Tag Archives: Seth Lloyd

Microsoft, D-Wave Systems, quantum computing, and quantum supremacy?

Before diving into some of the latest quantum computing doings, here’s why quantum computing is so highly prized and chased after, from the Quantum supremacy Wikipedia entry, Note: Links have been removed,

In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem.[1][2][3] The term was coined by John Preskill in 2011,[1][4] but the concept dates to Yuri Manin’s 1980[5] and Richard Feynman’s 1981[6] proposals of quantum computing.

Quantum supremacy and quantum advantage have been mentioned a few times here over the years. You can check my March 6, 2020 posting for when researchers from the University of California at Santa Barbara claimed quantum supremacy and my July 31, 2023 posting for when D-Wave Systems claimed a quantum advantage on optimization problems. I’d understood quantum supremacy and quantum advantage to be synonymous but according the article in Betakit (keep scrolling down to the D-Wave subhead and then, to ‘A controversy of sorts’ subhead in this posting), that’s not so.

The latest news on the quantum front comes from Microsoft (February 2025) and D-Wave systems (March 2025).

Microsoft claims a new state of matter for breakthroughs in quantum computing

Here’s the February 19, 2025 news announcement from Microsoft’s Chetan Nayak, Technical Fellow and Corporate Vice President of Quantum Hardware, Note: Links have been removed,

Quantum computers promise to transform science and society—but only after they achieve the scale that once seemed distant and elusive, and their reliability is ensured by quantum error correction. Today, we’re announcing rapid advancements on the path to useful quantum computing:

  • Majorana 1: the world’s first Quantum Processing Unit (QPU) powered by a Topological Core, designed to scale to a million qubits on a single chip.
  • A hardware-protected topological qubit: research published today in Nature, along with data shared at the Station Q meeting, demonstrate our ability to harness a new type of material and engineer a radically different type of qubit that is small, fast, and digitally controlled.
  • A device roadmap to reliable quantum computation: our path from single-qubit devices to arrays that enable quantum error correction.
  • Building the world’s first fault-tolerant prototype (FTP) based on topological qubits: Microsoft is on track to build an FTP of a scalable quantum computer—in years, not decades—as part of the final phase of the Defense Advanced Research Projects Agency (DARPA) Underexplored Systems for Utility-Scale Quantum Computing (US2QC) program.

Together, these milestones mark a pivotal moment in quantum computing as we advance from scientific exploration to technological innovation.

Harnessing a new type of material

All of today’s announcements build on our team’s recent breakthrough: the world’s first topoconductor. This revolutionary class of materials enables us to create topological superconductivity, a new state of matter that previously existed only in theory. The advance stems from Microsoft’s innovations in the design and fabrication of gate-defined devices that combine indium arsenide (a semiconductor) and aluminum (a superconductor). When cooled to near absolute zero and tuned with magnetic fields, these devices form topological superconducting nanowires with Majorana Zero Modes (MZMs) at the wires’ ends.

Chris Vallance’s February 19, 2025 article for the British Broadcasting Corporation (BBC) news online website provides a description of Microsoft’s claims and makes note of the competitive quantum research environment,

Microsoft has unveiled a new chip called Majorana 1 that it says will enable the creation of quantum computers able to solve “meaningful, industrial-scale problems in years, not decades”.

It is the latest development in quantum computing – tech which uses principles of particle physics to create a new type of computer able to solve problems ordinary computers cannot.

Creating quantum computers powerful enough to solve important real-world problems is very challenging – and some experts believe them to be decades away.

Microsoft says this timetable can now be sped up because of the “transformative” progress it has made in developing the new chip involving a “topological conductor”, based on a new material it has produced.

The firm believes its topoconductor has the potential to be as revolutionary as the semiconductor was in the history of computing.

But experts have told the BBC more data is needed before the significance of the new research – and its effect on quantum computing – can be fully assessed.

Jensen Huang – boss of the leading chip firm, Nvidia – said in January he believed “very useful” quantum computing would come in 20 years.

Chetan Nayak, a technical fellow of quantum hardware at Microsoft, said he believed the developments would shake up conventional thinking about the future of quantum computers.

“Many people have said that quantum computing, that is to say useful quantum computers, are decades away,” he said. “I think that this brings us into years rather than decades.”

Travis Humble, director of the Quantum Science Center of Oak Ridge National Laboratory in the US, said he agreed Microsoft would now be able to deliver prototypes faster – but warned there remained work to do.

“The long term goals for solving industrial applications on quantum computers will require scaling up these prototypes even further,” he said.

While rivals produced a steady stream of announcements – notably Google’s “Willow” at the end of 2024 – Microsoft seemed to be taking longer.

Pursuing this approach was, in the company’s own words, a “high-risk, high-rewards” strategy, but one it now believes is going to pay off.

If you have the time, do read Vallance’s February 19, 2025 article.

The research paper

Purdue University’s (Indiana, US) February 25, 2025 news release on EurekAlert announces publication of the research, Note: Links have been removed,

Microsoft Quantum published an article in Nature on Feb. 19 [2025] detailing recent advances in the measurement of quantum devices that will be needed to realize a topological quantum computer. Among the authors are Microsoft scientists and engineers who conduct research at Microsoft Quantum Lab West Lafayette, located at Purdue University. In an announcement by Microsoft Quantum, the team describes the operation of a device that is a necessary building block for a topological quantum computer. The published results are an important milestone along the path to construction of quantum computers that are potentially more robust and powerful than existing technologies.

“Our hope for quantum computation is that it will aid chemists, materials scientists and engineers working on the design and manufacturing of new materials that are so important to our daily lives,” said Michael Manfra, scientific director of Microsoft Quantum Lab West Lafayette and the Bill and Dee O’Brien Distinguished Professor of Physics and Astronomy, professor of materials engineering, and professor of electrical and computer engineering at Purdue. “The promise of quantum computation is in accelerating scientific discovery and its translation into useful technology. For example, if quantum computers reduce the time and cost to produce new lifesaving therapeutic drugs, that is real societal impact.” 

The Microsoft Quantum Lab West Lafayette team advanced the complex layered materials that make up the quantum plane of the full device architecture used in the tests. Microsoft scientists working with Manfra are experts in advanced semiconductor growth techniques, including molecular beam epitaxy, that are used to build low-dimensional electron systems that form the basis for quantum bits, or qubits. They built the semiconductor and superconductor layers with atomic layer precision, tailoring the material’s properties to those needed for the device architecture.

Manfra, a member of the Purdue Quantum Science and Engineering Institute, credited the strong relationship between Purdue and Microsoft, built over the course of a decade, with the advances conducted at Microsoft Quantum Lab West Lafayette. In 2017 Purdue deepened its relationship with Microsoft with a multiyear agreement that includes embedding Microsoft employees with Manfra’s research team at Purdue.

