Chinese Academy of Sciences Cuts TSP Complexity to O(1.865666^n), Beating Held-Karp Bound
Updated
Updated · Quantum Zeitgeist · Jun 18
Chinese Academy of Sciences Cuts TSP Complexity to O(1.865666^n), Beating Held-Karp Bound
1 articles · Updated · Quantum Zeitgeist · Jun 18
Summary
A Chinese Academy of Sciences team reported a hybrid quantum-classical travelling salesman solver with query complexity O*(1.865666^n), crossing below the classical Held-Karp benchmark for the first time in this line of work.
The gain comes from a 4-subset divide-and-conquer scheme that combines dynamic programming with quantum search, while also improving structured quantum-state preparation and parallel data loading rather than relying on search speedup alone.
Their reanalysis found earlier quantum estimates were wrong because they missed half the recursive branches, showing the previously studied 8-subset scheme could not actually beat classical methods.
Qiskit simulations on 6-node and 7-node graphs reached 98.9% and 100% accuracy, though the approach remains limited to small instances by qubit counts and coherence times.
The result refines a 2019 method and points toward scaling tests, noise studies and possible integration with techniques such as variational quantum eigensolvers for real-world optimisation.
How soon will this quantum breakthrough solve real-world logistical nightmares?
Is China's $15B investment creating an insurmountable lead in the quantum race?
Hybrid Quantum-Classical Algorithm Breaks TSP Benchmark: CAS Solves 10-City Instance with Record Efficiency
Overview
The Chinese Academy of Sciences (CAS) has achieved a major milestone in quantum computing by developing a novel quantum algorithm that efficiently solved a 10-city Traveling Salesperson Problem (TSP). This marks the first time a quantum computer has outperformed classical algorithms for a TSP of this scale, finding the optimal route much faster than traditional methods. Published in Nature Physics in June 2026, this breakthrough demonstrates the real-world potential of quantum computing for complex optimization problems, opening new possibilities for industries like logistics and supply chain management.