum repeater that effectively relayed quantum information over 600 kilometers of fiber optics. 

Taking on the noise

At the same time that the industry is linking up qubits, it is also moving away from an idea that came into vogue in the last five years—that chips with just a few hundred qubits might be able to do useful computing, even though noise easily disrupts their operations. 

This notion, called “noisy intermediate-scale quantum” (NISQ), would have been a way to see some short-term benefits from quantum computing, potentially years before reaching the ideal of large-scale quantum computers with many hundreds of thousands of qubits devoted to correcting errors. But optimism about NISQ seems to be fading. “The hope was that these computers could be used well before you did any error correction, but the emphasis is shifting away from that,” says Joe Fitzsimons, CEO of Singapore-based Horizon Quantum Computing.

Some companies are taking aim at the classic form of error correction, using some qubits to correct errors in others. Last year, both Google Quantum AI and Quantinuum , a new company formed by Honeywell and Cambridge Quantum Computing, issued papers demonstrating that qubits can be assembled into error-correcting ensembles that outperform the underlying physical qubits.

Other teams are trying to see if they can find a way to make quantum computers “fault tolerant” without as much overhead. IBM, for example, has been exploring characterizing the error-inducing noise in its machines and then programming in a way to subtract it (similar to what noise-canceling headphones do). It’s far from a perfect system—the algorithm works from a prediction of the noise that is likely to occur, not what actually shows up. But it does a decent job, Chow says: “We can build an error-correcting code, with a much lower resource cost, that makes error correction approachable in the near term.”

Maryland-based IonQ , which is building trapped-ion quantum computers, is doing something similar. “The majority of our errors are imposed by us as we poke at the ions and run programs,” says Chris Monroe, chief scientist at IonQ. “That noise is knowable, and different types of mitigation have allowed us to really push our numbers."

Getting serious about software

For all the hardware progress, many researchers feel that more attention needs to be given to programming. “Our toolbox is definitely limited, compared to what we need to have 10 years down the road,” says Michal Stechly of Zapata Computing , a quantum software company based in Boston. 

The way code runs on a cloud-accessible quantum computer is generally “circuit-based,” which means the data is put through a specific, predefined series of quantum operations before a final quantum measurement is made, giving the output. That’s problematic for algorithm designers, Fitzsimons says. Conventional programming routines tend to involve looping some steps until a desired output is reached, and then moving into another subroutine. In circuit-based quantum computing, getting an output generally ends the computation: there is no option for going round again.

Horizon Quantum Computing is one of the companies that have been building programming tools to allow these flexible computation routines. “That gets you to a different regime in terms of the kinds of things you’re able to run, and we’ll start rolling out early access in the coming year,” Fitzsimons says.

Helsinki-based Algorithmiq is also innovating in the programming space. “We need nonstandard frameworks to program current quantum devices,” says CEO Sabrina Maniscalco. Algorithmiq’s newly launched drug discovery platform, Aurora, combines the results of a quantum computation with classical algorithms. Such “hybrid” quantum computing is a growing area, and it’s widely acknowledged as the way the field is likely to function in the long term. The company says it expects to achieve a useful quantum advantage—a demonstration that a quantum system can outperform a classical computer on real-world, relevant calculations—in 2023. 

Competition around the world

Change is likely coming on the policy front as well. Government representatives including Alan Estevez, US undersecretary of commerce for industry and security, have hinted that trade restrictions surrounding quantum technologies are coming. 

Tony Uttley, COO of Quantinuum, says that he is in active dialogue with the US government about making sure this doesn’t adversely affect what is still a young industry. “About 80% of our system is components or subsystems that we buy from outside the US,” he says. “Putting a control on them doesn’t help, and we don’t want to put ourselves at a disadvantage when competing with other companies in other countries around the world.”

And there are plenty of competitors. Last year, the Chinese search company Baidu opened access to a 10-superconducting-qubit processor that it hopes will help researchers make forays into applying quantum computing to fields such as materials design and pharmaceutical development. The company says it has recently completed the design of a 36-qubit superconducting quantum chip. “Baidu will continue to make breakthroughs in integrating quantum software and hardware and facilitate the industrialization of quantum computing,” a spokesman for the company told MIT Technology Review. The tech giant Alibaba also has researchers working on quantum computing with superconducting qubits.

In Japan, Fujitsu is working with the Riken research institute to offer companies access to the country’s first home-grown quantum computer in the fiscal year starting April 2023. It will have 64 superconducting qubits. “The initial focus will be on applications for materials development, drug discovery, and finance,” says Shintaro Sato, head of the quantum laboratory at Fujitsu Research.

Not everyone is following the well-trodden superconducting path, however. In 2020, the Indian government pledged to spend 80 billion rupees ($1.12 billion when the announcement was made) on quantum technologies. A good chunk will go to photonics technologies—for satellite-based quantum communications, and for innovative “qudit” photonics computing.

Qudits expand the data encoding scope of qubits—they offer three, four, or more dimensions, as opposed to just the traditional binary 0 and 1, without necessarily increasing the scope for errors to arise. “This is the kind of work that will allow us to create a niche, rather than competing with what has already been going on for several decades elsewhere,” says Urbasi Sinha, who heads the quantum information and computing laboratory at the Raman Research Institute in Bangalore, India.

Though things are getting serious and internationally competitive, quantum technology remains largely collaborative—for now. “The nice thing about this field is that competition is fierce, but we all recognize that it’s necessary,” Monroe says. “We don’t have a zero-sum-game mentality: there are different technologies out there, at different levels of maturity, and we all play together right now. At some point there’s going to be some kind of consolidation, but not yet.”

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Quantum Computing Advance Begins New Era, IBM Says

A quantum computer came up with better answers to a physics problem than a conventional supercomputer.

A model of the interior of a quantum computer at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y. Credit... James Estrin/The New York Times

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By Kenneth Chang

• Published June 14, 2023 Updated June 19, 2023

Quantum computers today are small in computational scope — the chip inside your smartphone contains billions of transistors while the most powerful quantum computer contains a few hundred of the quantum equivalent of a transistor. They are also unreliable. If you run the same calculation over and over, they will most likely churn out different answers each time.

But with their intrinsic ability to consider many possibilities at once, quantum computers do not have to be very large to tackle certain prickly problems of computation, and on Wednesday, IBM researchers announced that they had devised a method to manage the unreliability in a way that would lead to reliable, useful answers.

“What IBM showed here is really an amazingly important step in that direction of making progress towards serious quantum algorithmic design,” said Dorit Aharonov, a professor of computer science at the Hebrew University of Jerusalem who was not involved with the research.

While researchers at Google in 2019 claimed that they had achieved “quantum supremacy” — a task performed much more quickly on a quantum computer than a conventional one — IBM’s researchers say they have achieved something new and more useful, albeit more modestly named.

“We’re entering this phase of quantum computing that I call utility,” said Jay Gambetta, a vice president of IBM Quantum. “The era of utility.”

A team of IBM scientists who work for Dr. Gambetta described their results in a paper published on Wednesday in the journal Nature .

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Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.

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Introduction

Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. A recently published report by McKinsey estimates the global market value of quantum computing to be at USD 1 trillion by 2035, mainly in the financial, chemical, pharmaceutical, and automotive sectors (Hazan et al., 2020 ). Today, the world’s largest technology companies, such as Google, IBM, Microsoft, Amazon, and Alibaba, are already investing billions in research and development of their quantum computing and provide partial access to these quantum computers to the public via cloud infrastructures. However, not only industry players invest but also governments, for example, China is investing USD 10 billion in a national quantum computing laboratory, the U.S. government provided USD 1 billion, and the EU has a budget of overall more than EUR 1 billion (Castelvecchi, 2018; Deicker & Yasiejko, 2018).

Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them (Ding & Chong, 2020 ). Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Additionally, interference plays an important role specifically when reading information from the quantum computer (Aaronson, 2008 ). Quantum computers can calculate and test extensive combinations of hypotheses simultaneously instead of sequentially (S.-S. Li et al., 2001 ). Furthermore, some quantum algorithms can be designed in a way that they can solve problems in much fewer steps than their classical counterparts (their complexity is lower). For this reason, quantum computing could represent a significant breakthrough in modern IT in the next few years and might initiate the transition to the "5th industrial revolution" (Hadda & Schinasi-Halet, 2019 ).

First experiments show promising results, such as the one done by Google in 2019 in which the company claims to have achieved so-called quantum supremacy (IBM “quantum advantage”) (Arute et al., 2019 ). In an artificial experiment, they were able to demonstrate that a programmable quantum device could solve a problem that a classical computer could not solve in a feasible amount of time. However, the task solved by Google ‘s quantum computer was custom tailored to the specific quantum hardware used and has no real-world applications. Nevertheless, it was an important proof of concept. Furthermore, in 2020, Chinese scientists claimed to have built a quantum computer that is able to perform specific computations approximately 100 trillion times faster than the world‘s most advanced supercomputer (Zhong et al., 2020 ).

Given its current state of development, experts anticipate that quantum computing could provide unprecedented advantages, especially in the areas of optimization, artificial intelligence, and simulation (Langione et al., 2019 ; Ménard et al., 2020 ). It is likely that simulations of molecules (for chemical and pharmaceutical industries) will be among the first real-world applications of quantum computers. This is because molecules directly follow the laws of quantum mechanics, so using quantum computers is the most natural way of simulating them. Other industries that could soon benefit include the financial sector, transportation and logistics, the global energy and materials sector but also areas such as meteorology or cybersecurity (Gerbert & Ruess, 2018 ; Langione et al., 2019 ; Ménard et al., 2020 ). However, to date, quantum computing has extensive unsolved challenges in physics and computer science, ranging from hardware architectures and data management to application software and algorithms, which requires fundamental research in all these areas and beyond (Almudever et al., 2017 ).