“This was a collaborative effort by a very sophisticated team, with a vital contribution from the Microsoft scientists at Purdue,” Manfra said. “It’s a Microsoft team achievement, but it’s also the culmination of a long-standing partnership between Purdue and Microsoft. It wouldn’t have been possible without an environment at Purdue that was conducive to this mode of work — I attempted to blend industrial with academic research to the betterment of both communities. I think that’s a success story.”

Quantum science and engineering at Purdue is a pillar of the Purdue Computes initiative, which is focused on advancing research in computing, physical AI, semiconductors and quantum technologies.

“This research breakthrough in the measurement of the state of quasi particles is a milestone in the development of topological quantum computing, and creates a watershed moment in the semiconductor-superconductor hybrid structure,” Purdue President Mung Chiang said. “Marking also the latest success in the strategic initiative of Purdue Computes, the deep collaboration that Professor Manfra and his team have created with the Microsoft Quantum Lab West Lafayette on the Purdue campus exemplifies the most impactful industry research partnership at any American university today.”

Most approaches to quantum computers rely on local degrees of freedom to encode information. The spin of an electron is a classic example of a qubit. But an individual spin is prone to disturbance — by relatively common things like heat, vibrations or interactions with other quantum particles — which can corrupt quantum information stored in the qubit, necessitating a great deal of effort in detecting and correcting errors. Instead of spin, topological quantum computers store information in a more distributed manner; the qubit state is encoded in the state of many particles acting in concert. Consequently, it is harder to scramble the information as the state of all the particles must be changed to alter the qubit state.

In the Nature paper, the Microsoft team was able to accurately and quickly measure the state of quasi particles that form the basis of the qubit.

“The device is used to measure a basic property of a topological qubit quickly,” Manfra said. “The team is excited to build on these positive results.”

“The team in West Lafayette pushed existing epitaxial technology to a new state-of-the-art for semiconductor-superconductor hybrid structures to ensure a perfect interface between each of the building blocks of the Microsoft hybrid system,” said Sergei Gronin, a Microsoft Quantum Lab scientist.

“The materials quality that is required for quantum computing chips necessitates constant improvements, so that’s one of the biggest challenges,” Gronin said. “First, we had to adjust and improve semiconductor technology to meet a new level that nobody was able to achieve before. But equally important was how to create this hybrid system. To do that, we had to merge a semiconducting part and a superconducting part. And that means you need to perfect the semiconductor and the superconductor and perfect the interface between them.”

While work discussed in the Nature article was performed by Microsoft employees, the exposure to industrial-scale research and development is an outstanding opportunity for Purdue students in Manfra’s academic group as well. John Watson, Geoffrey Gardner and Saeed Fallahi, who are among the coauthors of the paper, earned their doctoral degrees under Manfra and now work for Microsoft Quantum at locations in Redmond, Washington, and Copenhagen, Denmark. Most of Manfra’s former students now work for quantum computing companies, including Microsoft. Tyler Lindemann, who works in the West Lafayette lab and helped to build the hybrid semiconductor-superconductor structures required for the device, is earning a doctoral degree from Purdue under Manfra’s supervision.

“Working in Professor Manfra’s lab in conjunction with my work for Microsoft Quantum has given me a head start in my professional development, and been fruitful for my academic work,” Lindemann said. “At the same time, many of the world-class scientists and engineers at Microsoft Quantum have some background in academia, and being able to draw from their knowledge and experience is an indispensable resource in my graduate studies. From both perspectives, it’s a great opportunity.”

Here’s a link to and a citation for the paper,

Interferometric single-shot parity measurement in InAs–Al hybrid devices by Microsoft Azure Quantum, Morteza Aghaee, Alejandro Alcaraz Ramirez, Zulfi Alam, Rizwan Ali, Mariusz Andrzejczuk, Andrey Antipov, Mikhail Astafev, Amin Barzegar, Bela Bauer, Jonathan Becker, Umesh Kumar Bhaskar, Alex Bocharov, Srini Boddapati, David Bohn, Jouri Bommer, Leo Bourdet, Arnaud Bousquet, Samuel Boutin, Lucas Casparis, Benjamin J. Chapman, Sohail Chatoor, Anna Wulff Christensen, Cassandra Chua, Patrick Codd, William Cole, Paul Cooper, Fabiano Corsetti, Ajuan Cui, Paolo Dalpasso, Juan Pablo Dehollain, Gijs de Lange, Michiel de Moor, Andreas Ekefjärd, Tareq El Dandachi, Juan Carlos Estrada Saldaña, Saeed Fallahi, Luca Galletti, Geoff Gardner, Deshan Govender, Flavio Griggio, Ruben Grigoryan, Sebastian Grijalva, Sergei Gronin, Jan Gukelberger, Marzie Hamdast, Firas Hamze, Esben Bork Hansen, Sebastian Heedt, Zahra Heidarnia, Jesús Herranz Zamorano, Samantha Ho, Laurens Holgaard, John Hornibrook, Jinnapat Indrapiromkul, Henrik Ingerslev, Lovro Ivancevic, Thomas Jensen, Jaspreet Jhoja, Jeffrey Jones, Konstantin V. Kalashnikov, Ray Kallaher, Rachpon Kalra, Farhad Karimi, Torsten Karzig, Evelyn King, Maren Elisabeth Kloster, Christina Knapp, Dariusz Kocon, Jonne V. Koski, Pasi Kostamo, Mahesh Kumar, Tom Laeven, Thorvald Larsen, Jason Lee, Kyunghoon Lee, Grant Leum, Kongyi Li, Tyler Lindemann, Matthew Looij, Julie Love, Marijn Lucas, Roman Lutchyn, Morten Hannibal Madsen, Nash Madulid, Albert Malmros, Michael Manfra, Devashish Mantri, Signe Brynold Markussen, Esteban Martinez, Marco Mattila, Robert McNeil, Antonio B. Mei, Ryan V. Mishmash, Gopakumar Mohandas, Christian Mollgaard, Trevor Morgan, George Moussa, Chetan Nayak, Jens Hedegaard Nielsen, Jens Munk Nielsen, William Hvidtfelt Padkar Nielsen, Bas Nijholt, Mike Nystrom, Eoin O’Farrell, Thomas Ohki, Keita Otani, Brian Paquelet Wütz, Sebastian Pauka, Karl Petersson, Luca Petit, Dima Pikulin, Guen Prawiroatmodjo, Frank Preiss, Eduardo Puchol Morejon, Mohana Rajpalke, Craig Ranta, Katrine Rasmussen, David Razmadze, Outi Reentila, David J. Reilly, Yuan Ren, Ken Reneris, Richard Rouse, Ivan Sadovskyy, Lauri Sainiemi, Irene Sanlorenzo, Emma Schmidgall, Cristina Sfiligoj, Mustafeez Bashir Shah, Kevin Simoes, Shilpi Singh, Sarat Sinha, Thomas Soerensen, Patrick Sohr, Tomas Stankevic, Lieuwe Stek, Eric Stuppard, Henri Suominen, Judith Suter, Sam Teicher, Nivetha Thiyagarajah, Raj Tholapi, Mason Thomas, Emily Toomey, Josh Tracy, Michelle Turley, Shivendra Upadhyay, Ivan Urban, Kevin Van Hoogdalem, David J. Van Woerkom, Dmitrii V. Viazmitinov, Dominik Vogel, John Watson, Alex Webster, Joseph Weston, Georg W. Winkler, Di Xu, Chung Kai Yang, Emrah Yucelen, Roland Zeisel, Guoji Zheng & Justin Zilke. Nature 638, 651–655 (2025). DOI: https://doi.org/10.1038/s41586-024-08445-2 Published online: 19 February 2025 Issue Date: 20 February 2025

This paper is open access. Note: I usually tag all of the authors but not this time.

