Vancouver (Canada) area company, D-Wave Systems is trumpeting itself (with good reason) again. This 2021 ‘milestone’ achievement builds on work from 2018 (see my August 23, 2018 posting for the earlier work). For me, the big excitement was finding the best explanation for quantum annealing and D-Wave’s quantum computers that I’ve seen yet (that explanation and a link to more is at the end of this posting).
A February 18, 2021 news item on phys.org announces the latest achievement,
D-Wave Systems Inc. today [February 18, 2021] published a milestone study in collaboration with scientists at Google, demonstrating a computational performance advantage, increasing with both simulation size and problem hardness, to over 3 million times that of corresponding classical methods. Notably, this work was achieved on a practical application with real-world implications, simulating the topological phenomena behind the 2016 Nobel Prize in Physics. This performance advantage, exhibited in a complex quantum simulation of materials, is a meaningful step in the journey toward applications advantage in quantum computing.
The work by scientists at D-Wave and Google also demonstrates that quantum effects can be harnessed to provide a computational advantage in D-Wave processors, at problem scale that requires thousands of qubits. Recent experiments performed on multiple D-Wave processors represent by far the largest quantum simulations carried out by existing quantum computers to date.
The paper, entitled “Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets”, was published in the journal Nature Communications (DOI 10.1038/s41467-021-20901-5, February 18, 2021). D-Wave researchers programmed the D-Wave 2000Q™ system to model a two-dimensional frustrated quantum magnet using artificial spins. The behavior of the magnet was described by the Nobel-prize winning work of theoretical physicists Vadim Berezinskii, J. Michael Kosterlitz and David Thouless. They predicted a new state of matter in the 1970s characterized by nontrivial topological properties. This new research is a continuation of previous breakthrough work published by D-Wave’s team in a 2018 Nature paper entitled “Observation of topological phenomena in a programmable lattice of 1,800 qubits” (Vol. 560, Issue 7719, August 22, 2018). In this latest paper, researchers from D-Wave, alongside contributors from Google, utilize D-Wave’s lower noise processor to achieve superior performance and glean insights into the dynamics of the processor never observed before.
“This work is the clearest evidence yet that quantum effects provide a computational advantage in D-Wave processors,” said Dr. Andrew King, principal investigator for this work at D-Wave. “Tying the magnet up into a topological knot and watching it escape has given us the first detailed look at dynamics that are normally too fast to observe. What we see is a huge benefit in absolute terms, with the scaling advantage in temperature and size that we would hope for. This simulation is a real problem that scientists have already attacked using the algorithms we compared against, marking a significant milestone and an important foundation for future development. This wouldn’t have been possible today without D-Wave’s lower noise processor.”
“The search for quantum advantage in computations is becoming increasingly lively because there are special problems where genuine progress is being made. These problems may appear somewhat contrived even to physicists, but in this paper from a collaboration between D-Wave Systems, Google, and Simon Fraser University [SFU], it appears that there is an advantage for quantum annealing using a special purpose processor over classical simulations for the more ‘practical’ problem of finding the equilibrium state of a particular quantum magnet,” said Prof. Dr. Gabriel Aeppli, professor of physics at ETH Zürich and EPF Lausanne, and head of the Photon Science Division of the Paul Scherrer Institute. “This comes as a surprise given the belief of many that quantum annealing has no intrinsic advantage over path integral Monte Carlo programs implemented on classical processors.”
“Nascent quantum technologies mature into practical tools only when they leave classical counterparts in the dust in solving real-world problems,” said Hidetoshi Nishimori, Professor, Institute of Innovative Research, Tokyo Institute of Technology. “A key step in this direction has been achieved in this paper by providing clear evidence of a scaling advantage of the quantum annealer over an impregnable classical computing competitor in simulating dynamical properties of a complex material. I send sincere applause to the team.”
