Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches

Abstract

This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems: max-cut, number partitioning, knapsack, and quantum spin glass. Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver, quantum approximate optimization algorithm, quantum imaginary time evolution, and imaginary time quantum annealing, with both ansatz-based and ansatz-free implementations, alongside tensor network methods and direct simulations of the imaginary-time Schr\"odinger equation. For comparative analysis, we also utilize classical simulated annealing and quantum annealing on D-Wave devices. Employing default configurations, our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies. This work provides an objective performance baseline and serves as a critical reference point for advancing NISQ algorithms and quantum annealing platforms.