Controversy over this and previous Microsoft quantum computing claims

Elizabeth Hlavinka’s March 17, 2025 article for Salon.com provides an overview, Note: Links have been removed,

The matter making up the world around us has long-since been organized into three neat categories: solids, liquids and gases. But last month [February 2025], Microsoft announced that it had allegedly discovered another state of matter originally theorized to exist in 1937. 

This new state of matter called the Majorana zero mode is made up of quasiparticles, which act as their own particle and antiparticle. The idea is that the Majorana zero mode could be used to build a quantum computer, which could help scientists answer complex questions that standard computers are not capable of solving, with implications for medicine, cybersecurity and artificial intelligence.

In late February [2025], Sen. Ted Cruz presented Microsoft’s new computer chip at a congressional hearing, saying, “Technologies like this new chip I hold in the palm of my hand, the Majorana 1 quantum chip, are unlocking a new era of computing that will transform industries from health care to energy, solving problems that today’s computers simply cannot.”

However, Microsoft’s announcement, claiming a “breakthrough in quantum computing,” was met with skepticism from some physicists in the field. Proving that this form of quantum computing can work requires first demonstrating the existence of Majorana quasiparticles, measuring what the Majorana particles are doing, and creating something called a topological qubit used to store quantum information.

But some say that not all of the data necessary to prove this has been included in the research paper published in Nature, on which this announcement is based. And due to a fraught history of similar claims from the company being disputed and ultimately rescinded, some are extra wary of the results. [emphasis mine]

It’s not the first time Microsoft has faced backlash from presenting findings in the field. In 2018, the company reported that they had detected the presence of Majorana zero-modes in a research paper, but it was retracted by Nature, the journal that published it after a report from independent experts put their findings under more intense scrutiny.

In the [2018] report, four physicists not involved in the research concluded that it did not appear that Microsoft had intentionally misrepresented the data, but instead seemed to be “caught up in the excitement of the moment [emphasis mine].”

Establishing the existence of these particles is extremely complex in part because disorder in the device can create signals that mimic these quasiparticles when they are not actually there. 

Modern computers in use today are encoded in bits, which can either be in a zero state (no current flowing through them), or a one state (current flowing.) These bits work together to send information and signals that communicate with the computer, powering everything from cell phones to video games.

Companies like Google, IBM and Amazon have invested in designing another form of quantum computer that uses chips built with “qubits,” or quantum bits. Qubits can exist in both zero and one states at the same time due to a phenomenon called superposition. 

However, qubits are subject to external noise from the environment that can affect their performance, said Dr. Paolo Molignini, a researcher in theoretical quantum physics at Stockholm University.

“Because qubits are in a superposition of zero and one, they are very prone to errors and they are very prone to what is called decoherence, which means there could be noise, thermal fluctuations or many things that can collapse the state of the qubits,” Molignini told Salon in a video call. “Then you basically lose all of the information that you were encoding.”

In December [2024], Google said its quantum computer could perform a calculation that a standard computer could complete in 10 septillion years — a period far longer than the age of the universe — in just under five minutes.

However, a general-purpose computer would require billions of qubits, so these approaches are still a far cry from having practical applications, said Dr. Patrick Lee, a physicist at the Massachusetts Institute of Technology [MIT], who co-authored the report leading to the 2018 Nature paper’s retraction.

Microsoft is taking a different approach to quantum computing by trying to develop  a topological qubit, which has the ability to store information in multiple places at once. Topological qubits exist within the Majorana zero states and are appealing because they can theoretically offer greater protection against environmental noise that destroys information within a quantum system.

Think of it like an arrow, where the arrowhead holds a portion of the information and the arrow tail holds the rest, Lee said. Distributing information across space like this is called topological protection.

“If you are able to put them far apart from each other, then you have a chance of maintaining the identity of the arrow even if it is subject to noise,” Lee told Salon in a phone interview. “The idea is that if the noise affects the head, it doesn’t kill the arrow and if it affects only the tail it doesn’t kill your arrow. It has to affect both sides simultaneously to kill your arrow, and that is very unlikely if you are able to put them apart.”

… Lee believes that even if the data doesn’t entirely prove that topological qubits exist in the Majorana zero-state, it still represents a scientific advancement. But he noted that several important issues need to be solved before it has practical implications. For one, the coherence time of these particles — or how long they can exist without being affected by environmental noise — is still very short, he explained.

“They make a measurement, come back, and the qubit has changed, so you have lost your coherence,” Lee said. “With this very short time, you cannot do anything with it.”

“I just wish they [Microsoft] were a bit more careful with their claims because I fear that if they don’t measure up to what they are saying, there might be a backlash at some point where people say, ‘You promised us all these fancy things and where are they now?’” Molignini said. “That might damage the entire quantum community, not just themselves.”

Iif you have the time, please read Hlavinka’s March 17, 2025 article in its entirety .

D-Wave Quantum Systems claims quantum supremacy over real world problem solution

A March 15, 2025 article by Bob Yirka for phys.org announces the news from D-Wave Quantum Systems. Note: The company, which had its headquarters in Canada (Burnaby, BC) now seems to be a largely US company with its main headquarters in Palo Alto, California and an ancillary or junior (?) headquarters in Canada, Note: A link has been removed,

A team of quantum computer researchers at quantum computer maker D-Wave, working with an international team of physicists and engineers, is claiming that its latest quantum processor has been used to run a quantum simulation faster than could be done with a classical computer.

In their paper published in the journal Science, the group describes how they ran a quantum version of a mathematical approximation regarding how matter behaves when it changes states, such as from a gas to a liquid—in a way that they claim would be nearly impossible to conduct on a traditional computer.

Here’s a March 12, 2025 D-Wave Systems (now D-Wave Quantum Systems) news release touting its real world problem solving quantum supremacy,

New landmark peer-reviewed paper published in Science, “Beyond-Classical Computation in Quantum Simulation,” unequivocally validates D-Wave’s achievement of the world’s first and only demonstration of quantum computational supremacy on a useful, real-world problem

Research shows D-Wave annealing quantum computer performs magnetic materials simulation in minutes that would take nearly one million years and more than the world’s annual electricity consumption to solve using a classical supercomputer built with GPU clusters

D-Wave Advantage2 annealing quantum computer prototype used in supremacy achievement, a testament to the system’s remarkable performance capabilities

PALO ALTO, Calif. – March 12, 2025 – D-Wave Quantum Inc. (NYSE: QBTS) (“D-Wave” or the “Company”), a leader in quantum computing systems, software, and services and the world’s first commercial supplier of quantum computers, today announced a scientific breakthrough published in the esteemed journal Science, confirming that its annealing quantum computer outperformed one of the world’s most powerful classical supercomputers in solving complex magnetic materials simulation problems with relevance to materials discovery. The new landmark peer-reviewed paper, Beyond-Classical Computation in Quantum Simulation,” validates this achievement as the world’s first and only demonstration of quantum computational supremacy on a useful problem.

