Hybrid annealing using a quantum simulator coupled to a classical computer

TL;DR

This paper introduces a hybrid annealing algorithm combining quantum simulation and classical processing to efficiently find low-energy states in complex landscapes, outperforming traditional methods in certain scenarios.

Contribution

The paper proposes a novel hybrid annealing approach that integrates quantum and classical techniques, improving optimization in problems with many quasi-degenerate ground states.

Findings

Potentially outperforms simulated annealing and quantum annealing

Most effective for problems with many quasi-degenerate ground states

Simulations on small instances show promising results

Abstract

Finding the global minimum in a rugged potential landscape is a computationally hard task, often equivalent to relevant optimization problems. Simulated annealing is a computational technique which explores the configuration space by mimicking thermal noise. By slow cooling, it freezes the system in a low-energy configuration, but the algorithm often gets stuck in local minima. In quantum annealing, the thermal noise is replaced by controllable quantum fluctuations, and the technique can be implemented in modern quantum simulators. However, quantum-adiabatic schemes become prohibitively slow in the presence of quasidegeneracies. Here we propose a strategy which combines ideas from simulated annealing and quantum annealing. In such hybrid algorithm, the outcome of a quantum simulator is processed on a classical device. While the quantum simulator explores the configuration space by repeatedly applying quantum fluctuations and performing projective measurements, the classical computer evaluates each configuration and enforces a lowering of the energy. We have simulated this algorithm for small instances of the random energy model, showing that it potentially outperforms both simulated thermal annealing and adiabatic quantum annealing. It becomes most efficient for problems involving many quasi-degenerate ground states.