To inform information systems (IS) research, this Fundamental provides the fundamental concepts of quantum computing and depicts research opportunities. Therefore, we provide in our second section a brief overview of a quantum computer system and its three layers of a quantum computer: hardware, system software, and application layer. The third section introduces potential application areas of quantum computing. Footnote 1 Building upon these and the introduced conceptual layer view on quantum computing, we relate to each layer by detailing potential research opportunities in the context of electronic markets. A whole new ecosystem around quantum computing technology itself is emerging already, provoking questions around the change of (1) business models and process innovation, (2) challenges for IT organizations, or (3) sourcing from start-ups, full-stack providers such as Google, IBM, Microsoft, or Alibaba or individual development.

Quantum computing system

In 1980, Paul Benioff envisioned the concept of a quantum touring machine, i.e., the theoretical concept of a quantum computer (Benioff, 1980 ). In 1982, Richard Feynman proposed the first practical application of a quantum computer: efficient simulations of quantum systems (Feynman, 1982 ). In general, a quantum computer can be defined as a universal computing device that stores information in objects called quantum bits (or qubits) and transforms them by exploiting very specific properties from quantum mechanics (Ding & Chong, 2020 ). The quantum computer performs quantum computing, which is a type of computation that collects the different states of qubits, such as superposition, interference, and entanglement, to perform calculations (Grumbling & Horowitz, 2019 ). Importantly, quantum computers are not intended to become general purpose computers that operate by themselves. They will be highly specialized devices that can solve specific tasks much faster than classical computing. Operating quantum computers will most certainly require a classical computer for loading input/output data, retrieving results from computations as well as controlling the quantum computer’s electronic and internal processes. Thus, quantum computers and classical computers form a quantum computing system that enables quantum computers to perform quantum computing. To depict the different layers of a quantum computing system, we adopt the model of Ding and Chong ( 2020 ) for three reasons. First, it allows us to analytically distinguish the key components of a quantum component system to illustrate the fundamental mechanisms and elements. Second, it builds on an analytics distinction of hardware, system software, and application, which is mirrored in conceptual views on computing architectures, e.g., cloud computing (Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) or the layered modular architecture of digital technologies (Yoo et al., 2010). Third, our expert informants distinguished between similar layers as well in their interview statements to explain the state of the art, the challenges for today’s organizations, and the functioning of quantum computing systems. Figure  1 shows a quantum computing system consisting of a van Neumann architecture for classical computing and a quantum computer with its three layers architecture, which we will explain accordingly.

Showing a classical computer (von Neumann architecture) and a quantum computer forming a quantum computing system (adapted from Ding and Chong ( 2020 ))

Hardware layer

One fundamental difference between classical and quantum computers is how information is stored. Whereas classical computers use bits, which can have the value of either zero or one, to store information, quantum computers use quantum bits (or qubits), which can hold any linear combination of zero and one simultaneously (Steane, 1998 ). Qubits leverage the advantage of the properties of quantum mechanics and in particular the effect of superposition (visualization see Fig.  2 ).

Classical bit and qubit (Superposition)

Superposition

Loosely speaking, a qubit is described by its probability of being either zero or one and not by the distinct value of zero or one. Thus, a qubit can have the probability of being 60% zero and 40% one. Importantly, only at the point of measuring the state of the qubit, it “collapses” to the single classical defined value of either zero or one (Bosch, 2020 ; Ding & Chong, 2020 ). The property of superposition has the advantage that a quantum computer with just four qubits is able to represent 16 four-digit numbers simultaneously. With each further qubit, the number of representable states doubles whereas a classical computer, with a sequence of four bits, can only represent a single four-digit number.

The real advantage of quantum computing relies on the fact that one can perform an exponential amount of calculations at the same time. Even though at the end of every program it is possible to read only the solution to one calculation, it is possible to develop a quantum algorithm that makes it very likely that the final result is precisely the one that one is looking for. For example, we might be trying to find out if there exists any possible rarely occurring turbulence that could cause a plane to crash. Instead of simulating billions of combinations of air conditions on a classical computer and checking their individual results, we could simply test almost all possible air conditions at once on a quantum computer and read out only the result that causes the plane to crash.

Entanglement

Not only qubits are unique to quantum computing. Entanglement is also a property of quantum mechanics. Entanglement is when the state of one qubit is dependent on the state of another qubit (Steane, 1998 ). Thus, when two qubits are entangled, making any kind of flip or rotation on one of the qubits would result in the same change happening to the other qubit (Einstein et al., 1935 ; Schrödinger, 1935 ). Furthermore, when the state of either one of the two qubits is measured, the state of both qubits collapses to either one or zero (depending on their probabilities). This is even the case when the qubits are far away from each other. Thus, the advantage of entanglement is that when a qubit influences the other qubits around it, all are working in tandem to arrive at a solution. Therefore, qubits can be correlated in a way that is not possible for bits in traditional computers. This opens up new possibilities and gives the quantum computer the ability to process information in a fundamentally different way than a classical computer (Mooney et al., 2019 ). One example is superdense coding, which is the process of transporting two classical bits of information using one entangled qubit (A. Harrow et al., 2004 ). This process is especially interesting for secure quantum key distribution (Bennett & Brassard, 2014 ). This is a secure communication method that implements a cryptographic protocol relying on quantum entanglement and other quantum phenomena. It enables two parties to produce a shared random secret key (entangled qubit) known only to them, which can then be used to encrypt and decrypt messages (Scarani et al., 2009 ).

Based on the fundamentals of quantum mechanics, we now discuss the approaches to physically represent and manipulate qubits. Broadly speaking, the approaches can be split into two main categories: a) analog quantum computing and b) digital gate-based quantum computing (Ding & Chong, 2020 ).

Analog quantum computing

In analog quantum computing, the quantum state is smoothly changed by quantum operations such that the information encoded in the final system corresponds to the desired answer with high probability. One example of analog quantum computing is adiabatic quantum computers (Albash & Lidar, 2018 ), which refer to the idea of building a type of universal quantum computing. A special form of adiabatic quantum computers is quantum annealing, which is a framework that incorporates algorithms and hardware designed to solve computational problems via quantum evolution towards the ground states (Vinci & Lidar, 2017). Quantum annealing takes advantage of the fact that physical systems strive towards the state with the lowest energy, e.g., hot things cool down over time or objects roll downhill. As such, in quantum annealing the energetically most favorable state then corresponds to the solution of the optimization problem (Albash & Lidar, 2018 ). Using the property of superposition, the quantum annealer is able to calculate all potential solutions at the same time, which speeds up the calculation process drastically in comparison to classical computers (Shin et al., 2014). Quantum annealing is most suitable for optimization problems or probabilistic sampling and is used by companies such as D-Wave. However, to date, it is unclear whether the quantum annealing technique will ever achieve significant quantum speedup (Albash & Lidar, 2018 ).

Digital gate-based quantum computing

In digital gate-based quantum computing, the information encoded in qubits is manipulated through digital gates. In comparison to the analog approach in which you sample the natural evolution of quantum states to find the optimal state of low energy, in digital gate-based quantum computers the evolution of the quantum states is manipulated in terms of activity and controlled to find the optimal solution (Ding & Chong, 2020 ). Thus, the state of qubits is actively manipulated and therefore provides the advantage of being much more flexible, and it can be used to solve large classes of problems, in contrast to quantum annealing. Digital gate-based quantum computing is conceptually very similar to classical computation (Grumbling & Horowitz, 2019 ). A classical algorithm is run on a computer as a series of instructions (gates such as AND, OR, NOT, …). They manipulate individual or pairs of classical bits and flip them between zero and one states according to a set of rules. Quantum gates operate directly on one or multiple qubits by rotating and shifting them between different superpositions of the zero and one states as well as different entangled states. Companies using digital gate-based quantum computing are, for example, IBM, Google and Rigetti.

System software layer

The system software layer builds on top of the hardware layer and orchestrates the system’s processes to leverage the potentials of the qubits (superposition and entanglement). This layer has to cope with challenges of the thermodynamically unstable quantum states. It actively reduces thermal noise within and around the quantum system and performs error correction procedures.

In quantum computing there are many potential sources that can cause noise. For example, quantum computers and especially digital gate-based ones are highly sensitive to changes in the environment, such as vibration, temperature fluctuations, etc. Noise can also be caused by imprecise control of the quantum hardware or manufacturing defects (Ding & Chong, 2020 ). Most quantum computers even require their chips to be cooled down to a hundredth of a degree above absolute zero temperature to operate. Thus, since noise cannot be avoided, the first era of quantum computers is also called noisy Intermediate-Scale Quantum Computer (NISQ, Preskill, 2018). This abbreviation implies that current quantum hardware using dozens of qubits has error rates that are too high, which need to be improved before we can build useful quantum computers with hundreds, or even thousands, of usable qubits.