“Successfully demonstrating such complex phenomena is, on its own, further proof of the programmability and flexibility of D-Wave’s quantum computer,” said D-Wave CEO Alan Baratz. “But perhaps even more important is the fact that this was not demonstrated on a synthetic or ‘trick’ problem. This was achieved on a real problem in physics against an industry-standard tool for simulation–a demonstration of the practical value of the D-Wave processor. We must always be doing two things: furthering the science and increasing the performance of our systems and technologies to help customers develop applications with real-world business value. This kind of scientific breakthrough from our team is in line with that mission and speaks to the emerging value that it’s possible to derive from quantum computing today.”
The scientific achievements presented in Nature Communications further underpin D-Wave’s ongoing work with world-class customers to develop over 250 early quantum computing applications, with a number piloting in production applications, in diverse industries such as manufacturing, logistics, pharmaceutical, life sciences, retail and financial services. In September 2020, D-Wave brought its next-generation Advantage™ quantum system to market via the Leap™ quantum cloud service. The system includes more than 5,000 qubits and 15-way qubit connectivity, as well as an expanded hybrid solver service capable of running business problems with up to one million variables. The combination of Advantage’s computing power and scale with the hybrid solver service gives businesses the ability to run performant, real-world quantum applications for the first time.
That last paragraph seems more sales pitch than research oriented. It’s not unexpected in a company’s press release but I was surprised that the editors at EurekAlert didn’t remove it.
Here’s a link to and a citation for the latest paper,
Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets by Andrew D. King, Jack Raymond, Trevor Lanting, Sergei V. Isakov, Masoud Mohseni, Gabriel Poulin-Lamarre, Sara Ejtemaee, William Bernoudy, Isil Ozfidan, Anatoly Yu. Smirnov, Mauricio Reis, Fabio Altomare, Michael Babcock, Catia Baron, Andrew J. Berkley, Kelly Boothby, Paul I. Bunyk, Holly Christiani, Colin Enderud, Bram Evert, Richard Harris, Emile Hoskinson, Shuiyuan Huang, Kais Jooya, Ali Khodabandelou, Nicolas Ladizinsky, Ryan Li, P. Aaron Lott, Allison J. R. MacDonald, Danica Marsden, Gaelen Marsden, Teresa Medina, Reza Molavi, Richard Neufeld, Mana Norouzpour, Travis Oh, Igor Pavlov, Ilya Perminov, Thomas Prescott, Chris Rich, Yuki Sato, Benjamin Sheldan, George Sterling, Loren J. Swenson, Nicholas Tsai, Mark H. Volkmann, Jed D. Whittaker, Warren Wilkinson, Jason Yao, Hartmut Neven, Jeremy P. Hilton, Eric Ladizinsky, Mark W. Johnson, Mohammad H. Amin. Nature Communications volume 12, Article number: 1113 (2021) DOI: https://doi.org/10.1038/s41467-021-20901-5 Published: 18 February 2021
This paper is open access.
Quantum annealing and more
Dr. Andrew King, one of the D-Wave researchers, has written a February 18, 2021 article on Medium explaining some of the work. I’ve excerpted one of King’s points,
Insight #1: We observed what actually goes on under the hood in the processor for the first time
Quantum annealing — the approach adopted by D-Wave from the beginning — involves setting up a simple but purely quantum initial state, and gradually reducing the “quantumness” until the system is purely classical. This takes on the order of a microsecond. If you do it right, the classical system represents a hard (NP-complete) computational problem, and the state has evolved to an optimal, or at least near-optimal, solution to that problem.
What happens at the beginning and end of the computation are about as simple as quantum computing gets. But the action in the middle is hard to get a handle on, both theoretically and experimentally. That’s one reason these experiments are so important: they provide high-fidelity measurements of the physical processes at the core of quantum annealing. Our 2018 Nature article introduced the same simulation, but without measuring computation time. To benchmark the experiment this time around, we needed lower-noise hardware (in this case, we used the D-Wave 2000Q lower noise quantum computer), and we needed, strangely, to slow the simulation down. Since the quantum simulation happens so fast, we actually had to make things harder. And we had to find a way to slow down both quantum and classical simulation in an equitable way. The solution? Topological obstruction.
If you have time and the inclination, I encourage you to read King’s piece.