On Quantum Annealing Without a Physical Quantum Annealer

TL;DR

This paper introduces a hybrid quantum-classical heuristic called Quantum Accelerated Simulated Annealing (QASA), which uses discretised quantum annealing circuits to enhance classical simulated annealing, showing promising results in reducing steps needed.

Contribution

The paper proposes and evaluates QASA, a novel hybrid approach that integrates low-depth discretised quantum annealing with classical simulated annealing for optimization.

Findings

QASA performs comparably to classical simulated annealing with fewer steps.

Discretised quantum annealing circuits can effectively accelerate classical optimization.

The approach shows potential for future quantum optimization methods.

Abstract

Quantum annealing is an emerging metaheuristic used for solving combinatorial optimisation problems. However, hardware based physical quantum annealers are primarily limited to a single vendor. As an alternative, we can discretise the quantum annealing process (discretised quantum annealing or DiQA) and use it on gate-model quantum computers. In this work, we first benchmark DiQA against simulated annealing for a similar number of steps. We then propose and evaluate a hybrid quantum classical heuristic: Quantum Accelerated Simulated Annealing (QASA), where the traditional classical annealing procedure can be sped up with the use of (relatively) low depth DiQA circuits. This is done by (i) running a partial annealing scheme with a fraction of the depth of the complete circuit (ii) sampling the results from the circuit and fitting a Gibbs distribution on it and (iii) Using the inverse temperature of the Gibbs distribution and the best sample to initialise simulated annealing (SA). Our simulation results show QASA performing comparably to SA but for a reduced amount of steps. With the promising results of our work, we hope to generate interest for potential future work in the area of fixed-parameter quantum optimisation.