An international collaboration of scientists led by D-Wave performed simulations of quantum dynamics in programmable spin glasses—computationally hard magnetic materials simulation problems with known applications to business and science—on both D-Wave’s Advantage2TM prototype annealing quantum computer and the Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory. The work simulated the behavior of a suite of lattice structures and sizes across a variety of evolution times and delivered a multiplicity of important material properties. D-Wave’s quantum computer performed the most complex simulation in minutes and with a level of accuracy that would take nearly one million years using the supercomputer. In addition, it would require more than the world’s annual electricity consumption to solve this problem using the supercomputer, which is built with graphics processing unit (GPU) clusters.

“This is a remarkable day for quantum computing. Our demonstration of quantum computational supremacy on a useful problem is an industry first. All other claims of quantum systems outperforming classical computers have been disputed or involved random number generation of no practical value,” said Dr. Alan Baratz, CEO of D-Wave. “Our achievement shows, without question, that D-Wave’s annealing quantum computers are now capable of solving useful problems beyond the reach of the world’s most powerful supercomputers. We are thrilled that D-Wave customers can use this technology today to realize tangible value from annealing quantum computers.”

Realizing an Industry-First Quantum Computing Milestone
The behavior of materials is governed by the laws of quantum physics. Understanding the quantum nature of magnetic materials is crucial to finding new ways to use them for technological advancement, making materials simulation and discovery a vital area of research for D-Wave and the broader scientific community. Magnetic materials simulations, like those conducted in this work, use computer models to study how tiny particles not visible to the human eye react to external factors. Magnetic materials are widely used in medical imaging, electronics, superconductors, electrical networks, sensors, and motors.

“This research proves that D-Wave’s quantum computers can reliably solve quantum dynamics problems that could lead to discovery of new materials,” said Dr. Andrew King, senior distinguished scientist at D-Wave. “Through D-Wave’s technology, we can create and manipulate programmable quantum matter in ways that were impossible even a few years ago.”

Materials discovery is a computationally complex, energy-intensive and expensive task. Today’s supercomputers and high-performance computing (HPC) centers, which are built with tens of thousands of GPUs, do not always have the computational processing power to conduct complex materials simulations in a timely or energy-efficient manner. For decades, scientists have aspired to build a quantum computer capable of solving complex materials simulation problems beyond the reach of classical computers. D-Wave’s advancements in quantum hardware have made it possible for its annealing quantum computers to process these types of problems for the first time.

“This is a significant milestone made possible through over 25 years of research and hardware development at D-Wave, two years of collaboration across 11 institutions worldwide, and more than 100,000 GPU and CPU hours of simulation on one of the world’s fastest supercomputers as well as computing clusters in collaborating institutions,” said Dr. Mohammad Amin, chief scientist at D-Wave. “Besides realizing Richard Feynman’s vision of simulating nature on a quantum computer, this research could open new frontiers for scientific discovery and quantum application development.” 

Advantage2 System Demonstrates Powerful Performance Gains
The results shown in “Beyond-Classical Computation in Quantum Simulation” were enabled by D-Wave’s previous scientific milestones published in Nature Physics (2022) and Nature (2023), which theoretically and experimentally showed that quantum annealing provides a quantum speedup in complex optimization problems. These scientific advancements led to the development of the Advantage2 prototype’s fast anneal feature, which played an essential role in performing the precise quantum calculations needed to demonstrate quantum computational supremacy.

“The broader quantum computing research and development community is collectively building an understanding of the types of computations for which quantum computing can overtake classical computing. This effort requires ongoing and rigorous experimentation,” said Dr. Trevor Lanting, chief development officer at D-Wave. “This work is an important step toward sharpening that understanding, with clear evidence of where our quantum computer was able to outperform classical methods. We believe that the ability to recreate the entire suite of results we produced is not possible classically. We encourage our peers in academia to continue efforts to further define the line between quantum and classical capabilities, and we believe these efforts will help drive the development of ever more powerful quantum computing technology.”

The Advantage2 prototype used to achieve quantum computational supremacy is available for customers to use today via D-Wave’s Leap™ real-time quantum cloud service. The prototype provides substantial performance improvements from previous-generation Advantage systems, including increased qubit coherence, connectivity, and energy scale, which enables higher-quality solutions to larger, more complex problems. Moreover, D-Wave now has an Advantage2 processor that is four times larger than the prototype used in this work and has extended the simulations of this paper from hundreds of qubits to thousands of qubits, which are significantly larger than those described in this paper.

Leading Industry Voices Echo Support
Dr. Hidetoshi Nishimori, Professor, Department of Physics, Tokyo Institute of Technology:
“This paper marks a significant milestone in demonstrating the real-world applicability of large-scale quantum computing. Through rigorous benchmarking of quantum annealers against state-of-the-art classical methods, it convincingly establishes a quantum advantage in tackling practical problems, revealing the transformative potential of quantum computing at an unprecedented scale.”

Dr. Seth Lloyd, Professor of Quantum Mechanical Engineering, MIT:
Although large-scale, fully error-corrected quantum computers are years in the future, quantum annealers can probe the features of quantum systems today. In an elegant paper, the D-Wave group has used a large-scale quantum annealer to uncover patterns of entanglement in a complex quantum system that lie far beyond the reach of the most powerful classical computer. The D-Wave result shows the promise of quantum annealers for exploring exotic quantum effects in a wide variety of systems.”

Dr. Travis Humble, Director of Quantum Science Center, Distinguished Scientist at Oak Ridge National Laboratory:
“ORNL seeks to expand the frontiers of computation through many different avenues, and benchmarking quantum computing for materials science applications provides critical input to our understanding of new computational capabilities.”

Dr. Juan Carrasquilla, Associate Professor at the Department of Physics, ETH Zürich:
“I believe these results mark a critical scientific milestone for D-Wave. They also serve as an invitation to the scientific community, as these results offer a strong benchmark and motivation for developing novel simulation techniques for out-of-equilibrium dynamics in quantum many-body physics. Furthermore, I hope these findings encourage theoretical exploration of the computational challenges involved in performing such simulations, both classically and quantum-mechanically.”

Dr. Victor Martin-Mayor, Professor of Theoretical Physics, Universidad Complutense de Madrid:
“This paper is not only a tour-de-force for experimental physics, it is also remarkable for the clarity of the results. The authors have addressed a problem that is regarded both as important and as very challenging to a classical computer. The team has shown that their quantum annealer performs better at this task than the state-of-the-art methods for classical simulation.”

