Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers

TL;DR

The paper addresses accessibility barriers in quantum optimization by automating QUBO reformulation and solver integration. It proposes an automatic framework that preserves conventional optimization interfaces, translates problems to solver-ready QUBO, and decodes results, enabling non-experts to employ quantum solvers. The framework supports multiple solvers (QA, QAOA, VQE, GAS, SA) and provides automated penalty-weight estimation and thorough solution analysis. Demonstrated on knapsack and linear regression, the approach is open-source and offers a practical path toward broader adoption of quantum optimization tools.

Abstract

Optimizing objective functions stands to benefit significantly from leveraging quantum computers, promising enhanced solution quality across various application domains in the future. However, harnessing the potential of quantum solvers necessitates formulating problems according to the Quadratic Unconstrained Binary Optimization (QUBO) model, demanding significant expertise in quantum computation and QUBO formulations. This expertise barrier limits access to quantum solutions. Fortunately, automating the conversion of conventional optimization problems into QUBO formulations presents a solution for promoting accessibility to quantum solvers. This article addresses the unmet need for a comprehensive automatic framework to assist users in utilizing quantum solvers for optimization tasks while preserving interfaces that closely resemble conventional optimization practices. The framework prompts users to specify variables, optimization criteria, as well as validity constraints and, afterwards, allows them to choose the desired solver. Subsequently, it automatically transforms the problem description into a format compatible with the chosen solver and provides the resulting solution. Additionally, the framework offers instruments for analyzing solution validity and quality. Comparative analysis against existing libraries and tools in the literature highlights the comprehensive nature of the proposed framework. Two use cases (the knapsack problem and linear regression) are considered to show the completeness and efficiency of the framework in real-world applications. Finally, the proposed framework represents a significant advancement towards automating quantum computing solutions and widening access to quantum optimization for a broader range of users.

Figures (3)

• Figure 1: Quantum Optimization Flow.
• Figure 2: Workflow of the proposed framework. The letters correspond to the subsections where each step is discussed.
• Figure 3: Analysis of the results obtained by solving with each supported optimizer the use case problems.