Noise in the environment can lead to qubit decoherence which is environmental influences causing quantum states to randomly change (Grumbling & Horowitz, 2019 ). This is problematic, as a single error in a calculation usually causes the result to be incorrect, unless the error is corrected during the calculation. Since it is impossible to prevent every kind of noise, error correction is important. Ongoing research on quantum error correction seeks to achieve system-level fault tolerance. Quantum error correction differentiates between physical and logical qubits. Logical qubits are represented by a group of physical qubits, which are needed for error correction. Physical qubits work together on correcting errors on individual physical qubits. A group of physical qubits is less likely to cause an error in a calculation than just one physical qubit. Unfortunately, error-correcting mechanisms can cause errors themselves. Depending on the error-correcting mechanism, the relation is typically five to nine physical qubits to achieve one almost error-free logical qubit (Marinescu & Marinescu, 2012 ; Shor, 1995 ).

One way to do this is by representing every qubit with groups of several physical qubits that, loosely speaking, work together on correcting errors on individual physical qubits. A perfect physical qubit can work as a logical qubit, as it requires no error correction. Today, the biggest challenge is scaling up to thousands of qubits. Even though the computational space that can be used for calculations doubles (Ding & Chong, 2020 ) with the addition of every individual qubit, this advantage presently cannot be exploited in its full capacity due to high error rates. One prominent example for trying to increase the number of qubits is IBM, which states that it wants to achieve over 1,000 qubits by 2023, while currently there are machines with 60–100 available (Gambetta, 2020 ).

Application layer

One of the main challenges of today’s quantum computers is the unsolved problem of efficient quantum memory (Ciliberto et al., 2018 ). There exist several theoretical proposals for building quantum random access memory (QRAM). Even though it may be experimentally difficult to build (just as the quantum computer itself), recent publications demonstrated several possible paths of doing so (Hann et al., 2019 ; Park et al., 2019 ). Thus, currently exists no efficient way to store states of qubits in a memory for a long time for other calculations. Therefore, data needs to be loaded from a classical computer to the targeted quantum computer, and after performing the calculation states need to be read (measured) by the classical computer before the qubits lose their information. Due to the no cloning theorem, we are also not able to make copies of quantum states and use them for calculations. The only way to load a quantum state from quantum memory into a quantum program is by applying a SWAP operation, and thereby removing it from the memory.

When a quantum state is measured, it collapses to either one or zero. Therefore, we have no way of finding out what state a certain qubit is in. The only way we can approximately find out what state a qubit is in is if we have multiple copies of the same qubit and measure them all. In some cases, reading the classical data may dominate the cost of quantum algorithms so that it cannot speed up the whole algorithm at the macro level. Reading out the data exactly may be infeasible, which cannot meet the computing needs in some tasks. This is especially the case for methods that need large data sets, such as machine learning and artificial intelligence.

Finding a useful algorithm for quantum computers is mostly about constructing it in such a way that the probability of measuring the desired outcome is maximized. Even though the output of the quantum computer may be an exponentially large number of solutions, we are usually just interested in a small subset of these solutions. Finding them without having to run the whole algorithm many times is the art of quantum algorithm construction. Here are three of the most important quantum algorithms.

Grover’s algorithm is also known as the quantum search algorithm. Grover’s algorithm is used for searching an unstructured database or an unordered list. Classically, for finding a particular item in a database of size N, we need to go through, on average, N/2 items to find the right one. Using Grover’s algorithm, we can do this in only √N steps, on average. For a large N, this can be remarkably faster. This is called a quadratic speedup (Grover, 1996 ).

Shor’s algorithm , also known as the integer factorization algorithm, can factorize integers almost exponentially faster than the fastest known classical algorithm. Factorizing integers is very difficult computationally and is therefore also the basis of RSA encryption (Shor, 1994 ).

HHL (Harrow Hassidim Lloyd) is also known as the quantum algorithm for linear systems of equations. The algorithm can estimate the result of a function of the solution x of a linear system (Ax = b), where A is a matrix and b a vector (A. W. Harrow et al., 2009 ).

Application areas of quantum computing

Thanks to the enormous progress in hardware, more and more established commercial companies are investing in quantum technology. Examples include Boehringer Ingelheim, who recently announced a research partnership with Google (Boehringer-ingelheim, 2021 ), and Daimler, who announced progress in the field of materials research (Motta et al., 2020 ), or chemistry giants like BASF who aim to stay at the forefront of chemistry research and business (Hartmann & Deppe, 2021 ). Quantum computing has three essential capabilities to address today’s computational problems that current computers are not or only partially capable of and that bear benefits for companies: 1) search and graph, 2) algebraic and 3) simulation (Hoffmann, 2021 ; Li et al., 2020 ). These capabilities determine the potential applications of this technology in numerous industries, such as finance, chemistry and pharma, manufacturing, energy, or cybersecurity (Gerbert & Ruess, 2018 ; Langione et al., 2019 ; Ménard et al., 2020 ). Table 1 provides a summary of the problem types, approaches and potential use cases.

Search and graph

The fact that a qubit can theoretically represent an infinite number of states allows for solving complex combinatorial optimization problems, which is currently one of the major application areas for current quantum computing technologies, such as the solution of D-Wave (Johnson et al., 2011 ). Combinatorial optimization is the process of finding one or more optimal solutions to a problem. Examples of such problems include supply chain optimization, optimizing public transportation schedules and routes, package deliveries, etc. These solutions are searched for in a discrete (finite) but very large configuration space (i.e., a set of states). The set of possible solutions can be defined with several constraints and the goal is to optimize the objective function with the best solution.

Since the problem spaces in certain complex problems are very large, it is extremely difficult to find the optimal or even a single good solution to these problems with classical computers in a reasonable time frame or with sufficient accuracy. Such combinatorial optimization problems often pose a great challenge for the private as well as the public sector. While they are often simple to describe, they turn out to be very difficult to solve. Combinatorial optimization problems may be divided into order, assignment, grouping, and selection problems, and within these classes, subclasses exist, such as the knapsack or the traveling salesman problem. In addition to the property that there can be a lot of qualitatively different solutions for a problem, no known algorithm exists that can easily compute these problems directly. Searching very large problem spaces requires an enormous amount of computing capacity and time.

Respectively, quantum computers are expected to play a decisive role in the financial services industry. Especially players specializing in portfolio optimization and arbitrage could benefit (Egger et al., 2020 ). From a very large pool of existing financial instruments, a subset should be selected so that a certain portfolio volume is achieved, while at the same time a large number of factors must be taken into account to minimize risk and achieve profitability (Chakrabarti et al., 2021 ). Further, Deutsche Börse (a German company offering marketplace organizing for the trading of shares) already experimented with the applicability of quantum computing for a sensitivity analysis on one of their risk models, a computation that is too expensive to be run on classical computers (Braun et al., 2021 ). Due to its suitability to solve optimization problems, another application of quantum computing is the optimization of flow, e.g., of traffic or goods. Collaborating with D-Wave Systems, VW has already shown in a pilot project how to optimize a simplified traffic flow in the city of Lisbon by leveraging quantum annealing technologies (Neukart et al., 2017 ; Yarkoni et al., 2019 ) – a project that started in late 2016 with a proof-of-concept project. It investigated the readiness of quantum computing by building a traffic-flow optimization program that used GPS coordinates of 418 taxis in Beijing to resolve traffic congestion.

Moreover, quantum computers are superior to classical computers regarding certain prime factorization procedures that play an important role in the secure encryption of data. A popular example for this is the aforementioned Shor ( 1994 ) algorithm that factors a number into its prime factors, a process used often in cryptography and cybersecurity. A dataset encrypted with quantum technology would be impossible to decrypt with classical computer technology, or at least not in time periods relevant to human users. Conversely, it would be easy for a quantum computer to crack data encrypted with classical RSA technology – a phenomenon that may be coined as quantum threat (Mone, 2020 ).

The ability of quantum computing to accelerate optimization problems plays a crucial role for narrow AI approaches (Gao et al., 2018 ; Langione et al., 2019 ). Quantum computing can help to calculate complex network architectures and weights for machine learning and artificial intelligence. Quantum computing shows its advantage in transforming and calculating large metrices. For example, in the context of supervised learning, the model aims to minimize the error between the prediction of the model itself compared to the input and adequate output or label given. Quantum computers offer several approaches to solving problems like this, thereby, again, accelerating calculation and allowing for more complex network architectures (DeBenedictis, 2018 ). They may be applicable to all relevant practices or sub-tasks of artificial intelligence, such as image processing and computer vision (Dendukuri & Luu, 2018 ) or natural language processing, as demonstrated in an experiment by Cambridge Quantum Computing (Lorenz et al., 2021 ). Having said that, it is important to note that, so far, no near-term machine learning algorithm with provable speedup has been found.

A quantum computer has a fundamental advantage over classical computers: It can simulate other quantum systems (e.g., a nitrogen molecule) much more efficiently than any computer system available today. For classical computers, even molecules with comparatively low complexity represent an almost unsolvable task. In the 1980s, Richard Feynman theoretically substantiated the possibility of a quantum-based computer for simulating molecules (Feynman, 1982 ). Since then, researchers have attempted to transfer the quantum system of a molecule into another quantum system, i.e., into the quantum computer, in order to simulate it. One new hope in the application of quantum computers is the simulation of more efficient catalysts for ammonia synthesis in the Haber–Bosch process, which today accounts for about 1 to 2 percent of global energy consumption. Better catalysts could reduce energy consumption and thus also help slow global warming. Even quantum computers without full error correction may already be better suited for this application than simulations on classical computers (Budde & Volz, 2019 ).

