Progress on Data-Driven, Multi-Objective Quantum Optimization

TL;DR

This paper introduces two novel methods to enhance quadratic unconstrained binary optimization (QUBO) for practical data-driven materials design, improving efficiency and solution quality in complex, real-world optimization tasks.

Contribution

It presents a preprocessing scheme to remove constraints from QUBO models and a multi-objective optimization strategy compatible with non-convex landscapes, advancing QUBO's applicability.

Findings

Significant acceleration of solution search in QUBO models.

Improved Pareto front approximation in multi-objective optimization.

Enhanced applicability of QUBO in materials design tasks.

Abstract

Here, we present two complementary approaches that advance quadratic unconstrained binary optimization (QUBO) toward practical use in data-driven materials design and other real-valued black-box optimization tasks. First, we introduce a simple yet powerful preprocessing scheme that, when applied to a machine-learned QUBO model, entirely removes system-level equality constraints by construction. This makes cumbersome soft-penalty terms obsolete, simplifies QUBO formulation, and substantially accelerates solution search. Second, we develop a multi-objective optimization strategy inspired by Tchebycheff scalarization that is compatible with non-convex objective landscapes and outperforms existing QUBO-based Pareto front methods. We demonstrate the effectiveness of both approaches using a simplified model of a multi-phase aluminum alloy design problem, highlighting significant gains in efficiency and solution quality. Together, these methods broaden the applicability of QUBO-based optimization and provide practical tools for data-driven materials discovery and beyond.