Speaker
Description
Finding the ground state of many-body systems is a central challenge in statistical physics and combinatorial optimization. Hard optimization problems can be mapped onto spin-glass–like Hamiltonians whose ground-state configurations encode valid solutions. In this work, we introduce a quantum-inspired tensor network method to tackle this class of problems. Our approach extends standard quantum annealing by allowing for complex time evolution and projects the resulting dynamics onto a tensor network manifold using the time-dependent variational principle. We apply this method to the ground-state search of the paradigmatic Sherrington–Kirkpatrick (SK) spin-glass model. By comparing pure imaginary-time quantum annealing with complex-time annealing, we observe that the former exhibits superior performance in both accuracy and computational efficiency. We also analyze the scaling of computational effort and find that both the time to reach an accurate ground state and the required bond dimension grow only slowly with system size, indicating favorable scalability. Overall, our results indicate that tensor-network–based quantum-inspired optimization methods constitute a viable alternative within combinatorial optimization. Finally, we explore how convergence behavior may relate to structural indicators of instance hardness in typical combinatorial problems.
References
@article{PhysRevResearch.1.033142, title = {Analysis of the relation between quadratic unconstrained binary optimization and the spin-glass ground-state problem}, author = {Boettcher, Stefan}, journal = {Phys. Rev. Res.}, volume = {1}, issue = {3}, pages = {033142}, numpages = {9}, year = {2019}, month = {Dec}, publisher = {American Physical Society}, doi = {10.1103/PhysRevResearch.1.033142}, url = {https://link.aps.org/doi/10.1103/PhysRevResearch.1.033142} }
| Sessions | Quantum Simulation |
|---|---|
| Invited | No |