Furthermore, the development of active ingredients and drugs is often a lengthy and very cost-intensive process. This is due in particular to the fact that a large number of substances have to be tested on a trial-and-error basis in the real world. Yet, building on the same principles of quantum physics, quantum computing may be able to virtually replicate the behaviors such that simulation-based research may sooner or later replace this cost-intensive process.

For instance, BASF, pursuing its high requirements for the accuracy of quantum chemical calculations, investigated, in collaboration with the company HQS, the applicability of quantum computing. Specifically, they aimed to understand the quantum mechanical calculation of the energy course of chemical reactions, as this actually allows for the prediction of the probable course (i.e., how does the reaction proceed, which products, by-products, etc., are formed, how can I accelerate the reaction with the help of catalysts, etc.) of chemical reactions. This application of needed methods reaches the limits of conventional computing methods (Kühn et al., 2019 ). In addition, material research on the functioning of batteries is deemed to inform today’s electromobility and is already targeted by automotive giants such as VW (Neukart, 2021 ; Ziegler & Leonhardt, 2019 ).

Link to the field of information systems

Even though there are high investments in quantum computing, most expert estimations still place the widespread industrial application of quantum computing at least five to ten years in the future. Its exact manifestations in many critical areas remain unclear. Thus, it is the task of today’s research community to creatively conjure up and explore the full potential and the socio-technological consequences of quantum computing. Therefore, based on analyzing existing literature and the conducted interviews with 21 leading experts in industry and research, we propose the following four initial directions for research on quantum computing in information systems (for a summary, see Table 2 ): 1) quantum computing ecosystems as a new networked business, 2) digital understanding as a foundation for use cases and ecosystems, 3) quantum computing as a challenge for IT organizations and IT service providers, and 4) skills needed to leverage quantum computing in the quantum computing field.

All of these directions try to consider the fact that quantum computing despite its disruptive potential will initially be an extension of computing capabilities for established electronic markets, ecosystems, and its participants (see Fig.  3 ), while new ecosystem participants are already establishing themselves (e.g., IonQ or Rigetti).

Quantum computing system and relevant point of contacts for established ecosystems

We further try to focus on established research areas and the focus of our research community.

Quantum computing ecosystems as a new networked business

The adoption and diffusion of quantum computing will heavily rely on an emerging ecosystem comprising technology providers, such as IBM, Google, Microsoft, or Amazon Web Services, start-ups with specific playgrounds such as 1Qbit or IonQ as well as consulting firms and academic institutions to support customers in adopting and building applications using quantum computing technologies (Carrel-Billiard et al., 2021 ; IBM, 2019 ). Also, the European Union built their own ecosystem with the “Quantum Flagship”. Footnote 2 Companies, providers, research institutions, and governments ultimately need to engage in such an ecosystem to allow for getting hold of capabilities that transcend their own organizational boundaries or even their entire industry (e.g., building their own computing infrastructures, translating business problems into mathematical and quantum problems, etc.) (Carrel-Billiard et al., 2021 ). Due to this emerging new organizing logic and structure for quantum computing, key aspects need to be considered when pursuing information systems research in this context.

First, the entrance barrier to quantum computation is expected to be very high due to multiple limitations such as the necessity of knowledge in quantum physics, the expensiveness of building quantum computers and the shortage of experts in the labor market. As such, they may enforce divides and limit access. Steps should be taken to reduce a possible quantum divide. Second and consequently, incumbents will need to rely on the capabilities that technology providers, start-ups, consulting firms, or academic institutions may provide, as they might go beyond their domain expertise. As such, prevailing networked businesses and ecosystems need to develop methods and technologies to purposefully connect their way of doing digital business with the emerging quantum computing ecosystem players in the different layers, namely the hardware layer (e.g., Amazon Web Services, IBM, and Google), the system layer (e.g., IonQ and Rigetti), and the application layer (e.g., Cambridge Quantum Computing or 1QBit) (IBM, 2019 ).

Today, the playground is already diverse, with fuzzy boundaries leading to the need for design-science-oriented guidance for incumbents to assess their own business and technology maturity. For instance, IonQ and Rigetti are positioned on both the hardware and the system software layer. Additionally, for companies it is important to mediate the engagement with different players as part of their quantum computing road map. Thus, possible research questions might include the following: Does the access to quantum computing need to be regulated? Does quantum computing need new sourcing strategies? How does the emerging quantum computing ecosystem act as a spoke component to other industries and ecosystems? Which transformation may result from the emergence of a quantum computing networked business?

Digital understanding and representation as a foundation for quantum computing use cases and ecosystems

The proliferation of quantum computing as a generative technology for calculating with an enormous speedup relies on a fundamental premise: The problem which will be solved by a quantum computing approach needs to be replicated in the form of digital data on which basis a calculation becomes possible in the first place. Emerging technologies such as machine learning already challenge today’s organizations. The main reason is that it is complicated to digitally represent business practices and economic behavior to allow for analysis. This phenomenon may be summarized as datafication (Lycett, 2013 ). As such, the dematerialization of the physical world in the form of digital data as a digital representation is an essential prerequisite (Recker et al., 2021 ). Only with this prerequisite, one may use quantum computing when calculating the physical world based on its datafied digital representation.

Achieving an adequate digital representation of the respective quantum computing problem requires a mathematical and conceptual understanding to allow for assessing, understanding, and realizing the value of quantum computing aside from other computing approaches (e.g., high performance computing). Furthermore, quantum computing may also serve as an enabler for process innovation; for example, it could be interesting for research areas around process mining (Mendling et al., 2020 ), such as analyzing and optimizing process configurations or simulating contexts of processes or configurations of processes (vom Brocke et al., 2021 ). Therefore, research on use case analysis and in particular on methods of how to find, describe, and analyze use cases systematically and at scale are highly relevant. Possible research questions could include the following: What approaches could be used or developed to analyze business problems and therefore leverage the potential of quantum computing? How can these problems be described mathematically? What are possible design principles of artifacts to describe use cases? How will QC impact the modeling of a social and economic reality as a transformation from binary to multidimensional quantum states?

Quantum computing as a challenge for IT organizations and IT service providers

IT competencies are increasingly built up in business units using commercial IT services without having the IT department in the loop. Quantum computing drives this change even further, since for the next few decades, the first quantum computers will likely only be available via the cloud for most companies (Carrel-Billiard et al., 2021 ). IT departments are therefore under pressure in terms of how to manage quantum computer usage in companies, especially with regards to transmitting the respective data which is needed for quantum-computing-based calculations. This is of particular interest, since data preparation including data input and output might be the bottleneck for quantum computing in the long run. Furthermore, quantum computing and especially the ability of prime factorization is a threat for current encryption standards and poses huge challenges for the IT organization. Even though new encryption techniques can be used once quantum computers become a real threat to current encryption protocols, past communication and old data can be decrypted retroactively.

Future research questions could include the following: What are possible security approaches to protect legacy IT with old encryption standards? Can quantum computers and AI be used for real-time threat and anomaly detection? How can quantum computers be used to simulate possible intrusions and cyberattacks for calculating risk–cost evaluations? The latter is of special interest due to the hyper-connectivity of digital services, which poses an enormous vulnerability for an infrastructural attack.

Quantum computing skills

Historically, the role of information systems has been to bridge the gap between informatics and business. In the age of quantum computing, this role is becoming more important than ever before. In order to leverage the potential of quantum computing, at least three roles are required (Carrel-Billiard et al., 2021 ; Hughes et al., 2022 ): First, mathematical and quantum physical skills are needed to translate problems into mathematical formulas. Second, domain expertise is needed to integrate the business problem within the mathematical formulation. Third, an intermediary is needed to facilitate between both roles (Gartner, 2019 ). Due to the high complexity and high specialization of the job types (e.g., error correction specialist, quantum algorithm developer), the entrance barrier to the field of quantum computing is significantly higher than for regular "coding". Additionally, for years there has been a shortage of STEM (Science Technology Engineering Mathematics) graduates, which may amplify the war for talents in quantum computing (OECD, 2021 ). Having said that, companies such as IBM, Google, or research institutions such as ETH, are working on developing programming languages and compilers in which a device will decide if the application is suitable for a quantum computer. However, according to experts, this will take years. Future research questions could include the following: How could information systems act in a mediating role for adopting quantum computing technologies? Should quantum computing be included in the information systems curriculum? Since quantum computing knowledge is important on a strategic level, how can future information systems managers be trained to be aware of its potential? How can management leverage the potentials of available techniques, approaches and platforms around quantum computing? How can gaps of knowledge and access to infrastructures be mitigated?

In this Fundamentals article, we provide an overview of the constituting concepts of quantum computing. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. On this basis and our access to leading experts in quantum computing, we propose several focus areas for studying the socio-technical implications of quantum computing for the emergence of new ecosystems or their extensions as well as for ecosystem participants themselves.

The disruptive nature of quantum computing will lead to various changes in all socio-technical components of organizations and in IS-related ecosystems. As such, we expect a large impact on the IS discipline in academia, practice, and teaching. At the same time, we are aware that quantum computing is in its infancy, both as a field of research for IS research as well as in its development towards an established and well-understood computing approach. Against this backdrop, we hope to inform and inspire research on the socio-technical peculiarities of quantum computing on the ecosystem level or level of electronic markets (e.g., quantum computing ecosystems as a new networked business), the organizational level (e.g., the role of IT organizations and service provider for establishing quantum computing), the individual level (e.g., quantum computing skills) as well as on the crucial role of data (i.e., digital understanding and representation of economic behavior) to allow for quantum computing calculations.

