Hybrid Meta-Solving for Practical Quantum Computing

TL;DR

This paper presents Hybrid Meta-Solving, a novel framework combining classical and quantum optimization methods within an accessible software stack, aiming to enhance practical problem-solving in quantum computing contexts.

Contribution

It introduces a new hybrid meta-solving approach that decomposes problems for classical and quantum solvers, enabling customizable, extensible solutions with a prototype implementation.

Findings

Demonstrated applicability in industrial use cases

Reused classical algorithms extended with quantum techniques

Potential to outperform classical methods with future quantum advances

Abstract

The advent of quantum algorithms has initiated a discourse on the potential for quantum speedups for optimization problems. However, several factors still hinder a practical realization of the potential benefits. These include the lack of advanced, error-free quantum hardware, the absence of accessible software stacks for seamless integration and interaction, and the lack of methods that allow us to leverage the theoretical advantages to real-world use cases. This paper works towards the creation of an accessible hybrid software stack for solving optimization problems, aiming to create a fundamental platform that can utilize quantum technologies to enhance the solving process. We introduce a novel approach that we call Hybrid Meta-Solving, which combines classical and quantum optimization techniques to create customizable and extensible hybrid solvers. We decompose mathematical problems into multiple sub-problems that can be solved by classical or quantum solvers, and propose techniques to semi-automatically build the best solver for a given problem. Implemented in our ProvideQ toolbox prototype, Meta-Solving provides interactive workflows for accessing quantum computing capabilities. Our evaluation demonstrates the applicability of Meta-Solving in industrial use cases. It shows that we can reuse state-of-the-art classical algorithms and extend them with quantum computing techniques. Our approach is designed to be at least as efficient as state-of-the-art classical techniques, while having the potential to outperform them if future advances in the quantum domain are made.