Boosting quantum annealer performance via sample persistence

TL;DR

This paper introduces a method to improve quantum annealer performance by fixing high-probability variables, simplifying problems, and enhancing success rates, especially for complex, high-precision problems.

Contribution

The paper presents a novel variable-fixing approach that significantly boosts quantum annealer efficiency and success metrics, applicable to real-world and structured problems.

Findings

Increased success rate and better energy solutions with the method.

Effective for high-precision, challenging problems.

Iterative application further improves results.

Abstract

We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer (or any sampler) to fix the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are usually much easier for the quantum annealer to solve, due to their being smaller and consisting of disconnected components. This approach significantly increases the success rate and number of observations of the best known energy value in samples obtained from the quantum annealer, when compared with calling the quantum annealer without using it, even when using fewer annealing cycles. Use of the method results in a considerable improvement in success metrics even for problems with high-precision couplers and biases, which are more challenging for the quantum annealer to solve. The results are further enhanced by applying the method iteratively and combining it with classical pre-processing. We present results for both Chimera graph-structured problems and embedded problems from a real-world application.