Scientific Publications

Rahul Deshpande, Majid Kheirkhah, Chris Rich, Richard Harris, Jack Raymond, Emile Hoskinson, Pratik Sathe, Andrew J. Berkley, Stefan Paul, Brian Barch, Daniel A. Lidar, Markus Müller, Gabriel Aeppli, Andrew D. King, Mohammad H. Amin

https://arxiv.org/abs/2603.15534

Quantum annealing processors typically control qubits in unison, attenuating quantum fluctuations uniformly until the applied system Hamiltonian is diagonal in the computational basis. This simplifies control requirements, allowing annealing QPUs to scale to much larger sizes than gate-based systems, but constraining the class of available operations. Here we expand the class by performing analog-digital quantum computing in a highly-multiplexed, superconducting quantum annealing processor. This involves evolution under a fixed many-body Hamiltonian that, in the weak-coupling regime, is well-described by an effective XY model, together with arbitrary-basis initialization and measurement via auxiliary qubits. Operationally, this is equivalent to implementing single-qubit gates at the beginning and end of an analog quantum evolution. We demonstrate this capability with several foundational applications: single-qubit and two-qubit coherent oscillations with varying initialization and measurement bases, a multi-qubit quantum walk with fermionic dispersion in line with theory, and Anderson localization in a disordered chain. These experiments open the door to a wide range of new possibilities in quantum computation and simulation, greatly expanding the applications of commercially available quantum annealing processors.

Elijah Pelofske, Frank Barrows, Pratik Sathe, and Cristiano Nisoli

https://www.science.org/doi/10.1126/sciadv.aeb5192

While quantum annealers have emerged as versatile and controllable platforms for experimenting on correlated spin systems, the important phenomenology of magnetic memory and hysteresis remain unexplored on hardware designed to escape metastable states via quantum tunneling. Here, we present the first general protocol to experiment on magnetic hysteresis on programmable quantum annealers and implement it on three D-Wave superconducting qubit quantum annealers, using up to thousands of spins, for both ferromagnetic and disordered Ising models, and across different graph topologies. We observe hysteresis loops whose area depends nonmonotonically on quantum fluctuations, exhibiting both expected and unexpected features, such as disorder-induced steps and nonmonotonicities. Our work establishes quantum annealers as a platform for probing nonequilibrium emergent magnetic phenomena, thereby broadening the role of analog quantum computers into foundational questions in condensed matter physics.

Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama, Yutaro Hirono, Akira Suma, Matthew Woolway, Vladimir Vargas-Calderón, William Kim, Kevin Chern, Mohammad Amin, Masaru Tateno

https://arxiv.org/abs/2602.15451

Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To resolve this problem, we developed a novel framework for optimization of deep generative models integrated with a D-Wave quantum annealing computer, where our Neural Hash Function (NHF) presented herein is used both as the regularization and binarization schemes simultaneously, of which the latter is for transformation between continuous and discrete signals of the classical and quantum neural networks, respectively, in the error evaluation (i.e., objective) function. The compounds generated via the quantum-annealing generative models exhibited higher quality in both validity and drug-likeness than those generated via the fully-classical models, and was further indicated to exceed even the training data in terms of drug-likeness features, without any restraints and conditions to deliberately induce such an optimization. These results indicated an advantage of quantum annealing to aim at a stochastic generator integrated with our novel neural network architectures, for the extended performance of feature space sampling and extraction of characteristic features in drug design.

https://www.dwavequantum.com/media/41upubz2/14-1090a-a_fluxonium-dac-control.pdf

Any truly scalable gate-model quantum computing (GMQC) architecture needs to address the issue of control. Specifically, the number of control lines needed to operate a given quantum processing unit (QPU) must scale slowly with total device count within that QPU. Most proposed superconducting GMQC architectures to date have invoked a bruteforce scaling approach to increase QPU size, wherein that line count increases linearly with device count. However, this approach is nearing practical limitations around the scale of 100 physical qubits, which is far below the scale required for building commercially relevant QPUs. In contrast, quantum annealing (QA) QPUs exist today that have been designed with scalability built-in from the outset. This whitepaper describes how D-Wave Quantum Inc. (D-Wave) scalable control technology, as embodied in modern QA QPUs, can be adapted to find utility in the context of superconducting GMQC.