Dr. Alberto Nocera, Senior Staff Scientist, The University of British Columbia:
“Our work shows the impracticability of state-of-the-art classical simulations to simulate the dynamics of quantum magnets, opening the door for quantum technologies based on analog simulators to solve scientific questions that may otherwise remain unanswered using conventional computers.”

About D-Wave Quantum Inc.
D-Wave is a leader in the development and delivery of quantum computing systems, software, and services. We are the world’s first commercial supplier of quantum computers, and the only company building both annealing and gate-model quantum computers. Our mission is to help customers realize the value of quantum, today. Our 5,000+ qubit Advantage™ quantum computers, the world’s largest, are available on-premises or via the cloud, supported by 99.9% availability and uptime. More than 100 organizations trust D-Wave with their toughest computational challenges. With over 200 million problems submitted to our Advantage systems and Advantage2™ prototypes to date, our customers apply our technology to address use cases spanning optimization, artificial intelligence, research and more. Learn more about realizing the value of quantum computing today and how we’re shaping the quantum-driven industrial and societal advancements of tomorrow: www.dwavequantum.com.

Forward-Looking Statements
Certain statements in this press release are forward-looking, as defined in the Private Securities Litigation Reform Act of 1995. These statements involve risks, uncertainties, and other factors that may cause actual results to differ materially from the information expressed or implied by these forward-looking statements and may not be indicative of future results. These forward-looking statements are subject to a number of risks and uncertainties, including, among others, various factors beyond management’s control, including the risks set forth under the heading “Risk Factors” discussed under the caption “Item 1A. Risk Factors” in Part I of our most recent Annual Report on Form 10-K or any updates discussed under the caption “Item 1A. Risk Factors” in Part II of our Quarterly Reports on Form 10-Q and in our other filings with the SEC. Undue reliance should not be placed on the forward-looking statements in this press release in making an investment decision, which are based on information available to us on the date hereof. We undertake no duty to update this information unless required by law.

Here’s a link to and a citation for the most recent paper,

Beyond-classical computation in quantum simulation by Andrew D. King , Alberto Nocera, Marek M. Rams, Jacek Dziarmaga, Roeland Wiersema, William Bernoudy, Jack Raymond, Nitin Kaushal, Niclas Heinsdorf, Richard Harris, Kelly Boothby, Fabio Altomare, Mohsen Asad, Andrew J. Berkley, Martin Boschnak, Kevin Chern, Holly Christiani, Samantha Cibere, Jake Connor, Martin H. Dehn, Rahul Deshpande, Sara Ejtemaee, Pau Farre, Kelsey Hamer, Emile Hoskinson, Shuiyuan Huang, Mark W. Johnson, Samuel Kortas, Eric Ladizinsky, Trevor Lanting, Tony Lai, Ryan Li, Allison J. R. MacDonald, Gaelen Marsden, Catherine C. McGeoch, Reza Molavi, Travis Oh, Richard Neufeld, Mana Norouzpour, Joel Pasvolsky, Patrick Poitras, Gabriel Poulin-Lamarre, Thomas Prescott, Mauricio Reis, Chris Rich, Mohammad Samani, Benjamin Sheldan, Anatoly Smirnov, Edward Sterpka, Berta Trullas Clavera, Nicholas Tsai, Mark Volkmann, Alexander M. Whiticar, Jed D. Whittaker, Warren Wilkinson, Jason Yao, T.J. Yi, Anders W. Sandvik, Gonzalo Alvarez, Roger G. Melko, Juan Carrasquilla, Marcel Franz, and Mohammad H. Amin. Science 12 Mar 2025 First Release DOI: 10.1126/science.ado6285

This paper appears to be open access.Note: I usually tag all of the authors but not this time either.

A controversy of sorts

Madison McLauchlan’s March 19, 2025 article for Betakit (website for Canadian Startup News & Tech Innovation), Note: Links have been removed,

Canadian-born company D-Wave Quantum Systems said it achieved “quantum supremacy” last week after publishing what it calls a groundbreaking paper in the prestigious journal Science. Despite the lofty term, Canadian experts say supremacy is not the be-all, end-all of quantum innovation. 

D-Wave, which has labs in Palo Alto, Calif., and Burnaby, BC, claimed in a statement that it has shown “the world’s first and only demonstration of quantum computational supremacy on a useful, real-world problem.”

Coined in the early 2010s by physicist John Preskill, quantum supremacy is the ability of a quantum computing system to solve a problem no classical computer can in a feasible amount of time. The metric makes no mention of whether the problem needs to be useful or relevant to real life. Google researchers published a paper in Nature in 2019 claiming they cleared that bar with the Sycamore quantum processor. Researchers at the University of Science and Technology in China claimed they demonstrated quantum supremacy several times. 

D-Wave’s attempt differs in that its researchers aimed to solve a real-world materials-simulation problem with quantum computing—one the company claims would be nearly impossible for a traditional computer to solve in a reasonable amount of time. D-Wave used an annealing designed to solve optimization problems. The problem is represented like an energy space, where the “lowest energy state” corresponds to the solution. 

While exciting, quantum supremacy is just one metric among several that mark the progress toward widely useful quantum computers, industry experts told BetaKit. 

“It is a very important and mostly academic metric, but certainly not the most important in the grand scheme of things, as it doesn’t take into account the usefulness of the algorithm,” said Martin Laforest, managing partner at Quantacet, a specialized venture capital fund for quantum startups. 

He added that Google and Xanadu’s [Xanadu Quantum Technologies based in Toronto, Canada] past claims to quantum supremacy were “extraordinary pieces of work, but didn’t unlock practicality.” 

Laforest, along with executives at Canadian quantum startups Nord Quantique and Photonic, say that the milestones of ‘quantum utility’ or ‘quantum advantage’ may be more important than supremacy. 

According to Quantum computing company Quera [QuEra?], quantum advantage is the demonstration of a quantum algorithm solving a real-world problem on a quantum computer faster than any classical algorithm running on any classical computer. On the other hand, quantum utility, according to IBM, refers to when a quantum computer is able to perform reliable computations at a scale beyond brute-force classical computing methods that provide exact solutions to computational problems. 

Error correction hasn’t traditionally been considered a requirement for quantum supremacy, but Laforest told BetaKit the term is “an ever-moving target, constantly challenged by advances in classical algorithms.” He added: “In my opinion, some level of supremacy or utility may be possible in niche areas without error correction, but true disruption requires it.”

Paul Terry, CEO of Vancouver-based Photonic, thinks that though D-Wave’s claim to quantum supremacy shows “continued progress to real value,” scalability is the industry’s biggest hurdle to overcome.

But as with many milestone claims in the quantum space, D-Wave’s latest innovation has been met with scrutiny from industry competitors and researchers on the breakthrough’s significance, claiming that classical computers have achieved similar results. Laforest echoed this sentiment.

