Quantum-Enhanced Simulated Annealing Using Rydberg Atoms

Abstract: Quantum-classical hybrid algorithms offer a promising strategy for tackling computationally challenging problems, such as the maximum independent set (MIS) problem that plays a crucial role in areas like network design and data analysis. This study experimentally demonstrates that a Rydberg quantum-classical hybrid algorithm, termed as quantum-enhanced simulated annealing (QESA), provides a computational time advantage over standalone simulated annealing (SA), a classical heuristic optimization method. The performance of QESA is evaluated based on the approximation ratio and the Hamming distance, relative to the graph size. The analysis shows that QESA outperforms standalone SA by leveraging a warm-start input derived from two types of Rydberg atomic array experimental data: quench evolution (QE) (implemented on the Quera Aquila machine) and adiabatic quantum computing (AQC) (using the experimental dataset archieved in K. Kim et al., Scientific Data 11, 111 (2024). Based on these results, an estimate is provided for the maximum graph size that can be handled within a one-day computational time limit on a standard personal computer. These findings suggest that QESA has the potential to offer a computational advantage over classical methods for solving complex optimization problems efficiently.