- Access by Xinjiang University
Efficient algorithms for weakly interacting quantum spin systems
Phys. Rev. A 114, 012432 – Published 13 July, 2026
DOI: https://doi.org/10.1103/45rz-42gr
Abstract
We establish efficient algorithms for weakly interacting quantum spin systems at arbitrary temperature. In particular, we obtain a fully polynomial-time approximation scheme for the partition function and an efficient approximate sampling scheme for the thermal distribution over a classical spin space. Our approach is based on the cluster expansion method and a standard reduction from approximate sampling to approximate counting.
Physics Subject Headings (PhySH)
Article Text
References (28)
- T. Helmuth, W. Perkins, and G. Regts, Algorithmic Pirogov–Sinai theory, Probab. Theory Relat. Fields 176, 851 (2020).
- M. Jenssen, P. Keevash, and W. Perkins, Algorithms for #BIS-hard problems on expander graphs, SIAM J. Comput. 49, 681 (2020).
- Z. Chen, A. Galanis, L. A. Goldberg, W. Perkins, J. Stewart, and E. Vigoda, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019), Leibniz International Proceedings in Informatics (Schloss Dagstuhl–Leibniz-Zentrum für Informatik, Saarbrücken, 2019), Vol. 145, pp. 41:1–41:14.
- S. Cannon and W. Perkins, in Proceedings of the Fourteeth Annual ACM-SIAM Symposium on Discrete Algorithms, edited by S. Chawla (SIAM, Philadelphia, 2020), pp. 1456–1466.
- M. Jenssen and W. Perkins, Independent sets in the hypercube revisited, J. London Math. Soc. 102, 645 (2020).
- M. Jenssen, W. Perkins, and A. Potukuchi, Approximately counting independent sets in bipartite graphs via graph containers, Random Struct. Algorithms 63, 215 (2023).
- D. Galvin, G. McKinley, W. Perkins, M. Sarantis, and P. Tetali, On the zeroes of hypergraph independence polynomials, Comb. Probab. Comput. 33, 65 (2024).
- M. Collares, J. Erde, A. Geisler, and M. Kang, Counting independent sets in expanding bipartite regular graphs, arXiv:2503.22255.
- C. Borgs, J. Chayes, T. Helmuth, W. Perkins, and P. Tetali, Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, Chicago, 2020 (ACM, New York, 2020), pp. 738–751.
- T. Helmuth, M. Jenssen, and W. Perkins, Finite-size scaling, phase coexistence, and algorithms for the random cluster model on random graphs, Ann. Inst. H. Poincaré Probab. Statist. 59, 817 (2023).
- C. Carlson, E. Davies, N. Fraiman, A. Kolla, A. Potukuchi, and C. Yap, Algorithms for the ferromagnetic Potts model on expanders, Comb. Probab. Comput. 33, 487 (2024).
- R. L. Mann and T. Helmuth, Efficient algorithms for approximating quantum partition functions, J. Math. Phys. 62, 022201 (2021).
- T. Helmuth and R. L. Mann, Efficient algorithms for approximating quantum partition functions at low temperature, Quantum 7, 1155 (2023).
- R. L. Mann and R. M. Minko, Algorithmic cluster expansions for quantum problems, PRX Quantum 5, 010305 (2024).
- R. Kotecký and D. Preiss, Cluster expansion for abstract polymer models, Commun. Math. Phys. 103, 491 (1986).
- S. Bravyi, D. DiVincenzo, and D. Loss, Polynomial-time algorithm for simulation of weakly interacting quantum spin systems, Commun. Math. Phys. 284, 481 (2008).
- H. Chen, C. Rouzé, J. Chen, J. Jiang, S. O. Scalet, Y. Zhan, G. K.-L. Chan, L. Ying, and Y. Tong, Convergence of the cumulant expansion and polynomial-time algorithm for weakly interacting fermions, arXiv:2512.12010.
- Y. Tong and Y. Zhan, Fast mixing of weakly interacting fermionic systems at any temperature, PRX Quantum 6, 030301 (2025).
- Š. Šmíd, R. Meister, M. Berta, and R. Bondesan, Polynomial-time quantum Gibbs sampling for the weak and strong coupling regime of the Fermi-Hubbard model at any temperature, Nat. Commun. 16, 10736 (2025).
- Y. Zhan, Z. Ding, J. Huhn, J. Gray, J. Preskill, G. K.-L. Chan, and L. Lin, Rapid quantum ground state preparation via dissipative dynamics, Phys. Rev. X 16, 011004 (2026).
- Š. Šmíd, R. Meister, M. Berta, and R. Bondesan, Rapid mixing of quantum Gibbs samplers for weakly-interacting quantum systems, arXiv:2510.04954.
- S. Friedli and Y. Velenik, Statistical Mechanics of Lattice Systems: A Concrete Mathematical Introduction (Cambridge University Press, Cambridge, 2017).
- M. R. Jerrum, L. G. Valiant, and V. V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theor. Comput. Sci. 43, 169 (1986).
- A. Sinclair and M. Jerrum, Approximate counting, uniform generation and rapidly mixing Markov chains, Inf. Comput. 82, 93 (1989).
- C. Yin and A. Lucas, Polynomial-time classical sampling of high-temperature quantum Gibbs states, arXiv:2305.18514.
- A. Bakshi, A. Liu, A. Moitra, and E. Tang, in Proceedings of the 2024 IEEE 65th Annual Symposium on Foundations of Computer Science, Chicago, 2024 (IEEE, Piscataway, 2024), pp. 1027–1036.
- A. Ramkumar, Y. Cai, Y. Tong, and J. Jiang, High-temperature fermionic Gibbs states are mixtures of Gaussian states, arXiv:2505.09730.
- Handbook of Combinatorics, edited by R. L. Graham, M. Grötschel, and L. Lovász (Elsevier, Amsterdam, 1995), Vol. 2.