Elevating Variational Quantum Semidefinite Programs for Polynomial Objectives

TL;DR

This paper introduces Product-State Lifting (PSL), a method to extend variational quantum semidefinite programs for polynomial optimization, enabling efficient handling of higher-degree problems with minimal resource increase.

Contribution

The authors propose PSL, a simple encoding that upgrades vQSDPs to handle k-degree polynomial objectives with only linear resource growth, bridging quadratic and polynomial optimization.

Findings

PSL maintains device-friendly structure of vQSDPs.

PSL enables linear resource scaling with polynomial degree.

Application to Max-kSAT demonstrates practical potential.

Abstract

Many practically important NP-hard optimization problems are inherently higher-order polynomial optimizations, which are typically addressed using approximation algorithms. Classical relaxations express polynomial objectives over a polynomial basis and solve the resulting quadratic objective as a semidefinite program, which can significantly inflate problem size and degrade approximation behavior. Variational quantum analogues to classical semidefinite programs (vQSDPs) are near-term formulations geared towards quadratic objectives. We introduce Product-State Lifting (PSL), a simple product-register encoding that upgrades any vQSDP with basis-state encoding to tackle $k$-degree polynomial optimization. This upgrade requires only a linear increase in resources with constraints constant in $k$. As a worked example, we pair PSL with the recently-proposed vQSDP with the Hadamard test and approximate amplitude constraints [Quantum 7, 1057 (2023)], and outline an application to Max-$k$SAT. PSL maintains the device-friendly structure of vQSDPs while making polynomial degree a linear resource parameter, offering a general path from quadratic to polynomial optimization without the constraint growth typical of classical relaxations.