No quantum advantage implies improved bounds and classical algorithms for the binary paint shop problem
This work addresses an APX-hard optimization problem, providing a classical algorithm that surpasses quantum methods, which is incremental but relevant for sparse optimization benchmarks.
The paper tackles the binary paint shop problem (BPSP), showing that the Mean-Field Approximate Optimization Algorithm (MF-AOA) achieves a paint swap ratio of approximately 0.2799, outperforming known classical heuristics and quantum algorithms.
The binary paint shop problem (BPSP) is an APX-hard optimization problem in which, given n car models that occur twice in a sequence of length 2n, the goal is to find a colouring sequence such that the two occurrences of each model are painted differently, while minimizing the number of times the paint is swapped along the sequence. A recent classical heuristic, known as the recursive star greedy (RSG) algorithm, is conjectured to achieve an expected paint swap ratio of 0.361, thereby outperforming the QAOA with circuit depth p = 7. Since the performance of the QAOA with logarithmic circuit depth is instance independent, the average paint swap ratio is upper-bounded by the QAOA, which numerical evidence suggests is approximately between 0.265 and 0.282. To provide hardware-relevant comparisons, we additionally implement the BPSP on a D-Wave Quantum Annealer Advantage 2, obtaining a minimum paint swap ratio of 0.320. Given that the QAOA with logarithmic circuit depth does not exhibit quantum advantage for sparse optimization problems such as the BPSP, this implies the existence of a classical algorithm that surpasses both the RSG algorithm and logarithmic depth QAOA. We provide numerical evidence that the Mean-Field Approximate Optimization Algorithm (MF-AOA) is one such algorithm that beats all known classical heuristics and quantum algorithms to date with a paint swap ratio of approximately 0.2799.