Change history

09 october 2022.

Missing Open Access funding information has been added in the Funding Note.

The Fundamentals article is built on the extent body of knowledge on quantum computing. For our literature review we broadly searched for the term “quantum computing” in libraries, such as EBSCO, ScienceDirect, IEEE, or the AIS eLibrary in computer science and IS research. Both the application areas as well as the research opportunities are informed by the prevailing themes of 21 conducted interviews with technology and academic experts from well-established Fortune 500 companies and prestigious academic institutions.

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Acknowledgements

We would also like to thank all the interviewees for their help and especially Rajiv Krishnakumar for his support in revising this article.

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Ibm’s latest research paper signals a new era of quantum computing is here.

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Quantum advantage, a key milestone in quantum computing, refers to the yet-to-be-achieved stage where a quantum computer can surpass the performance of a traditional computer. Essentially, it's all about the comparison of computational power between quantum and classical computers. Because it is such an important concept, it isn’t uncommon to see an occasional scientific paper written by a company or university claiming to have attained quantum advantage in some narrow slice of quantum computing.

This article discusses a new IBM research paper that demonstrates how error mitigation was used to obtain superior quantum performance compared to a classical supercomputer running state-of-the-art approximation methods. This experiment resulted in some of the largest quantum circuits ever run on a quantum computer.

We may not quite be there yet, but quantum computing is clearly approaching quantum advantage.

Key areas of research for quantum advantage

IBM defines quantum advantage as a significant improvement in quantum algorithm runtime for practical cases over the best classical algorithm. It further explains that the algorithm needed to prove quantum advantage must have an efficient representation as quantum circuits, and that there should be no classical algorithm capable of simulating those circuits efficiently.

As yet there have been no useful applications demonstrating quantum advantage, and the reason is simple: today’s quantum computers are too noisy, too error prone and too small to solve large, real-world problems. Unfortunately, most claims about quantum advantage put forward in papers are usually based on either random circuit sampling or Gaussian boson sampling , neither of which are considered to be useful applications.

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According to IBM, three major problems must be solved for quantum machines to perform useful problems:

• A method is needed to deal with quantum noise.
• Qubits must be scalable to large numbers.
• Quantum processors must have sufficient speed (measured in circuit layer operations per second, or CLOPS).

There is a direct relationship between quantum computing noise and the size of a problem you can tackle. The chain is simple—noise causes errors, and uncorrected errors limit the number of qubits you can incorporate in your circuit, which in turn limits algorithm complexity. Clearly error control is important.

IBM has a long history of error correction research, going back as far as investigations in 1996 by David DiVincenzo, Charlie Bennett and John Smolin. In 2015, IBM developed the first system to detect quantum bit flip and phase flip errors. Today, almost every corporate and academic quantum computing program has some form of error correction research in progress.

That said, quantum error correction (QEC) is a complex engineering and physics problem, and a truly satisfactory answer for it appears to be many years away. This is important because scaling a quantum computer to tens of thousands of qubits simply isn’t possible without error correction.

On the other hand, IBM believes quantum advantage without fault tolerance is possible. It recently published a paper illustrating that quantum qubits and gates can be used deeper and wider than we previously thought.

New theories, new solutions

Quantum Circuit Complexity

The above IBM chart compares scaling error-mitigated quantum circuits to the scaling of classical computers. For the first time, IBM has shown that quantum error mitigation provides a more efficient path to run circuits with a higher complexity (and at a minimal increase in simulation cost) than is possible with classical computers.

Going for utility advantage

Evidence for the utility of quantum computing before fault tolerance.

Instead of relying on fault-tolerant error correction, which is still several years away, dramatic speed-ups now appear to be possible through the use of error mitigation and error suppression techniques. IBM published proof of this concept in a new research paper published in Nature titled “Evidence for the utility of quantum computing before fault tolerance.” This paper was produced in collaboration between IBM and researchers at the University of California, Berkeley, RIKEN iTHEMS and the Lawrence Berkeley National Laboratory.

IBM used its 127-qubit Eagle R3 processor to simulate 127 interacting spin states. For the simulation, each qubit played the role of a spin, using two-qubit gates with a depth of 60. UCBerkeley ran the equivalent problem using cutting-edge tensor network techniques on powerful classical supercomputers at the National Energy Research Scientific Computing Center and at Purdue University.

The research team deliberately picked a problem known to be challenging for classical hardware. The research was structured to provide a comparison between the quantum solution and the classical solution. It did this by alternating solution attempts back and forth between the two teams while shifting from Clifford to non-Clifford gates and varying the weight of observables.

There were computational regions of the problem that caused the classical supercomputer’s brute-force methods to fail. However, when the supercomputer failed, the quantum processor didn’t falter, but rather continued to provide solutions. Given that the workflow called for a comparison on every run, on the occasions when an actual classical solution wasn’t available, comparisons to the quantum results were still provided by advanced classical approximation methods. Those approximated solutions revealed that the quantum processor delivered more accurate results than the classical methods.

Based on the performance against the brute-force methods, the quantum simulation was still able to run and produce reasonable results, versus the classical approximation methods. Overall, quantum processing provided greater accuracy and in shorter timeframes.

Although a comparison of power consumption wasn’t specifically part of the project, estimates put power consumption for quantum computing at 1.4 MWh per day and classical supercomputing at 10–15 MWh per day. So it appears that quantum provides a significant advantage in this respect as well.

Future research targets

Circuit Depth

Traditional thinking supports the notion that useful quantum computation can’t be done without fault tolerance. Although IBM’s paper isn’t conclusive proof, it provides an important data point that demonstrates current quantum computers can provide value much sooner than expected by using error mitigation.

As shown in the above graphic, IBM believes the door to successful quantum advantage is through the 100 x 100 section of the chart, which represents at least 100 qubits with a depth of 100 gates. IBM believes that if enough third-party research is conducted in that part of the chart, solid evidence of quantum advantage may become evident.

IBM credits much of the experiment’s success to the quality of its quantum hardware and new error mitigation methods such as PEC and ZNE.

IBM's roadmap calls for error mitigation to provide a continuous development path to quantum error-correction. Once QEC is attained, it will enable us to build fault-tolerant quantum machines running millions of qubits in a quantum-centric supercomputing environment. These machines will have the ability to simulate large many-body systems, optimize complex supply chain logistics, create new drugs and materials, model and react to sophisticated financial market behavior and much more.

Fault-tolerant quantum computers will signal that a new era of quantum-centric scientific investigation has arrived. And with that new capability will come the potential to responsibly change the world.

Jay Gambetta, IBM Fellow and vice president of quantum computing, recently discussed the results of the paper with Patrick Moorhead of Moor Insights & Strategy.

Analyst Notes

• Error mitigation is the leading method that IBM believes will bridge the gap between today’s error-prone hardware and tomorrow’s fault-tolerant quantum computers. Error mitigation’s interim purpose is to enable early achievement of quantum advantage. IBM has done more continuous error mitigation research than any other institution.
• Although IBM has already begun using error mitigation, its roadmap shows a more extensive focus on error mitigation beginning in 2024 and leading to fault tolerance later in the decade.
• During the project discussed above, the research teams operated like a ping pong match. Researchers at IBM Quantum and the University of California, Berkeley alternately took turns running increasingly complex computations. IBM Quantum tested the algorithms on the 127-qubit IBM Quantum Eagle processor, then the Berkeley researchers would run the equivalent calculations using state-of-the-art classical approximation methods on the Lawrence Berkeley National Lab and Purdue University supercomputers.
• It’s important to note that IBM isn’t claiming that any specific calculation tested on the Eagle processor exceeded the abilities of classical computers. Other specialized classical methods may soon return correct answers for the calculation IBM was testing.
• For more information about IBM’s quantum error correction strategy, you can read my previous article, “ IBM On Track To Achieve Quantum Advantage By 2026 Using Error Mitigation .”

Moor Insights & Strategy provides or has provided paid services to technology companies like all research and tech industry analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and video and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Ampere Computing, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Cadence Systems, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cohesity, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, HYCU, IBM, Infinidat, Infoblox, Infosys, Inseego, IonQ,  IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Juniper Networks, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, LoRa Alliance, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, Multefire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA, Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), NXP, onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Fivestone Partners, Frore Systems, Groq, MemryX, Movandi, and Ventana Micro.  

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New paper explores how quantum computing can unlock understanding of aging and disease

InSilico Medicine

Biological networks are interconnected. Just as knowing the ingredients alone is not sufficient to understand how to prepare a dish, understanding only the list of genes or proteins is insufficient to comprehend how they interact. 

Credit: Insilico Medicine

In a new paper in WIREs Computational Molecular Science , researchers from clinical stage artificial intelligence (AI)-driven drug discovery company Insilico Medicine (“Insilico”) demonstrate how quantum computing can be integrated into the study of living organisms in order to provide greater insight into biological processes like aging and disease. 

In May 2023, Insilico, University of Toronto’s Acceleration Consortium, and Foxconn Research Institute published research that successfully demonstrated the potential advantages of quantum generative adversarial networks in generative chemistry. Those findings were published in the American Chemical Society’s Journal of Chemical Information and Modeling .

In this latest paper, Insilico researchers present a broad picture of how combining methods from AI, quantum computing, and the physics of complex systems can help researchers advance new understandings of human health – and detail the latest breakthroughs in physics-guided AI. 

