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Paper 1
A Co-Optimization Qubit Mapping Algorithm viaTwo-Stage Search and Bidirectional Look
Xu K, Chen P, Wang Y, Li D.
- Year
- 2026
- Journal
- Europe PMC
- DOI
- 10.21203/rs.3.rs-8363879/v1
- arXiv
- -
No abstract.
Open paperPaper 2
A Resource-Efficient Variational Quantum Framework for the Traveling Salesman Problem
Yuefeng Lin, Chao Zheng, Cong Guo
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2605.00739
- arXiv
- 2605.00739
The Traveling Salesman Problem (TSP) is a prototypical combinatorial optimization problem, but its quantum implementation is limited by the O(n^2)-qubit overhead of standard one-hot encodings. Here, we propose a resource-efficient variational quantum framework based on compact binary-register encoding, a permutation-preserving problem-inspired ansatz, and a complementary divide-and-conquer execution strategy. The compact encoding reduces the data-qubit requirement to O(n log n), while the divide-and-conquer formulation lowers the number of qubits required in each local hardware execution to the size of the largest subsystem. Numerical simulations on TSP instances with 4, 5, and 6 cities achieve best average success rates of 100%, 100%, and 95.5%, respectively. A local two-qubit implementation of the divide-and-conquer approximation is further evaluated for a 5-city TSP instance on SpinQ Gemini Pro and SpinQ Triangulum II NMR quantum computers. Taken together, the results indicate how compact encoding and divide-and-conquer execution with classical post-processing can be used to study small combinatorial optimization instances on resource-constrained quantum hardware.
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