Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm

TL;DR

This paper demonstrates that the Quantum Approximate Optimization Algorithm (QAOA) can outperform classical heuristics in solving the binary paint shop problem, a hard combinatorial optimization challenge, especially as problem size grows.

Contribution

It shows how QAOA with constant depth can beat classical heuristics on average for large instances of the BPSP and explores its performance through numerical and experimental studies.

Findings

QAOA outperforms classical heuristics in the infinite size limit

QAOA's performance varies with instance hardness

First experiments conducted on a quantum computer

Abstract

The binary paint shop problem (BPSP) is an APX-hard optimization problem of the automotive industry. In this work, we show how to use the Quantum Approximate Optimization Algorithm (QAOA) to find solutions of the BPSP and demonstrate that QAOA with constant depth is able to beat classical heuristics on average in the infinite size limit $n\rightarrow\infty$. For the BPSP, it is known that no classical algorithm can exist which approximates the problem in polynomial runtime. We introduce a BPSP instance which is hard to solve with QAOA, and numerically investigate its performance and discuss QAOA's ability to generate approximate solutions. We complete our studies by running first experiments of small-sized instances on a trapped-ion quantum computer through AWS Braket.