While AI has been an invaluable tool in helping researchers process and analyze large, complex biological datasets in order to find new disease pathways and connect aging and disease at the cellular level, they write, it still faces challenges in applying those insights to more complex interactions within the body. 

In order to fully understand the inner workings of living organisms, the researchers note, scientists need multimodal modeling methods that can manage three key areas of complexity: the complexity of scale, the complexity of the algorithms, and the increasing complexity of datasets. 

“While we are not a quantum company, it is important to utilize capabilities to take advantage of the speed provided by the new hybrid computing solutions and hyperscalers. As this computing goes mainstream, it may be possible to perform very complex biological simulations and discover personalized interventions with desired properties for a broad range of diseases and age-associated processes. We are very happy to see our research center in the UAE producing valuable insights in this area,” says co-author Alex Zhavoronkov, PhD, founder and co-CEO of Insilico Medicine. 

Biological processes within living systems scale from cells to organs to the whole body with lots of complex interactions between systems. Interpreting these processes needs to work on multiple scales simultaneously. And access to biological data has reached previously unimaginable levels. There’s the 1000 Genomes Project – a catalog of human genetic variation which has identified over 9 million single nucleotide variants (SNVs) – and the UK Biobank which contains full sequences from 500,000 genomes of British volunteers, to name just a couple. We need massive computing power to analyze and process it.   

Quantum computing, the researchers write, is uniquely positioned to augment AI approaches – allowing researchers to interpret across multiple levels of the biological system simultaneously. Because qubits hold values of 0 and 1 simultaneously, whereas classical bits hold only values of 0 or 1, qubits have massively greater computing speed and capability. 

The authors note that major advances in quantum computing are already underway, including IBM’s recent debut of both a utility-scale quantum processor and the company’s first modular quantum computer, which has already begun operations. 

Ultimately, the authors call for a physics-guided AI approach to better understand human biology – a new field that combines physics-based and neural network models, which they write is already underway.

By combining methods from AI, quantum computing, and the physics of complex systems, scientists can better understand how, as the authors write, “the collective interactions of smaller-scale elements within a cell, organism, or society generate emergent characteristics that can be observed at larger scales and levels of reality.” 

About Insilico Medicine

Insilico Medicine, a global clinical stage biotechnology company powered by generative AI, is connecting biology, chemistry, and clinical trials analysis using next-generation AI systems. The company has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques for novel target discovery and the generation of novel molecular structures with desired properties. Insilico Medicine is developing breakthrough solutions to discover and develop innovative drugs for cancer, fibrosis, immunity, central nervous system diseases, infectious diseases, autoimmune diseases, and aging-related diseases. www.insilico.com  

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Researchers demonstrate the power of quantum computing in drug design

by Tim Cox, Gero

Gero, an AI-driven biotech focused on aging and longevity, has demonstrated the feasibility of applying quantum computing for drug design and generative chemistry, which now offers significant promise for the future of healthcare. The research, published in Scientific Reports , outlines how a hybrid quantum-classical machine-learning model was used to interface between classical and quantum computational devices with the goal of generating novel chemical structures for potential drugs—an industry first.

The research paper follows in the wake of recent advancements from Gero, which sparked vigorous discussion among longevity experts in the scientific community when a story was published in Popular Mechanics that asserted humans can stop—but not fully reverse—aging. Earlier this year, Gero announced a target discovery deal with Pfizer, whereby Gero's machine-learning technology platform is being applied to discover potential therapeutic targets for fibrotic diseases using large-scale human data.

In this new line of research, the team explored whether a hybrid generative AI system—a deep neural network working in conjunction with commercially available quantum hardware—could suggest unique chemical structures that are synthetically feasible and possess drug-like properties.

The need for new computational approaches

The vast structural space of all possible drug-like molecules presents a monumental challenge in drug discovery . The number of realistic drug-like molecules is estimated to be between 10 23 and 10 60 —and only about 10 8 substances have ever been synthesized.

This untapped molecular landscape could hold the keys to future game-changing treatments for currently incurable age-related diseases and aging itself. However, the size and complexity of this uncharted chemical diversity space requires innovative tools for the selection of novel, biologically active and, at the same time, synthetically accessible molecules waiting to be turned into future drugs.

"These breakthroughs pave the way for a dramatic acceleration of the drug discovery process," said Peter Fedichev, CEO of Gero.

"Drug design operates at the intersection of the realms of classical and quantum phenomena , and requires simultaneous determination of quantum properties of drug-like molecules and their effects on living systems described by classical physics . This is why quantum computing will significantly augment our capacity to develop transformative treatments for the most challenging diseases and conditions, including aging itself."

A team of researchers with wide-ranging expertise

The research team comprises leading experts in diverse fields, encompassing physics, modern machine learning, generative models, quantum physics , and drug design. As documented in the paper, Hybrid quantum-classical machine learning for generative chemistry and drug design, the researchers developed a hybrid model that combines a compact discrete variational autoencoder (DVAE, a generative chemistry algorithm) in a form that can run on an existing state-of-the-art quantum device called a D-Wave quantum annealer.

The proposed system is a hybrid quantum/classical generative mode trained to sample from the distribution of drug-like and synthetically available molecules. Once the training was complete, the system could be run in the generative mode and suggested 2,331 novel chemical structures with properties typical for biologically active compounds. Encouragingly, less than 1% of the generated molecules had a high similarity to any molecule in the training set, indicating a high level of novelty in the generated compounds.

Realizing the potential of quantum computing in drug discovery

The development of quantum algorithms and hybrid quantum-classical machine-learning models for drug discovery could significantly advance the field of medicinal chemistry. Because the vastness of the structural space of possible drug-like molecules poses a significant challenge for classical computing, quantum computing may offer a much more efficient approach.

Molecules are archetypical quantum objects and hence quantum computers are naturally suited for solving complex quantum chemistry problems. And, based on the results from the scientific study, the team is now convinced that quantum algorithms can enhance machine learning in drug design—and could potentially evolve into ultimate generative chemistry algorithms.

As quantum hardware matures, specific components of the network could be converted into their fully quantum counterpart, potentially transforming the system into a Quantum VAE (QVAE) that could sample from richer, non-classical distributions. This could ultimately speed up the training of the system, potentially making quantum-enhanced generative models more efficient for drug-design applications.

"In this study, using a quantum computer, we explored an entirely new dimension in chemical space and opened a door to an entirely new room," said Alexey Fedorov, head of the RQC research team that co-authored the paper with Gero.

"As quantum computers become even more powerful, we expect that they will become more and more helpful in various studies, especially in the machine-learning domain applied to naturally quantum mechanical problems. In the next five to ten years, we will see a new generation of drugs and materials created with the help of quantum computers."

As documented in Scientific Reports , the research established two important conclusions:

1. Hybrid quantum-classical machine learning has excellent potential for drug-discovery applications . The researchers demonstrated that it is feasible to use hybrid architectures that combine quantum computers with deep classical networks for drug-discovery applications. They built a compact and yet sufficiently powerful model, small enough to fit on a state-of-the-art D-Wave quantum annealer and trained this model on a subset of the ChEMBL dataset of biologically active compounds.

2. We can generate novel chemical structures using a commercially available quantum machine . The hybrid quantum-classical model generated 2,331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for biologically active molecules from the ChEMBL dataset. Importantly, the quantum computer used to perform the calculations is available as cloud computing infrastructure.

What this means for drug discovery

Further develop quantum machine-learning models. The scientific report demonstrated that a hybrid quantum-classical machine learning model can generate novel drug-like molecules. The next step is to further develop and refine these models. This includes enhancing the generative capabilities of the model to generate more diverse and novel molecules and optimizing the efficiency of the models.

Transition to full quantum models. The researchers used a hybrid quantum-classical model as a stepping stone towards fully quantum generative models. As quantum hardware matures, the Restricted Boltzmann Machine (RBM) used in the study could be transformed into a Quantum Boltzmann Machine (QBM), and the whole system might be transformed into a Quantum Variational Autoencoder (QVAE) that could sample from potentially richer non-classical distributions.

For actual drug design, the models should be able to predict additional properties, such as the binding constant to a specific target, on top of generating novel compounds. This would allow for the generation of compounds designed to bind specific medically relevant targets.

Quantum computing and drug discovery are complex fields that require expertise from many different areas. Collaborations between quantum computing experts, pharmaceutical companies, and medical researchers would be beneficial in moving the field forward.

Fedichev sees enormous promise for the application of quantum computing to address lifespan and healthspan:

"Our goal is to slow down or even stop human aging. This is no small feat and will require intense effort and the fusion of probably yet unknown amounts of technology borrowing from the science of complex systems, modern AI and machine-learning technologies, vast biomedical datasets, and revolutionary bioengineering," he added.

"Recognizing the potential of quantum computing and quantum machine learning—a rapidly advancing field with immense promise—we have incorporated these into our arsenal, setting the stage for much-needed advancements in drug design against aging."

Journal information: Scientific Reports

Provided by Gero

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Quantum computing

Report highlights.

Publication on quantum computing has increased steeply since the early 1990s

Publication is international and significant investments are happening

The top 10 institutions with the highest publication output are located in China, France, Canada, the US, the UK and Singapore

The two most prolific authors are from Chinese and US institutions

Learn more about tracking current R&D priorities while also monitoring influential trends

Are we at the brink of a second quantum revolution?

