Post-Error Correction for Quantum Annealing Processor using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based post-error correction method for quantum annealers, aiming to improve solution quality for Ising model problems, with promising scalability but currently limited performance compared to classical algorithms.

Contribution

It presents a novel reinforcement learning approach for error correction in quantum annealing, applied to the Chimera topology, demonstrating scalability and potential for real-world problem solving.

Findings

Reinforcement learning can correct quantum annealer outputs to lower energies.

The method scales well from small to large problem instances.

Performance currently lags behind classical algorithms like simulated annealing.

Abstract

Finding the ground state of the Ising spin-glass is an important and challenging problem (NP-hard, in fact) in condensed matter physics. However, its applications spread far beyond physic due to its deep relation to various combinatorial optimization problems, such as travelling salesman or protein folding. Sophisticated and promising new methods for solving Ising instances rely on quantum resources. In particular, quantum annealing is a quantum computation paradigm, that is especially well suited for Quadratic Unconstrained Binary Optimization (QUBO). Nevertheless, commercially available quantum annealers (i.e., D-Wave) are prone to various errors, and their ability to find low energetic states (corresponding to solutions of superior quality) is limited. This naturally calls for a post-processing procedure to correct errors (capable of lowering the energy found by the annealer). As a proof-of-concept, this work combines the recent ideas revolving around the DIRAC architecture with the Chimera topology and applies them in a real-world setting as an error-correcting scheme for quantum annealers. Our preliminary results show how to correct states output by quantum annealers using reinforcement learning. Such an approach exhibits excellent scalability, as it can be trained on small instances and deployed for large ones. However, its performance on the chimera graph is still inferior to a typical algorithm one could incorporate in this context, e.g., simulated annealing.