Pratik Sathe, Andrew D. King, Susan M. Mniszewski, Carleton Coffrin, Cristiano Nisoli & Francesco Caravelli

https://www.nature.com/articles/s41467-025-67568-w

Quantum annealing provides a powerful platform for simulating magnetic materials and realizing statistical physics models, presenting a compelling alternative to classical Monte Carlo methods. We demonstrate that quantum annealers can accurately reproduce phase diagrams and simulate critical phenomena without suffering from the critical slowing down that often affects classical algorithms. To illustrate this, we study the piled-up dominoes model, which interpolates between the ferromagnetic 2D Ising model and Villain’s fully frustrated “odd model”. We map out its phase diagram and employ finite-size scaling and Binder cumulants on a quantum annealer to study critical exponents for thermal phase transitions. Our method achieves systematic temperature control by tuning the energy scale of the Hamiltonian, eliminating the need to adjust the physical temperature of the quantum hardware. This work demonstrates how, through fine-tuning and calibration, a quantum annealer can be employed to apply sophisticated finite-size scaling techniques from statistical mechanics. Our results establish quantum annealers as robust statistical physics simulators, offering a novel pathway for studying phase transitions and critical behavior.

Andrew D. King

https://arxiv.org/abs/2511.01762

An important task in multi-objective optimization is generating the Pareto front—the set of all Pareto-optimal compromises among multiple objective functions applied to the same set of variables. Since this task can be computationally intensive even for small problems, it is a natural target for quantum optimization. Indeed, this problem was recently approached using the quantum approximate optimization algorithm (QAOA) on an IBM gate-model processor. Here we compare these QAOA results with quantum annealing on the same two input problems, using the same methodology. We find that quantum annealing vastly outperforms not just QAOA run on the IBM processor, but all classical and quantum methods analyzed in the previous study. On the harder problem, quantum annealing improves upon the best known Pareto front. This small study reinforces the promise of quantum annealing in multi-objective optimization.

Alejandro Lopez-Bezanilla, William Bernoudy, Kelly Boothby, Jack Raymond, Alberto Nocera, Andrew D. King

arXiv:2505.08994 

The complex energy landscapes exhibited by frustrated magnetic systems undergoing quantum fluctuations are a challenge to accurately simulate, and thus of great interest for testing diverse qubit platforms in the field of quantum simulation. This study experimentally demonstrates quantum fluctuations lifting the degenerate ground-state manifold of classical magnetic configurations in fullerene Ising models with resonating dimers. The interplay between degeneracy and quantum fluctuations makes these boundary-free models a suitable benchmark for quantum simulators. Indeed, we observe significant performance improvement across generations of superconducting quantum annealers, showing the potential of highly symmetric, frustrated systems for assessing the precision of quantum-simulation technologies.

King, A. D., Raymond, J., Lanting, T., Harris, R., Zucca, A., Altomare, F., Berkley, A. J., Boothby, K., Ejtemaee, S., Enderud, C., Hoskinson, E., Huang, S., Ladizinsky, E., MacDonald, A. J. R., Marsden, G., & others. (2025). Beyond-classical computation in quantum simulation. Science. https://www.science.org/doi/10.1126/science.ado6285

Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. Establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here we show that superconducting quantum annealing processors can rapidly generate samples in close agreement with solutions of the Schrödinger equation. We demonstrate area-law scaling of entanglement in the model quench dynamics of two-, three- and infinite-dimensional spin glasses, supporting the observed stretched-exponential scaling of effort for matrix-product-state approaches. We show that several leading approximate methods based on tensor networks and neural networks cannot achieve the same accuracy as the quantum annealer within a reasonable timeframe. Thus quantum annealers can answer questions of practical importance that may remain out of reach for classical computation.

