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Physical Review A

Unraveling reverse annealing: A study of D-Wave quantum annealers

Vrinda Mehta, Hans A. De Raedt, Kristel F. L. Michielsen, Fengping Jin

Published Jan 1, 2025• 0 citations
Publisher

Abstract

D-Wave quantum annealers offer reverse annealing as a feature allowing them to refine solutions to optimization problems. This paper investigates the influence of key parameters, such as annealing times and reversal distance, on the behavior of reverse annealing by studying models containing up to 1000 qubits. Through the analysis of theoretical models and experimental data, we explore the interplay between quantum and classical processes in describing the empirical data. Our findings provide a deeper understanding that can better equip users to fully harness the potential of the D-Wave annealers.

DOI
10.34734/fzj-2026-00024

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D-Wave quantum annealers offer reverse annealing as a feature allowing them to refine solutions to optimization problems. This paper investigates the influence of key parameters, such as annealing times and reversal distance, on the behavior of reverse annealing by studying models containing up to 1000 qubits. Through the analysis of theoretical models and experimental data, we explore the interplay between quantum and classical processes in describing the empirical data. Our findings provide a deeper understanding that can better equip users to fully harness the potential of the D-Wave annealers.
D-Wave quantum annealers offer reverse annealing as a feature allowing them to refine solutions to optimization problems. This paper investigates the influence of key parameters, such as annealing times and reversal distance, on the behavior of reverse annealing by studying models containing up to 1000 qubits. Through the analysis of theoretical models and experimental data, we explore the interplay between quantum and classical processes in describing the empirical data. Our findings provide a deeper understanding that can better equip users to fully harness the potential of the D-Wave annealers.
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Understanding the physical nature of the D-Wave annealers remains a subject of active investigation. In this study, we analyze the sampling behavior of these systems and explore whether their results can be replicated using quantum and Markovian models. Employing the standard and the fast annealing protocols, we observe that the D-Wave annealers sample states with frequencies matching the Gibbs distribution for sufficiently long annealing times. Using Bloch equation simulations for single-qubit problems and Lindblad and Markovian master equations for two-qubit systems, we compare experimental data with theoretical predictions. Our results provide insights into the role of quantum mechanics in these devices.
Understanding the physical nature of the D-Wave annealers remains a subject of active investigation. In this study, we analyze the sampling behavior of these systems and explore whether their results can be replicated using quantum and Markovian models. Employing the standard and the fast annealing protocols, we observe that the D-Wave annealers sample states with frequencies matching the Gibbs distribution for sufficiently long annealing times. Using Bloch equation simulations for single-qubit problems and Lindblad and Markovian master equations for two-qubit systems, we compare experimental data with theoretical predictions. Our results provide insights into the role of quantum mechanics in these devices.
Being immersed in the noisy intermediate-scale quantum (NISQ) era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called reverse annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewhere. Despite the research activity around reverse annealing, no study has theorized about the possible benefits related to the transfer of knowledge under this paradigm. This work moves in that direction and is driven by experimentation focused on answering two key research questions: i) is reverse annealing a paradigm that can benefit from knowledge transfer between similar problems? and ii) can we infer the characteristics that an input solution should meet to help increase the probability of success? To properly guide the tests in this paper, the well-known knapsack problem has been chosen for benchmarking purposes, using a total of 34 instances composed of 14 and 16 items.
Quantum annealers conventionally use forward annealing to generate heuristic solutions. Reverse annealing can potentially generate better solutions but necessitates an appropriate initial state. Ways to find such states are generally unknown or highly problem dependent, offer limited success, and severely restrict the scope of reverse annealing. We use a general method that improves the overall solution quality and quantity by feeding reverse annealing with low-quality solutions obtained from forward annealing. An experimental demonstration of solving the graph coloring problem using the D-Wave quantum annealers shows that our method is able to convert invalid solutions obtained from forward annealing to at least one valid solution obtained after assisted reverse annealing for 57% of 459 random Erdos–Rényi graphs. Our method significantly outperforms random initial states, obtains more unique solutions on average, and widens the applicability of reverse annealing. Although the average number of valid solutions obtained drops exponentially with the problem size, a scaling analysis for the graph coloring problem shows that our method effectively extends the computational reach of conventional forward annealing using reverse annealing.