Approximation Algorithms for Quantum Max-$d$-Cut

TL;DR

This paper introduces the first polynomial-time approximation algorithm for the Quantum Max-d-Cut problem, a quantum generalization of Max-d-Cut, with performance guarantees and a proven gap instance for d ≥ 3.

Contribution

It develops a novel approximation algorithm for Quantum Max-d-Cut and establishes its limitations through a gap instance, advancing understanding of quantum combinatorial optimization.

Findings

Provides a polynomial-time randomized approximation algorithm.

Achieves non-trivial performance guarantees for quantum states.

Proves the tightness of the analysis with a gap instance for d ≥ 3.

Abstract

We initiate the algorithmic study of the Quantum Max-$d$-Cut problem, a quantum generalization of the well-known Max-$d$-Cut problem. The Quantum Max-$d$-Cut problem involves finding a quantum state that maximizes the expected energy associated with the projector onto the antisymmetric subspace of two, $d$-dimensional qudits over all local interactions. Equivalently, this problem is physically motivated by the $SU(d)$-Heisenberg model, a spin glass model that generalized the well-known Heisenberg model over qudits. We develop a polynomial-time randomized approximation algorithm that finds product-state solutions of mixed states with bounded purity that achieve non-trivial performance guarantees. Moreover, we prove the tightness of our analysis by presenting an algorithmic gap instance for Quantum Max-d-Cut problem with $d \geq 3$.