“Personally, I wouldn’t say it’s an unequivocal demonstration of supremacy, but it is a damn nice experiment that once again shows the murky zone between traditional computing and early quantum advantage,” Laforest said.

Originally founded out of the University of British Columbia, D-Wave went public on the New York Stock Exchange just over two years ago through a merger with a special-purpose acquisition company in 2022. D-Wave became a Delaware-domiciled corporation as part of the deal.

Earlier this year, D-Wave’s stock price dropped after Nvidia CEO Jensen Huang publicly stated that he estimated that useful quantum computers were more than 15 years away. D-Wave’s stock price, which had been struggling, has seen a considerable bump in recent months alongside a broader boost in the quantum market. The price popped after its most recent earnings, shared right after its quantum supremacy announcement. 

The beat goes on

Some of this is standard in science. There’s always a debate over big claims and it’s not unusual for people to get over excited and have to make a retraction. Scientists are people too. That said, there’s a lot of money on the line and that appears to be making situation even more volatile than usual.

That last paragraph was completed on the morning of March 21, 2025 and later that afternoon I came across this March 21, 2025 article by Michael Grothaus for Fast Company, Note: Links have been removed,

Quantum computing stocks got pummeled yesterday, with the four most prominent public quantum computing companies—IonQ, Rigetti Computing, Quantum Computing Inc., and D-Wave Quantum Inc.—falling anywhere from over 9% to over 18%. The reason? A lot of it may have to do with AI chip giant Nvidia. Again.

Stocks crash yesterday on Nvidia quantum news

Yesterday was a bit of a bloodbath on the stock market for the four most prominent publicly traded quantum computing companies. …

All four of these quantum computing stocks [IonQ, Inc.; Rigetti Computing, Inc.; Quantum Computing Inc.; D-Wave Quantum Inc.] tumbled on the day that AI chip giant Nvidia kicked off its two-day Quantum Day event. In a blog post from January 14 announcing Quantum Day, Nvidia said the event “brings together leading experts for a comprehensive and balanced perspective on what businesses should expect from quantum computing in the coming decades — mapping the path toward useful quantum applications.”

Besides bringing quantum experts together, the AI behemoth also announced that it will be launching a new quantum computing research center in Boston.

Called the NVIDIA Accelerated Quantum Research Center (NVAQC), the new research lab “will help solve quantum computing’s most challenging problems, ranging from qubit noise to transforming experimental quantum processors into practical devices,” the company said in a press release.

The NVAQC’s location in Boston means it will be near both Harvard University and the Massachusetts Institute of Technology (MIT). 

Before Nvidia’s announcement yesterday, IonQ, Rigetti, D-Wave, and Quantum Computing Inc. were the leaders in the nascent field of quantum computing. And while they still are right now (Nvidia’s quantum research lab hasn’t been built yet), the fear is that Nvidia could use its deep pockets to quickly buy its way into a leadership spot in the field. With its $2.9 trillion market cap, the company can easily afford to throw billions of research dollars into quantum computing.

As noted by the Motley Fool, the location of the NVIDIA Accelerated Quantum Research Center in Boston will also allow Nvidia to more easily tap into top quantum talent from Harvard and MIT—talent that may have otherwise gone to IonQ, Rigetti, D-Wave, and Quantum Computing Inc.

Nvidia’s announcement is a massive about-face from the company in regard to how it views quantum computing. It’s also the second time that Nvidia has caused quantum stocks to crash this year. Back in January, shares in prominent quantum computing companies fell after Huang said that practical use of quantum computing was decades away.

Those comments were something quantum computing company CEOs like D-Wave’s Alan Baratz took issue with. “It’s an egregious error on Mr. Huang’s part,” Bartaz told Fast Company at the time. “We’re not decades away from commercial quantum computers. They exist. There are companies that are using our quantum computer today.”

According to Investor’s Business Daily, Huang reportedly got the idea for Nvidia’s Quantum Day event after the blowback to his comments, inviting quantum computing executives to the event to explain why he was incorrect about quantum computing.

The word is volatile.

Machine learning software and quantum computers that think

A Sept. 14, 2017 news item on phys.org sets the stage for quantum machine learning by explaining a few basics first,

Language acquisition in young children is apparently connected with their ability to detect patterns. In their learning process, they search for patterns in the data set that help them identify and optimize grammar structures in order to properly acquire the language. Likewise, online translators use algorithms through machine learning techniques to optimize their translation engines to produce well-rounded and understandable outcomes. Even though many translations did not make much sense at all at the beginning, in these past years we have been able to see major improvements thanks to machine learning.

Machine learning techniques use mathematical algorithms and tools to search for patterns in data. These techniques have become powerful tools for many different applications, which can range from biomedical uses such as in cancer reconnaissance, in genetics and genomics, in autism monitoring and diagnosis and even plastic surgery, to pure applied physics, for studying the nature of materials, matter or even complex quantum systems.

Capable of adapting and changing when exposed to a new set of data, machine learning can identify patterns, often outperforming humans in accuracy. Although machine learning is a powerful tool, certain application domains remain out of reach due to complexity or other aspects that rule out the use of the predictions that learning algorithms provide.

Thus, in recent years, quantum machine learning has become a matter of interest because of is vast potential as a possible solution to these unresolvable challenges and quantum computers show to be the right tool for its solution.

A Sept. 14, 2017 Institute of Photonic Sciences ([Catalan] Institut de Ciències Fotòniques] ICFO) press release, which originated the news item, goes on to detail a recently published overview of the state of quantum machine learning,

In a recent study, published in Nature, an international team of researchers integrated by Jacob Biamonte from Skoltech/IQC, Peter Wittek from ICFO, Nicola Pancotti from MPQ, Patrick Rebentrost from MIT, Nathan Wiebe from Microsoft Research, and Seth Lloyd from MIT have reviewed the actual status of classical machine learning and quantum machine learning. In their review, they have thoroughly addressed different scenarios dealing with classical and quantum machine learning. In their study, they have considered different possible combinations: the conventional method of using classical machine learning to analyse classical data, using quantum machine learning to analyse both classical and quantum data, and finally, using classical machine learning to analyse quantum data.

Firstly, they set out to give an in-depth view of the status of current supervised and unsupervised learning protocols in classical machine learning by stating all applied methods. They introduce quantum machine learning and provide an extensive approach on how this technique could be used to analyse both classical and quantum data, emphasizing that quantum machines could accelerate processing timescales thanks to the use of quantum annealers and universal quantum computers. Quantum annealing technology has better scalability, but more limited use cases. For instance, the latest iteration of D-Wave’s [emphasis mine] superconducting chip integrates two thousand qubits, and it is used for solving certain hard optimization problems and for efficient sampling. On the other hand, universal (also called gate-based) quantum computers are harder to scale up, but they are able to perform arbitrary unitary operations on qubits by sequences of quantum logic gates. This resembles how digital computers can perform arbitrary logical operations on classical bits.

