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D-Wave Deep Dive: A Look at The Quantum Advantage Findings — And The Questions That Remain
Matt Swayne
March 13, 2025
D-Wave researchers reported that superconducting quantum annealers have demonstrated superior accuracy and efficiency over leading classical methods in solving specific quantum dynamics problems, suggesting practical quantum advantage beyond random circuit sampling.
The study compared quantum annealers against classical techniques such as matrix product states (MPS), projected entangled-pair states (PEPS), and neural quantum states (NQS), finding that classical methods struggle with scalability but remain competitive in certain problem instances.
Critics argue that properly configured classical simulations can reproduce or even surpass quantum annealer performance in some cases, with competing research from EPFL and the Flatiron Institute showing that time-dependent variational Monte Carlo (t-VMC) and belief propagation offer alternative paths to solving these problems efficiently.
D-Wave researchers reported that superconducting quantum annealers have demonstrated the ability to solve certain problems with greater accuracy and efficiency than leading classical methods, according to their study published in Science. The findings provide evidence that quantum processors can outperform classical computers in practical applications beyond tasks like boson sampling and random circuit sampling, tasks that have been used in past claims of quantum advantage.
However, some scientists aren’t sure the results are an unambiguous display of quantum supremacy, citing research that suggests properly configured classical approaches can compete with quantum in similar tasks.
Breaking It Down
In D-Wave’s paper, researchers showed that quantum annealers can rapidly generate samples that closely match solutions derived from the Schrödinger equation. This equation is the fundamental equation of quantum mechanics, describing how the quantum state of a system evolves over time.
The study examined the performance of superconducting quantum annealing processors when simulating quantum dynamics in spin glass models, a class of problems relevant to materials science, condensed matter physics and artificial intelligence. These systems are known for their complex energy landscapes, making them difficult for classical computers to simulate efficiently.
The results suggest that quantum annealers provide a computational advantage over tensor network and neural network-based classical simulation techniques in certain tasks. Unlike previous demonstrations of quantum supremacy, which focused on tasks like generating random numbers in ways that are difficult for classical systems to replicate, this study targets a problem with practical significance. The team’s findings offer evidence that quantum annealers can model real-world quantum systems more effectively than classical approximation techniques.
“The ability of superconducting QA processors to solve this simulation problem with high accuracy has been established for 1D chains and small spin glasses,” the authors write. “Here we simulate large programmable topologies of varying dimension.”
By demonstrating that quantum annealers can solve these complex problems with greater efficiency than classical alternatives, the study could strengthen the case for their utility in scientific and industrial applications. This includes fields such as drug discovery, materials science and machine learning, where quantum systems play a fundamental role.
Methodology
The researchers studied how quantum annealers performed when solving the transverse-field Ising model (TFIM), a widely used model in quantum computing and statistical mechanics. The simulations involved quenching — rapidly changing — an external field and observing how a quantum system evolves over time. The study tested two superconducting quantum processors: the Advantage system (ADV1) and a prototype of the next-generation Advantage2 system (ADV2).
To evaluate the accuracy of the quantum annealers, researchers compared their results with those produced by matrix product state (MPS) simulations run on the Summit and Frontier supercomputers at Oak Ridge National Laboratory. MPS techniques represent one of the best classical methods for approximating quantum dynamics but become increasingly expensive in terms of time and memory as system size increases.
The team writes: “On the largest problems, MPS would take millions of years on the Frontier supercomputer per input to match QPU quality. Memory requirements would exceed its storage, and electricity requirements would exceed annual global consumption. We emphasize that this scaling analysis applies to MPS — the only method with which we can match QPU quality for all considered quench times — and is not an intrinsic lower bound for all classical methods.”
The study also examined alternative classical approaches using projected entangled-pair states (PEPS) and neural quantum state (NQS) simulations, but these methods failed to match the accuracy of quantum annealers within feasible computational limits.
The quantum annealers generated at least 1,000 samples per second, allowing researchers to compare their output with classical methods over a range of different system sizes. The study also analyzed entanglement entropy, a key measure of quantum correlations, showing that the quantum processors exhibited an area-law scaling that classical methods struggled to replicate.
By analyzing spin-spin correlations and other statistical measures, the researchers confirmed that the quantum annealers provided results consistent with quantum mechanical predictions, even in regimes where classical methods struggled.
Limitations and Questions
Like other supremacy claims, D-Wave’s study is attracting scrutiny.
Science News points out that a preliminary draft of D-Wave’s study was posted on arXiv.org a year ago, allowing other researchers to examine the findings. Joseph Tindall, a quantum computer scientist at the Flatiron Institute in New York City, and colleagues developed a competing classical approach using belief propagation, an algorithm widely used in artificial intelligence, according to Science News. Their method, which repurposes a 40-year-old technique, produced more accurate results than the quantum annealer in certain cases involving two- and three-dimensional systems. Their findings, submitted to arXiv.org on March 7 but not yet peer-reviewed, challenge some of the conclusions of the quantum study.
“For the … spin glass problem at hand, our classical approach demonstrably outperforms other reported methods,” Tindall and his colleagues write, Science News reports. “In [two cases] we are also able to reach errors noticeably lower than the quantum annealing approach employed by the D-Wave Advantage2 system.”
However, the classical simulations focused on only a subset of the problems tested in the study, and there is ongoing debate over whether their method can match the quantum annealer across all scenarios. The researchers disagree on whether the classical technique can fully reproduce the quantum computer’s results, particularly for more complex three-dimensional systems.
