Recommending Solution Paths for Solving Optimization Problems with Quantum Computing

TL;DR

This paper introduces a framework that helps users select optimal solution paths for quantum computing-based optimization, integrating various algorithms and tools to simplify complex decision-making.

Contribution

It presents a novel abstraction layer and modular framework for recommending and evaluating solution paths in quantum optimization workflows, making quantum techniques more accessible.

Findings

Validated approach on capacitated vehicle routing problem

Developed tools for graphical analysis of variational quantum algorithms

Identified key requirements and challenges for automation layer

Abstract

Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed automation layer within a quantum-assisted solution workflow for optimization problems.