However, they address the fact that controlling a quantum system is very complex and analyzing classical data with quantum resources is not as straightforward as one may think, mainly due to the challenge of building quantum interface devices that allow classical information to be encoded into a quantum mechanical form. Difficulties, such as the “input” or “output” problems appear to be the major technical challenge that needs to be overcome.

The ultimate goal is to find the most optimized method that is able to read, comprehend and obtain the best outcomes of a data set, be it classical or quantum. Quantum machine learning is definitely aimed at revolutionizing the field of computer sciences, not only because it will be able to control quantum computers, speed up the information processing rates far beyond current classical velocities, but also because it is capable of carrying out innovative functions, such quantum deep learning, that could not only recognize counter-intuitive patterns in data, invisible to both classical machine learning and to the human eye, but also reproduce them.

As Peter Wittek [emphasis mine] finally states, “Writing this paper was quite a challenge: we had a committee of six co-authors with different ideas about what the field is, where it is now, and where it is going. We rewrote the paper from scratch three times. The final version could not have been completed without the dedication of our editor, to whom we are indebted.”

It was a bit of a surprise to see local (Vancouver, Canada) company D-Wave Systems mentioned but i notice that one of the paper’s authors (Peter Wittek) is mentioned in a May 22, 2017 D-Wave news release announcing a new partnership to foster quantum machine learning,

Today [May 22, 2017] D-Wave Systems Inc., the leader in quantum computing systems and software, announced a new initiative with the Creative Destruction Lab (CDL) at the University of Toronto’s Rotman School of Management. D-Wave will work with CDL, as a CDL Partner, to create a new track to foster startups focused on quantum machine learning. The new track will complement CDL’s successful existing track in machine learning. Applicants selected for the intensive one-year program will go through an introductory boot camp led by Dr. Peter Wittek [emphasis mine], author of Quantum Machine Learning: What Quantum Computing means to Data Mining, with instruction and technical support from D-Wave experts, access to a D-Wave 2000Q™ quantum computer, and the opportunity to use a D-Wave sampling service to enable machine learning computations and applications. D-Wave staff will be a part of the committee selecting up to 40 individuals for the program, which begins in September 2017.

For anyone interested in the paper, here’s a link to and a citation,

Quantum machine learning by Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, & Seth Lloyd. Nature 549, 195–202 (14 September 2017) doi:10.1038/nature23474 Published online 13 September 2017

This paper is behind a paywall.

Unbreakable encrypted message with key that’s shorter than the message

A Sept. 5, 2016 University of Rochester (NY state, US) news release (also on EurekAlert), makes an intriguing announcement,

Researchers at the University of Rochester have moved beyond the theoretical in demonstrating that an unbreakable encrypted message can be sent with a key that’s far shorter than the message—the first time that has ever been done.

Until now, unbreakable encrypted messages were transmitted via a system envisioned by American mathematician Claude Shannon, considered the “father of information theory.” Shannon combined his knowledge of algebra and electrical circuitry to come up with a binary system of transmitting messages that are secure, under three conditions: the key is random, used only once, and is at least as long as the message itself.

The findings by Daniel Lum, a graduate student in physics, and John Howell, a professor of physics, have been published in the journal Physical Review A.

“Daniel’s research amounts to an important step forward, not just for encryption, but for the field of quantum data locking,” said Howell.

Quantum data locking is a method of encryption advanced by Seth Lloyd, a professor of quantum information at Massachusetts Institute of Technology, that uses photons—the smallest particles associated with light—to carry a message. Quantum data locking was thought to have limitations for securely encrypting messages, but Lloyd figured out how to make additional assumptions—namely those involving the boundary between light and matter—to make it a more secure method of sending data.  While a binary system allows for only an on or off position with each bit of information, photon waves can be altered in many more ways: the angle of tilt can be changed, the wavelength can be made longer or shorter, and the size of the amplitude can be modified. Since a photon has more variables—and there are fundamental uncertainties when it comes to quantum measurements—the quantum key for encrypting and deciphering a message can be shorter that the message itself.

Lloyd’s system remained theoretical until this year, when Lum and his team developed a device—a quantum enigma machine—that would put the theory into practice. The device takes its name from the encryption machine used by Germany during World War II, which employed a coding method that the British and Polish intelligence agencies were secretly able to crack.

Let’s assume that Alice wants to send an encrypted message to Bob. She uses the machine to generate photons that travel through free space and into a spatial light modulator (SLM) that alters the properties of the individual photons (e.g. amplitude, tilt) to properly encode the message into flat but tilted wavefronts that can be focused to unique points dictated by the tilt. But the SLM does one more thing: it distorts the shapes of the photons into random patterns, such that the wavefront is no longer flat which means it no longer has a well-defined focus. Alice and Bob both know the keys which identify the implemented scrambling operations, so Bob is able to use his own SLM to flatten the wavefront, re-focus the photons, and translate the altered properties into the distinct elements of the message.

Along with modifying the shape of the photons, Lum and the team made use of the uncertainty principle, which states that the more we know about one property of a particle, the less we know about another of its properties. Because of that, the researchers were able to securely lock in six bits of classical information using only one bit of an encryption key—an operation called data locking.

“While our device is not 100 percent secure, due to photon loss,” said Lum, “it does show that data locking in message encryption is far more than a theory.”

The ultimate goal of the quantum enigma machine is to prevent a third party—for example, someone named Eve—from intercepting and deciphering the message. A crucial principle of quantum theory is that the mere act of measuring a quantum system changes the system. As a result, Eve has only one shot at obtaining and translating the encrypted message—something that is virtually impossible, given the nearly limitless number of patterns that exist for each photon.

The paper by Lum and Howell was one of two papers published simultaneously on the same topic. The other paper, “Quantum data locking,” was from a team led by Chinese physicist Jian-Wei Pan.

“It’s highly unlikely that our free-space implementation will be useful through atmospheric conditions,” said Lum. “Instead, we have identified the use of optic fiber as a more practical route for data locking, a path Pan’s group actually started with. Regardless, the field is still in its infancy with a great deal more research needed.”

Here’s a link to and a citation for the paper,

Quantum enigma machine: Experimentally demonstrating quantum data locking by Daniel J. Lum, John C. Howell, M. S. Allman, Thomas Gerrits, Varun B. Verma, Sae Woo Nam, Cosmo Lupo, and Seth Lloyd. Phys. Rev. A, Vol. 94, Iss. 2 — August 2016 DOI: http://dx.doi.org/10.1103/PhysRevA.94.022315

©2016 American Physical Society

This paper is behind a paywall.

There is an earlier open access version of the paper by the Chinese researchers on arXiv.org,

Experimental quantum data locking by Yang Liu, Zhu Cao, Cheng Wu, Daiji Fukuda, Lixing You, Jiaqiang Zhong, Takayuki Numata, Sijing Chen, Weijun Zhang, Sheng-Cai Shi, Chao-Yang Lu, Zhen Wang, Xiongfeng Ma, Jingyun Fan, Qiang Zhang, Jian-Wei Pan. arXiv.org > quant-ph > arXiv:1605.04030

The Chinese team’s later version of the paper is available here,

Experimental quantum data locking by Yang Liu, Zhu Cao, Cheng Wu, Daiji Fukuda, Lixing You, Jiaqiang Zhong, Takayuki Numata, Sijing Chen, Weijun Zhang, Sheng-Cai Shi, Chao-Yang Lu, Zhen Wang, Xiongfeng Ma, Jingyun Fan, Qiang Zhang, and Jian-Wei Pan. Phys. Rev. A, Vol. 94, Iss. 2 — August 2016 DOI: http://dx.doi.org/10.1103/PhysRevA.94.020301

©2016 American Physical Society

This version is behind a paywall.