Quantum computing is a hallmark "moonshot" of a second quantum revolution. Whereas the first revolution enabled lasers and transistors based on rules of quantum mechanics, the second aims to control quantum systems. 1,2  Quantum computing uses quantum phenomena to perform computations. Quantum computing is part of the broader area of quantum information technologies or quantum technologies. Quantum technologies seek to understand how quantum phenomena can be used in computing, communication, sensing and metrology to go beyond what classical systems can do.

For example, qubits 3  — the quantum version of the classical bit — are not restricted to the pure 0 or 1 of binary digits. Physically a qubit could be realized using the polarization of a single photon, or electron spin, or currents in a superconductor. At the end of a calculation when the answer is read, each qubit will come out as either 0 or 1. But during the calculation the qubit represents both 0 and 1 simultaneously. This is called superposition. This core aspect of quantum computing enables quantum computers to outperform classical computers in some problems requiring intense computation. A second crucial aspect is entanglement, or the superposition of a pair of more than one qubits. This allows a quantum computer to explore a much larger computational space than any classical computer can explore.

The second quantum revolution is where you’re really using the quantum mechanics to do everything for you.

Ray Simmonds, U.S. National Institute of Standards and Technology 1

Quantum computing has made a big splash across the news in the last few years — from Google’s 53-qubit quantum computer “Sycamore” achieving what has been coined quantum supremacy to multibillion-dollar initiatives around the world to develop quantum technologies for computing and beyond. The race is on.

Research methodology

This report is based on research and analysis conducted in Scopus, an expert-curated abstract and citation database, in February 2021.

The search was conducted assuming a limited knowledge of the topic and interest in research activity around possible applications of quantum computing. It began with "quantum comput*" to combine a loose phrase with a wild card (*), which accommodated word variations like computation.

To broaden the search slightly, we added the terms "quantum algorith*", "quantum simulation*", "qubit" and "quantum bit" to arrive at the search used for this research trends report:

TITLE-ABS-KEY ("Quantum comput*") OR TITLE-ABS-KEY ("Qauntum algorith*") OR TITLE-ABS-KEY ("Quantum simulation*")) OR (TITLE-ABS-KEY ("Qubit*") OR TITLE-ABS-KEY ("quantum bit*")

This report provides search results for document titles, abstracts and keywords. Scopus indexes content from 25,000 active titles and 7,000 publishers, as well as patents and conference abstracts. Scopus enriches data, metrics and analytical tools inform both research and business strategy, driving better decisions and outcomes.

The search will pick up some documents in related areas such as quantum communication, but also exclude some of the major works in these areas. It could be further refined to include or remove a portion of other areas, such as "quantum simulation", "quantum communication", "quantum cryptography", and "quantum sensing". Am ore extensive search can be done to cover the broader area of quantum technologies, but here the focus is more on quantum computing, including quantum simulation.

A brief history of quantum computing

In 1982, the physicist and 1957 Nobel Laureate Richard Feynman discussed a machine that would operate on quantum mechanical principles to simulate the behavior of one quantum system using another quantum system - a quantum simulator. 4 In 1985, David Deutsch of oxford University further advanced the field by proposing a quantum Turing machine (based on the pioneering work of Alan Turing on what constitutes a general computer) and specified an algorithm designed to run on a quantum computer. 5 Beyond the realm of researchers in quantum physics and theoretical computer science, the field really took off in the mid-90s. In 1994, the mathematician Peter Shor proposed an algorithm for a real world "killer application" of quantum computers. It would factorize large numbers into their prime number counterparts exponentially faster than possible with a classical computer. 6

Why was Shor's result so important? Much of modern encryption, such as RSA encryption, is based on the idea that it is very difficult to factor a large integer that is the product of two large prime numbers. 7 While it is easy to take two primes and multiply them together to form a big number, doing the inverse from a large number to find its constituting unknown primes is hard. In fact, so hard that a classical computer might take a lifetime or more to perform the calculation. However, on a quantum computer, if it can be made to operate with a large number of qubits, finding prime numbers could be done efficiently (mathematically in polynomial time), putting the basis of much modern encryption used on the internet at risk, If that hard problem can be solved, what other hard problems might become much easier? Actually, quite a few, including hacking bitcoins and other cryptocurrencies.

Publication rate on quantum computing has steeply increased

Figure 1. Quantum computing documents by year, 1982-2020

Scopus search results show a steady increase in research output in quantum computing and related areas, really kicking off in 1994. The growth increases steadily resulting in over 48,000 publication. Especially from 2015 onward there is sa steeper rate of publication.

Concurrent with the faster research developments, achievements in quantum computing in terms of number of qubits used for computations emerge:

2012 — A group at University of Bristol factored the number 21 with Shor's algorithm 8

2017 — D-Wave Systems announced the first sale of its D-Wave 2000Q quantum computer; it is not a general purpose quantum computer, but can address optimization problems 9

2020 — Google accurately simulated the binding of hydrogen chains and isomerization of diazene using Sycamore 10

A significant hurdle in quantum computing

Progress in quantum computing and the broader area of quantum technologies has been quite remarkable. However, we still may face significant hurdles before a general purpose quantum computer is available to tackle, for instance, really large scale factorization into prime numbers or computational tasks, optimization problems excluded. The challenge is that a quantum computer must do its computation, the superposition that allows for the computational advantage breaks down, a process called decoherence. 11 The more qubits used, the more susceptible to noise the superposition becomes. This is why real-world quantum computers have been so hard to build.

Physical implementations of quantum computers today, such as those using semiconduction technology, must operate at close to absolute zero temperature in a highly isolated environment. Related areas like quantum communication (notably quantum cryptography) and quantum sensing do not rely on a large number of qubits to operate. As an interesting twist, while quantum computing may potentially make current encryption schemes unsecure, quantum cryptography would be a way to provide fundamentally secure encryption schemes. 12

Are there potential solutions to make computer more resistant to distubances? In classical computing and communication, error correcting codes are introduced to correct for noise; for instance, if someone cannot hear what you say, you repeat your message. Impressive work has been done to generalize the concept of error correction codes into quantum error correction for an overview, see the now classical textbook by Michael Nielsen and Isaac Chuang. 13 To correct errors in one qubit, however, requires the use of several qubits, and the technological race is still to scale up quantum, computers for large scale operations. Physicist John Preskill called this the Noisy Intermediate-Scale Quantum (NISQ) era as physicist and engineers are still working hard to solve all technical challlenges.

As quantum computing moves toward real-world applications, it continues to be an area for fundamental new discoveries in physics. In 2012, Serge Haroche and David Wineland were awarded the Nobel Prize in Physics "for ground-breaking experimental methods that enable measuring and manipulation of individual quantum systems". 14

Their work has profound implications for quantum information and quantum computing:

Their ground-breaking methods have enabled this field of research to take the very first steps towards building a new type of super fast computer based on quantum physics. Perhaps the quantum computer will change our everyday lives in this century in the same radical way as the classical computer did in the last century." - NobelPize.org

Top quantum computing publication types are journal articles and conference papers

Figure 2. Quantum computing documents by source, 1982-2020

Quantum technologies/computing research outputs with Scopus are primarily journal articles. This reflects a long history of basic research in physics, with many well-established journals (more on journals below). The novelty and rapid evolution of the field is reflected in the high proportion of publications emerging from conferences (21%), which is about twice as high as the overall proportion of conference papers in Scopus(11%). It is notable that the quantum technology community was early adopters of using the ArXiv preprint server to share their works.

Quantum computing research has an international footprint

Figure 3. Quantum comp[uting global top affiliations by document count, 1982-2020

The 10 institutions with the highest publication output are located in China, France (via the CNRS laboratories), Canada, the US, the UK and Singapore. The Chinese Academy of Sciences shows particularly hight output. China's overall spending on quantum technologies, including quantum computing is not public information. However, the government's interest in funding quantum technologies is clear from the-Five-Year Plan 2016-202 and a 10 billion USD investment to build the world's largest quantum research facility. 15

The majority of author affiliations are academic and governmental research institutes. Private sector enterprises appear much further down the list with companies such as Nippon Telegraph and Telephone (NTT), IBM Thomas Watson Research Center, and Microsoft Research.

Top Contributors to quantum computing

Figure 4. Quantum computing global top authors by document count, 1982-2020

The two first authors on the list are Professors Guang-Can Guo and Franco Nori. Prof. Guang-Can Guo opens in new tab/window , University of Science and Technology, Hefei, China, publishes on the broad topics of quantum information and has many publications in photonic quantum computing.

Figure 5. Scopus author profile of Guang-Can Guo opens in new tab/window , University of Science and Technology of China (excerpt of full profile)

Prof. Franco Nori opens in new tab/window , University of Michigan, Ann Arbor, US, is a leading researcher on solid state quantum computing. Prof. Nori also has a long history working in Japan.

Figure 6.  Scopus author profile of Franco Nori opens in new tab/window , University of Michigan, USA, and RIKEN, Japan (excerpt of full profile)

Physics, optical sciences and engineering journals lead in quantum computing publications

Figure 7. Quantum computing documents per year by source, 1982–2020

Physics, optical sciences and engineering journals top the list of journals leading in quantum computing publications. The journal with the highest number of publications in the field is  Physical Review A . Over the last decade, the journal has undergone topical restructuring that led to a change in statement scope in the 1990s ( Atomic, Molecular and Optical Physics ) and then the explicit inclusion of quantum information in  Physical Review A  as of 2016. The Journal  Quantum Information Processing  was first published in 2002 and is a relatively new contributor to the body of literature.

