2-Approximation Algorithm Rivals Quantum Traveling Salesman Problem Performance

Researchers at the University of Toronto Scarborough have developed an open-source framework for comparing classical and quantum approaches to solving the notoriously difficult Traveling Salesman Problem. Their work reveals that a 2-approximation algorithm utilizing minimum spanning trees can rival the performance of the Quantum Approximate Optimization Algorithm on this NP-hard problem, suggesting relatively simple classical methods remain competitive with emerging quantum techniques. The team implemented and evaluated four distinct solution methods, brute-force search, the 2-approximation algorithm, simulated annealing, and QAOA, on graphs of varying sizes to analyze performance and scalability. This comparative study addresses a gap in existing research by focusing on implementation-level trade-offs and providing a reproducible environment for further…