Constraint-oriented biased quantum search for linear constrained combinatorial optimization problems

TL;DR

This paper introduces a quantum search framework for linear constrained combinatorial optimization, leveraging circuit optimization and machine learning to potentially outperform classical solvers with future quantum hardware.

Contribution

It extends a Grover-based heuristic to handle general linear constraints and incorporates optimization techniques for improved performance.

Findings

Demonstrates potential quantum advantage over classical solvers

Framework enables performance improvements via circuit optimization and machine learning

Shows promising results with suitable quantum hardware

Abstract

In this paper, we extend a previously presented Grover-based heuristic to tackle general combinatorial optimization problems with linear constraints. We further describe the introduced method as a framework that enables performance improvements through circuit optimization and machine learning techniques. Comparisons with state-of-the-art classical solvers further demonstrate the algorithm's potential to achieve a quantum advantage in terms of speed, given appropriate quantum hardware.