Quantum-Informed Recursive Optimization Algorithms

TL;DR

This paper introduces quantum-informed recursive optimization algorithms that combine quantum resources with classical problem reduction techniques to efficiently solve large combinatorial problems, demonstrating competitive results with weak quantum hardware.

Contribution

The paper presents a novel hybrid quantum-classical recursive optimization framework that leverages quantum information for problem simplification and improves classical heuristics.

Findings

QIRO achieves results comparable to classical heuristics.

Using quantum correlations enhances optimization performance.

Demonstrated on quantum hardware via Amazon Braket.

Abstract

We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps that recursively simplify the problem. These reduction steps address the limitations of the quantum component and ensure solution feasibility in constrained optimization problems. Additionally, we use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware. We demonstrate the capabilities of our approach by informing QIRO with correlations from classical simulations of shallow (depth $p=1$) circuits of the quantum approximate optimization algorithm (QAOA), solving instances of maximum independent set and maximum satisfiability problems with hundreds of variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum processor available online on Amazon Braket to find large independent sets of graphs. In summary, our scheme achieves results comparable to classical heuristics, such as simulated annealing and greedy algorithms, even with relatively weak quantum resources. Furthermore, enhancing the quality of these quantum resources improves the performance of the algorithms, highlighting the potential of QIRO. Notably, the modular nature of QIRO offers various avenues for modifications, positioning our work as a blueprint for designing a broader class of hybrid quantum-classical algorithms for combinatorial optimization.