Certified bounds on optimization problems in quantum theory

TL;DR

This paper develops a rigorous method to extract exact rational bounds from numerical semidefinite relaxations in quantum optimization problems, enhancing the certifiability of solutions in quantum information science.

Contribution

It introduces a framework for obtaining exact rational bounds from numerical data in non-commutative quantum optimization, improving reliability and certification of semidefinite relaxation results.

Findings

Framework successfully extracts exact bounds from numerical solutions.

Extension to sparse and symmetry-adapted relaxations improves efficiency.

Establishes rational post-processing as a practical certification tool.

Abstract

Semidefinite relaxations of polynomial optimization have become a central tool for addressing the non-convex optimization problems over non-commutative operators that are ubiquitous in quantum information theory and, more in general, quantum physics. Yet, as these global relaxation methods rely on floating-point methods, the bounds issued by the semidefinite solver can - and often do - exceed the global optimum, undermining their certifiability. To counter this issue, we introduce a rigorous framework for extracting exact rational bounds on non-commutative optimization problems from numerical data, and apply it to several paradigmatic problems in quantum information theory. An extension to sparsity and symmetry-adapted semidefinite relaxations is also provided and compared to the general dense scheme. Our results establish rational post-processing as a practical route to reliable certification, pushing semidefinite optimization toward a certifiable standard for quantum information science.