Integer programming techniques for minor-embedding in quantum annealers

TL;DR

This paper introduces integer programming techniques to improve minor-embedding in quantum annealers, enabling better detection of infeasibility and solution bounds compared to heuristic methods.

Contribution

It presents novel IP-based methods, including a direct translation and a decomposition approach, for solving the minor-embedding problem in quantum annealers.

Findings

Decomposition approach outperforms monolithic IP model

Methods can detect infeasibility of instances

Provides bounds on solution quality

Abstract

A major limitation of current generations of quantum annealers is the sparse connectivity of manufactured qubits in the hardware graph. This technological limitation generated considerable interest, motivating efforts to design efficient and adroit minor-embedding procedures that bypass sparsity constraints. In this paper, starting from a previous equational formulation by Dridi et al. (arXiv:1810.01440), we propose integer programming (IP) techniques for solving the minor-embedding problem. The first approach involves a direct translation from the previous equational formulation to IP, while the second decomposes the problem into an assignment master problem and fiber condition checking subproblems. The proposed methods are able to detect instance infeasibility and provide bounds on solution quality, capabilities not offered by currently employed heuristic methods. We demonstrate the efficacy of our methods with an extensive computational assessment involving three different families of random graphs of varying sizes and densities. The direct translation as a monolithic IP model can be solved with existing commercial solvers yielding valid minor-embeddings, however, is outperformed overall by the decomposition approach. Our results demonstrate the promise of our methods for the studied benchmarks, highlighting the advantages of using IP technology for minor-embedding problems.