Quantum Max-Cut is NP hard to approximate
Stephen Piddock
TL;DR
The paper proves unconditional NP-hardness of approximating Quantum Max-Cut within a constant factor on unweighted graphs of bounded degree, by connecting Product-QMC to Quantum Max-Cut and embedding a classical Max-Cut instance via a two-stage reduction. It introduces a PTAS reduction framework and uses SU(2) representation theory and Lieb's inequality to relate product-state optima to full quantum optima. A rank-constrained Max-Cut construction with triangle gadgets and bipartite expanders yields APX-completeness for all constant k, including a direct hardness for Product-QMC with XY interactions. Overall, the work establishes strong hardness of approximation for a broad quantum local Hamiltonian family without relying on conjectures, and clarifies the computational landscape of quantum max-cut type problems.
Abstract
We unconditionally prove that it is NP-hard to compute a constant multiplicative approximation to the QUANTUM MAX-CUT problem on an unweighted graph of constant bounded degree. The proof works in two stages: first we demonstrate a generic reduction to computing the optimal value of a quantum problem, from the optimal value over product states. Then we prove an approximation preserving reduction from MAX-CUT to PRODUCT-QMC the product state version of QUANTUM MAX-CUT. More precisely, in the second part, we construct a PTAS reduction from MAX-CUT$_k$ (the rank-k constrained version of MAX-CUT) to MAX-CUT$_{k+1}$, where MAX-CUT and PRODUCT-QMC coincide with MAX-CUT$_1$ and MAX-CUT$_3$ respectively. We thus prove that Max-Cut$_k$ is APX-complete for all constant $k$.