Getting back to the folks at the University of Rochester, they have provided this image to illustrate their work,

The quantum enigma machine developed by researchers at the University of Rochester, MIT, and the National Institute of Standards and Technology. (Image by Daniel Lum/University of Rochester)

The quantum enigma machine developed by researchers at the University of Rochester, MIT, and the National Institute of Standards and Technology. (Image by Daniel Lum/University of Rochester)

Handling massive digital datasets the quantum way

A Jan. 25, 2016 news item on phys.org describes a new approach to analyzing and managing huge datasets,

From gene mapping to space exploration, humanity continues to generate ever-larger sets of data—far more information than people can actually process, manage, or understand.

Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.

Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at [Massachusetts Institute of Technology] MIT, the University of Waterloo, and the University of Southern California [USC}.

A Jan. 25, 2016 MIT news release (*also on EurekAlert*), which originated the news item, describes the theory in more detail,

… Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.

In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd [ explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”

It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”

Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.

Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.

“That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.

“Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.

There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.

The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.

While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”

Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”

Here’s a link to and a citation for the paper,

Quantum algorithms for topological and geometric analysis of data by Seth Lloyd, Silvano Garnerone, & Paolo Zanardi. Nature Communications 7, Article number: 10138 doi:10.1038/ncomms10138 Published 25 January 2016

This paper is open access.

ETA Jan. 25, 2016 1245 hours PST,

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

*’also on EurekAlert’ text and link added Jan. 26, 2016.

100 percent efficiency transporting the energy of sunlight from receptors to reaction centers

Genetic engineering has been combined with elements of quantum physics to find a better way of transferring the energy derived from sunlight from the receptors to the reaction centers (i.e., photosynthesis). From an Oct. 15, 2015 news item on Nanowerk,

Nature has had billions of years to perfect photosynthesis, which directly or indirectly supports virtually all life on Earth. In that time, the process has achieved almost 100 percent efficiency in transporting the energy of sunlight from receptors to reaction centers where it can be harnessed — a performance vastly better than even the best solar cells.

One way plants achieve this efficiency is by making use of the exotic effects of quantum mechanics — effects sometimes known as “quantum weirdness.” These effects, which include the ability of a particle to exist in more than one place at a time [superposition], have now been used by engineers at MIT to achieve a significant efficiency boost in a light-harvesting system.

Surprisingly, the MIT [Massachusetts Institute of Technology] researchers achieved this new approach to solar energy not with high-tech materials or microchips — but by using genetically engineered viruses.

An Oct. 15, 2015 MIT news release (also on EurekAlert), which originated the news item, recounts an exciting tale of interdisciplinary work and an international collaboration,

This achievement in coupling quantum research and genetic manipulation, described this week in the journal Nature Materials, was the work of MIT professors Angela Belcher, an expert on engineering viruses to carry out energy-related tasks, and Seth Lloyd, an expert on quantum theory and its potential applications; research associate Heechul Park; and 14 collaborators at MIT and in Italy.

Lloyd, a professor of mechanical engineering, explains that in photosynthesis, a photon hits a receptor called a chromophore, which in turn produces an exciton — a quantum particle of energy. This exciton jumps from one chromophore to another until it reaches a reaction center, where that energy is harnessed to build the molecules that support life.

But the hopping pathway is random and inefficient unless it takes advantage of quantum effects that allow it, in effect, to take multiple pathways at once and select the best ones, behaving more like a wave than a particle.

This efficient movement of excitons has one key requirement: The chromophores have to be arranged just right, with exactly the right amount of space between them. This, Lloyd explains, is known as the “Quantum Goldilocks Effect.”

That’s where the virus comes in. By engineering a virus that Belcher has worked with for years, the team was able to get it to bond with multiple synthetic chromophores — or, in this case, organic dyes. The researchers were then able to produce many varieties of the virus, with slightly different spacings between those synthetic chromophores, and select the ones that performed best.

In the end, they were able to more than double excitons’ speed, increasing the distance they traveled before dissipating — a significant improvement in the efficiency of the process.

The project started from a chance meeting at a conference in Italy. Lloyd and Belcher, a professor of biological engineering, were reporting on different projects they had worked on, and began discussing the possibility of a project encompassing their very different expertise. Lloyd, whose work is mostly theoretical, pointed out that the viruses Belcher works with have the right length scales to potentially support quantum effects.

In 2008, Lloyd had published a paper demonstrating that photosynthetic organisms transmit light energy efficiently because of these quantum effects. When he saw Belcher’s report on her work with engineered viruses, he wondered if that might provide a way to artificially induce a similar effect, in an effort to approach nature’s efficiency.

“I had been talking about potential systems you could use to demonstrate this effect, and Angela said, ‘We’re already making those,'” Lloyd recalls. Eventually, after much analysis, “We came up with design principles to redesign how the virus is capturing light, and get it to this quantum regime.”

Within two weeks, Belcher’s team had created their first test version of the engineered virus. Many months of work then went into perfecting the receptors and the spacings.

Once the team engineered the viruses, they were able to use laser spectroscopy and dynamical modeling to watch the light-harvesting process in action, and to demonstrate that the new viruses were indeed making use of quantum coherence to enhance the transport of excitons.

“It was really fun,” Belcher says. “A group of us who spoke different [scientific] languages worked closely together, to both make this class of organisms, and analyze the data. That’s why I’m so excited by this.”

While this initial result is essentially a proof of concept rather than a practical system, it points the way toward an approach that could lead to inexpensive and efficient solar cells or light-driven catalysis, the team says. So far, the engineered viruses collect and transport energy from incoming light, but do not yet harness it to produce power (as in solar cells) or molecules (as in photosynthesis). But this could be done by adding a reaction center, where such processing takes place, to the end of the virus where the excitons end up.

MIT has produced a video explanation of the work,

Here’s a link to and a citation for the paper,

Enhanced energy transport in genetically engineered excitonic networks by Heechul Park, Nimrod Heldman, Patrick Rebentrost, Luigi Abbondanza, Alessandro Iagatti, Andrea Alessi, Barbara Patrizi, Mario Salvalaggio, Laura Bussotti, Masoud Mohseni, Filippo Caruso, Hannah C. Johnsen, Roberto Fusco, Paolo Foggi, Petra F. Scudo, Seth Lloyd, & Angela M. Belcher. Nature Materials (2015) doi:10.1038/nmat4448 Published online 12 October 2015

This paper is behind a paywall.