Amin, M. H., Raymond, J., Kinn, D., Hamze, F., Hamer, K., Pasvolsky, J., Bernoudy, W., King, A. D., & Kortas, S. (2025). Blockchain with proof of quantum work.

https://arxiv.org/abs/2503.14462v1

We propose a blockchain architecture in which mining requires a quantum computer. The consensus mechanism is based on proof of quantum work, a quantum-enhanced alternative to traditional proof of work that leverages quantum supremacy to make mining intractable for classical computers. We have refined the blockchain framework to incorporate the probabilistic nature of quantum mechanics, ensuring stability against sampling errors and hardware inaccuracies. To validate our approach, we implemented a prototype blockchain on four D-WaveTM quantum annealing processors geographically distributed within North America, demonstrating stable operation across hundreds of thousands of quantum hashing operations. Our experimental protocol follows the same approach used in the recent demonstration of quantum supremacy [1], ensuring that classical computers cannot efficiently perform the same computation task. By replacing classical machines with quantum systems for mining, it is possible to significantly reduce the energy consumption and environmental impact traditionally associated with blockchain mining. Beyond serving as a proof of concept for a meaningful application of quantum computing, this work highlights the potential for other near-term quantum computing applications using existing technology.

Zhang, H., Boothby, K. & Kamenev, A. Cyclic quantum annealing: searching for deep low-energy states in 5000-qubit spin glass. Sci Rep 14, 30784 (2024). https://doi.org/10.1038/s41598-024-80761-z

Quantum computers promise a qualitative speedup in solving a broad spectrum of practical optimization problems. The latter can be mapped onto the task of finding low-energy states of spin glasses, which is known to be exceedingly difficult. Using D-Wave’s 5000-qubit quantum processor, we demonstrate that a recently proposed iterative cyclic quantum annealing algorithm can find deep low-energy states in record time. We also find intricate structures in a low-energy landscape of spin glasses, such as a power-law distribution of connected clusters with a small surface energy. These observations offer guidance for further improvement of the optimization algorithms.

Thomas Lubinski, Carleton Coffrin, Catherine McGeoch, Pratik Sathe, Joshua Apanavicius, David Bernal Neira, Quantum Economic Development Consortium(QED-C) Collaboration

ACM Transactions on Quantum Computing, Volume 5, Issue 3

Catherine McGeoch, D-Wave; David Esteban Bernal Neira, Purdue University; Susan Mniszewski, Los Alamos National Laboratory; Nicholas Chancellor, Durham University 

The past decade has seen four generations of quantum annealing processors, with qubit counts increasing from 512 on the D-Wave Two (released in 2013), to over 5000 on Advantage processors available in 2023. During this time, expanding access for researchers has sparked enormous growth in publications and in the body of knowledge surrounding capabilities, applications, and best practices in use of these novel computing systems.

This Research Topic will invite submissions on all aspects of empirical experience with annealing-based quantum computers. The intention is to present a broad survey of the current state of knowledge about quantum annealing hardware, performance, software infrastructures, and application

Abhishek Awasthi∗, Nico Kraus†, Florian Krellner‡, David Zambrano† ∗BASF Digital Solutions GmbH, Ludwigshafen am Rhein, Germany †Aqarios GmbH, Munich, Germany
‡SAP SE, Walldorf, Germany

This work is a benchmark study for quantum- classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on different machines, with a non-linear objective function. We first present the motivation and the problem description, along with different modeling techniques for classical and quantum computing. The modeling for classical solvers has been done as a mixed-integer convex program, while for the quantum-classical solver we model the problem as a binary quadratic program, which is best suited to the D-Wave Leap’s Hybrid Solver. This ensures that all the solvers we use are fetched with dedicated and most suitable model(s). Henceforth, we carry out benchmarking and comparisons between classical and quantum-classical methods, on problem sizes ranging till approximately 150, 000 variables. We utilize an industry grade classical solver and compare its results with D-Wave Leap’s Hybrid Solver. The results we obtain from D-Wave are highly competitive and sometimes offer speedups, compared to the classical solver.

Pérez Armas, L.F., Creemers, S. & Deleplanque, S. Solving the resource constrained project scheduling problem with quantum annealing. Scientific Reports14, 16784 (2024). 

Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations researc