Scalable Determination of Penalization Weights for Constrained Optimizations on Approximate Solvers

TL;DR

This paper introduces a scalable, provably effective method for setting penalization weights in QUBO formulations, enhancing solver performance across classical and quantum architectures.

Contribution

It presents a pre-computation strategy with theoretical guarantees for penalization weights, improving efficiency and robustness in constrained QUBO problems.

Findings

Achieves order-of-magnitude speedups over existing heuristics

Demonstrates robustness across diverse problem types and solver architectures

Provides polynomial complexity solutions with provable guarantees

Abstract

Quadratic unconstrained binary optimization (QUBO) provides problem formulations for various computational problems that can be solved with dedicated QUBO solvers, which can be based on classical or quantum computation. A common approach to constrained combinatorial optimization problems is to enforce the constraints in the QUBO formulation by adding penalization terms. Penalization introduces an additional hyperparameter that significantly affects the solver's efficacy: the relative weight between the objective terms and the penalization terms. We develop a pre-computation strategy for determining penalization weights with provable guarantees for Gibbs solvers and polynomial complexity for broad problem classes. Experiments across diverse problems and solver architectures, including large-scale instances on Fujitsu's Digital Annealer, show robust performance and order-of-magnitude speedups over existing heuristics.