While classical methods require fundamentally different approaches for infinite-dimensional systems, the study suggests that quantum annealers can efficiently generate samples in this context, an area where classical techniques remain underexplored., Science News article points out.
Monte Carlo Techniques
In another test of D-Wave’s claims, researchers at EPFL’s Institute of Physics and Center for Quantum Science and Engineering have pushed the boundaries of quantum advantage by demonstrating that large-scale classical simulations can match or even challenge the performance of quantum annealers. The study posted on the pre-print server arXiv, led by Linda Mauron and Giuseppe Carleo, employs time-dependent variational Monte Carlo (t-VMC) techniques to simulate the quantum annealing of spin glass models.
The findings question the assumption that quantum processors have an insurmountable edge in annealing-based simulations. Using a Jastrow-Feenberg wave function, the researchers efficiently modeled systems up to 128 spins on a three-dimensional diamond lattice, a scale previously deemed beyond classical feasibility. Their method achieves correlation errors below 7%, aligning with or surpassing the accuracy of D-Wave’s quantum annealers while requiring only polynomial computational resources — in contrast to tensor network methods, which scale exponentially, the researchers suggest.
It would seem, further, that the results would weaken the argument that infinite-dimensional systems inherently favor quantum annealers.
Other Limitations
The study acknowledges other limitations that point toward future work. The quantum annealers tested were not error-free and required careful calibration. Additionally, verifying quantum advantage in a meaningful way requires comparisons to the best available classical methods, which in turn demands significant computational resources.
For small-scale problems, classical methods can still reproduce quantum results with sufficient accuracy. However, as system sizes grow, classical simulations become exponentially more expensive. The study estimates that achieving the same accuracy as the quantum annealers on large problems using MPS methods would take millions of years on the Frontier supercomputer, at least for the specific problem instances studied. The memory requirements would exceed its entire storage capacity, and the energy consumption would surpass global annual electricity use.
The findings seem to show a fundamental difference between quantum and classical approaches. While classical methods rely on approximations and truncation techniques to manage computational complexity, quantum processors naturally encode and evolve the full quantum state. This allows quantum annealers to maintain accuracy even as system size and complexity increase.
However, quantum annealing is not a universal quantum computing technique. Unlike gate-based quantum computers, which can theoretically perform arbitrary computations, quantum annealers are specialized for optimization and sampling tasks. Their advantage is most pronounced in problems that involve simulating many-body quantum interactions, where classical methods scale poorly.
Future Directions
The study’s results suggest that quantum annealers could be applied to real-world scientific problems that are currently intractable for classical computers. Potential applications include optimizing complex networks, modeling new materials, and improving artificial intelligence algorithms.
Beyond direct applications, the findings could also influence the development of new classical algorithms. The authors note that their work could inspire classical simulation techniques that better approximate quantum behavior, advancing the broader field of computational physics. Improvements in hybrid quantum-classical algorithms may enable more efficient solutions for practical applications in the near term.
Future research will likely focus on extending these findings to larger and more diverse problem sets, improving quantum hardware stability, and refining methods for error correction. The researchers also emphasize the need for additional benchmarking studies to further validate quantum advantage across different types of problems.
The impracticability of classical simulation opens the door to quantum advantage in optimization and AI, addressing scientific questions that may otherwise remain unanswered, the study concludes.
As quantum hardware continues to improve, demonstrations like this may move quantum computing beyond theoretical promise and into practical utility for solving real-world problems. The next step will be scaling quantum annealers to larger qubit counts and refining control techniques to maximize their computational power. If successful, these advances could mark a turning point in the race to harness quantum computing for practical applications.
Broader View
One of the hot-button terms in the quantum industry is the idea of a “quantum horse race.” Many protest the term — usually leveled at the variety of competing modalities — as an unfair and inaccurate analogy.
But, the truth may be that the industry doesn’t really have a horse race. It has an entire Triple Crown horse race season. Quantum companies must show advantage in their own modalities. They even are entered in a race to prove not just quantum advantage, not just quantum supremacy, but, now, quantum advantage in real-world tasks.
Finally, a not-often mentioned long shot is in the race: classical computing innovation. Quantum companies must contend with the ever-advancing classical techniques to prove quantum advantage.
What this all means is that — and excuse yet another sports analogy — the finish line in the quantum horse race is constantly moving.
Institutions
The research involved multiple institutions spanning Canada, the United States and Switzerland. The majority of contributors are affiliated with D-Wave Quantum Inc. Additional institutional affiliations include the Department of Physics at Boston University in the United States, as well as the Computational Sciences and Engineering Division at Oak Ridge National Laboratory in Tennessee. In Canada, researchers are associated with the Department of Physics and Astronomy at the University of Waterloo, the Perimeter Institute for Theoretical Physics, and the Vector Institute in Toronto. Further international contributions come from the Institute for Theoretical Physics at ETH Zürich in Switzerland. The Department of Physics and Astronomy and Quantum Matter Institute at the University of British Columbia also played a role, along with Simon Fraser University’s Department of Physics in Burnaby, British Columbia.
https://thequantuminsider.com/2025/03/13/d-wave-deep-dive-a-look-at-the-quantum-advantage-findings-and-the-questions-that-remain/?utm_source=resonance-newsletters.beehiiv.com&utm_medium=newsletter&utm_campaign=the-quantum-insider-weekly-d-wave-makes-waves-nist-s-back-up-algorithm-and-more-news&_bhlid=af32a4c05dbe1c9ffd6dbe500ba8ca7a24ce18a3
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