Do you need to follow developments in quantum computing and the broader area of quantum technologies?

Absolutely.

Quantum technologies, not only quantum computing, are shifting towards enabling real-world uses. Quantum cryptography is already seeing some commercial applications. Research and quantum technology applications are fueled by strategic government investment in the US, 19 the EU 20 and China. 14 Industry initiatives include the Pistoia Alliance 21 and IBM's Quantum Summit 2020, 22 , where, among others, IBM discussed their ambition to build a 1000 bit quantum computer by 2023. 23

Technology business journalist Kara Swisher once asked Google CEO Sundar Pichai it was possible that Google missed the Cloud service trend in the early 2000s and allowed Amazon a head start. (Google has invested heavily to catch up.) Pichai's answer was that they had been busy doing other things.

Don't miss out on an important research trend. Bookmark this page for updates and a head start in planning for quantum computing.

Business Intelligence for science and technology

With Scopus, track your current R&D priorities while also monitoring influential trends. This rich abstract and citation database provides insights into comprehensive interdisciplinary research and analytics. Gain scientific knowledge and competitive intelligence as you:

Track research trends Get alerts of breakthroughs that can impact your business

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J. Dowling and G. Milburn, “ Quantum Technology, the Second Quantum Revolution opens in new tab/window ,” Phil. Trans. R. Soc. Lond. A (2003).

NIST. 2018.  The second quantum revolution opens in new tab/window . Accessed September 2020.

The word qubit coined in 1995 by B. Shumacher and W.K. Wooters,see https://quantumfrontiers.com/2015/06/09/who-named-the-qubit/ opens in new tab/window

Feynman, R. 1982. Simulating physics with computers. Int. J. Theor. Phys. 21: 467.

Deutsch, David (1985). "Quantum theory, the Church-Turing principle and the universal quantum computer." Proceedings of the Royal Society A. 400(1818): 97–117.

Shor, P.W. (1994). "Algorithms for quantum computation: discrete logarithms and factoring". Proceedings 35th Annual Symposium on Foundations of Computer Science. IEEE Comput. Soc. Press: 124–134.

For an explanation of RSA encryption, https://www.abc.net.au/news/science/2018-01-20/how-prime-numbers-rsa-encryption-works/ opens in new tab/window , accessed Feb. 2021.

Martin-Lopez, E. et al. 2012. Experimental realization of Shor’s quantum factoring algorithm using qubit recycling. Nature Photonics 6: 773.

D-Wave Systems. 2017. Temporal Defense Systems purchases the first D-Wave 2000Q quantum computer opens in new tab/window . Accessed September 2020.

Google AI Quantum and Collaborators. 2020. Hartree-Fock on a superconducting qubit quantum computer. Science 369: 1084.

W. Zhurek, Decoherence and the Transition from Quantum to Classical”  Physics” Today 44, 10, 36 (1991);  https://doi.org/10.1063/1.881293 opens in new tab/window .

For a brief on quantum cryptography, see for instance  ETSI White Paper No. 8

Quantum Safe Cryptography and Security An introduction, benefits, enablers and challenges opens in new tab/window June 2015 ISBN No. 979-10-92620-03-0.

Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quantum Information. Cambridge University Press (2000).

Nobel Prize in Physics 2012, Press Release opens in new tab/window .

IDQ. 2018. China’s growing investment in quantum computing opens in new tab/window . Accessed September 2020.

Marr, B. 2017. 6 Practical Examples Of How Quantum Computing Will Change Our World opens in new tab/window . Accessed September 2020.

Drug Discovery Online. 2020. Researchers use NSF Convergence Accelerator to shorten drug discovery timeline opens in new tab/window . Accessed November 2020.

Alvarez-Rodriguez, U. et al. (2918) Quantum artificial life in an IBM quantum computer. Scientific Reports 8: 14793.

Kratsios, M. and Liddell, C. 2020. The Trump administration is investing 1 billion USD in research institutes to advance industries of the future. Accessed September 2020.

European Commission. 2018. Quantum Technologies Flagship kicks off with first 20 projects opens in new tab/window . Accessed September 2020.

Pistoia Alliance. Almost one third of life science companies set to being quantum computing evaluation this year opens in new tab/window . Accessed September 2020.

Annunziata, A. 2020. IBM Quantum Summit 2020: Exploring the promise of quantum computing for industry opens in new tab/window . Accessed November 2020.

Adrian Cho. IBM promises 1000-qubit quantum computer — a milestone — by 2023 opens in new tab/window , Science, Sep. 15, By Adrian ChoSep. 15, 2020.

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Quantum Physics

Title: quantum computing 2022.

Abstract: Quantum technology is full of figurative and literal noise obscuring its promise. In this overview, we will attempt to provide a sober assessment of the promise of quantum technology with a focus on computing. We provide a tour of quantum computing and quantum technology that is aimed to be comprehensible to scientists and engineers without becoming a popular account. The goal is not a comprehensive review nor a superficial introduction but rather to serve as a useful map to navigate the hype, the scientific literature, and upcoming press releases about quantum technology and quantum computing. We have aimed to cite the most recent topical reviews, key results, and guide the reader away from fallacies and towards active discussions in the current quantum computing literature. The goal of this article was to be pedantic and introductory without compromising on the science.

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Ph.D. student is lead author on paper published on cover of 'Optica Quantum'

Interdisciplinary research from the center for detectors focuses on entanglement in quantum information systems.

Ph.D. student Evan Manfreda-Schulz’s first published paper was featured on the cover of Optica Quantum.

Microsystems engineering Ph.D. student Evan Manfreda-Schulz ’20 ( physics ) accomplished something many academic researchers aim for when his first paper was published on the cover of Optica Quantum .

The paper, “Generating high-dimensional entanglement using a foundry-fabricated photonic integrated circuit,” is a culmination of years of study in the realm of quantum information systems. The work was co-authored by J. Dulany Elliot ’23 (physics and computer science ); Matthew can Niekerk ’23 Ph.D. (microsystems engineering); Daniel Proctor ’23 (physics), Mario Ciminelli , engineer in the Department of Electrical and Microelectronic Engineering ; Tom Palone , reliability and packaging engineer in the Department of Manufacturing and Mechanical Engineering Technology ; Christopher C. Tison, Air Force Research Laboratory; Michael L. Fanto, Air Force Research Laboratory; Stefan F. Preble , professor in the Department of Electrical and Microelectronic Engineering; and Gregory A. Howland , assistant professor in the School of Physics and Astronomy . The work was supported, in part, by the Air Force Research Laboratory and the RIT-L3Harris Quantum Information Science and Technology Collaboration.

RIT has emerged as a leader in quantum technology, which leverages the behavior of quantum systems at the single particle level. Quantum resources such as entanglement, which is observed via correlations in the measurement statistics of disparate particles, are key to unlocking new possibilities and capabilities in computing, communication, imaging, and sensing.

“Entanglement is a resource for all sorts of things that we might want to use quantum information systems for,” explained Manfreda-Schulz. “Generating it is one part of the challenge, and figuring out how much of it you have is another.”

The team used a silicon photonic integrated circuit to generate 1.45 ebits of high-dimensional quantum entanglement. They documented how the system was designed, packaged, and calibrated using an array of four tunable photon pair sources. Then they described how they used an entanglement witness to quantify the resource they generated. The research represents an advancement towards the efficient deployment of quantum information systems built on the platform of silicon integrated photonics.

This research comes under the direction of RIT’s Center for Detectors , an interdisciplinary center that designs, develops, and implements photon devices for a wide range of scientific areas, and the Integrated Photonics Lab .

“I’m really grateful for everyone in the group,” added Manfreda-Schulz. “It’s not just something that I did, it’s our whole team. I’m appreciative of all the help I received along the way.”

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The paper includes definitions of elements of quantum IT referred to classical technologies of computation. It explains the principles of transformation of calculating algorithms to the domain of quantum computations using the optimisation and matrix calculus. Exemplary applications of classical algorithms are presented with possibilities of their realisation in domain of quantum IT. Autor presents some possibilities for using quantum algorithms in new computation technologies concerning quantum cryptography and data analyses with complex computations.

Post-Quantum and Code-Based Cryptography—Some Prospective Research Directions

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems, based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing poses a major threat to the modern cryptosystems based on modular arithmetic, whereby even the computationally hard problems which constitute the strength of the modular arithmetic ciphers could be solved in polynomial time. This threat triggered post-quantum cryptography research to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides an overview of the various research directions that have been explored in post-quantum cryptography and, specifically, the various code-based cryptography research dimensions that have been explored. Some potential research directions that are yet to be explored in code-based cryptography research from the perspective of codes is a key contribution of this paper.

Trends In Natural Language Processing : Scope And Challenges

Quantum cryptography is a comparatively new and special type of cryptography which uses Quantum mechanics to provide unreal protection of data/information and unconditionally secure communications. This is achieved with Quantum Key Distribution (QKD) protocols which is a representation of an essential practical application of Quantum Computation. In this paper the authors will venture the concept of QKD by reviewinghow QKD works, the authors shall take a look at few protocols of QKD, followed by a practical example of Quantum Cryptography using QKD and certain limitations from the perspective of Computer Science in specific and Quantum Physics in general.

Securing the future internet of things with post‐quantum cryptography

Efficient implementation of finite field arithmetic for binary ring-lwe post-quantum cryptography through a novel lookup-table-like method, quantum cryptography, export citation format, share document.