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  • Open Access

Effective temperature in approximate quantum many-body states

Yu-Qin Chen1,* and Shi-Xin Zhang2,†

  • *Contact author: yqchen@gscaep.ac.cn
  • †Contact author: shixinzhang@iphy.ac.cn

Phys. Rev. Research 7, 033185 – Published 22 August, 2025

DOI: https://doi.org/10.1103/zpjv-bm5c

Abstract

In the pursuit of numerically identifying ground states of quantum many-body systems, approximate quantum wave function ansatzes are commonly employed. This study focuses on the spectral decomposition of these approximate quantum many-body states into exact eigenstates of the target Hamiltonian, which reflects the intricate physics at the interplay between quantum systems and numerical algorithms. Here, we examine various parametrized approximate quantum states constructed from neural networks, tensor networks, and quantum circuits, employing differentiable programming to numerically approximate ground states and imaginary-time evolved states. Our findings reveal a consistent exponential pattern in the energy eigenbasis decomposition contributions of approximate states across different ansatzes, optimization objectives, and quantum systems, characterized by remarkably small decay rates, i.e., high effective temperatures. This finding is counterintuitive for high-fidelity approximate ground states: While the total contribution from excited states can be made sufficiently small, the residual spectral weight does not decay rapidly with energy. This behavior is an intrinsic property of the variational ansatz, independent of the approximation’s overall accuracy. The effective temperature is related to ansatz expressiveness and accuracy and shows phase transition behaviors in learning imaginary-time evolved states. The universal picture and unique features suggest the significance and potential of the effective temperature metric in characterizing approximate quantum states.

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References (33)

  1. A. Kitaev, A. H. Shen, and M. N. Vyalyi, Classical and Quantum Computation (American Mathematical Society, Providence, RI, 2002).
  2. J. Kempe, A. Kitaev, and O. Regev, The complexity of the local Hamiltonian problem, SIAM J. Comput. 35, 1070 (2006).
  3. B.-X. Zheng, C.-M. Chung, P. Corboz, G. Ehlers, M.-P. Qin, R. M. Noack, H. Shi, S. R. White, S. Zhang, and G. K.-L. Chan, Stripe order in the underdoped region of the two-dimensional Hubbard model, Science 358, 1155 (2017).
  4. S. Lee, J. Lee, H. Zhai, Y. Tong, A. M. Dalzell, A. Kumar, P. Helms, J. Gray, Z.-H. Cui, W. Liu, M. Kastoryano, R. Babbush, J. Preskill, D. R. Reichman, E. T. Campbell, E. F. Valeev, L. Lin, and G. K.-L. Chan, Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry, Nat. Commun. 14, 1952 (2023).
  5. W. Greiner, L. Neise, and H. Stöcker, Thermodynamics and Statistical Mechanics (Springer, New York, NY, 1995).
  6. D. Wu, R. Rossi, F. Vicentini, N. Astrakhantsev, F. Becca, X. Cao, J. Carrasquilla, F. Ferrari, A. Georges, M. Hibat-Allah, M. Imada, A. M. Läuchli, G. Mazzola, A. Mezzacapo, A. Millis, J. Robledo Moreno, T. Neupert, Y. Nomura, J. Nys, O. Parcollet et al., Variational benchmarks for quantum many-body problems, Science 386, 296 (2024).
  7. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature (London) 323, 533 (1986).
  8. M. Bartholomew-Biggs, S. Brown, B. Christianson, and L. Dixon, Automatic differentiation of algorithms, J. Comput. Appl. Math. 124, 171 (2000).
  9. H.-J. Liao, J.-G. Liu, L. Wang, and T. Xiang, Differentiable programming tensor networks, Phys. Rev. X 9, 031041 (2019).
  10. S.-X. Zhang, Z.-Q. Wan, and H. Yao, Automatic differentiable Monte Carlo: Theory and application, Phys. Rev. Res. 5, 033041 (2023).
  11. S. R. White, Density matrix formulation for quantum renormalization groups, Phys. Rev. Lett. 69, 2863 (1992).
  12. U. Schollwöck, The density-matrix renormalization group in the age of matrix product states, Ann. Phys. 326, 96 (2011).
  13. E. Stoudenmire and S. R. White, Studying two-dimensional systems with the density matrix renormalization group, Annu. Rev. Condens. Matter Phys. 3, 111 (2012).
  14. F. Verstraete and J. I. Cirac, Renormalization algorithms for quantum-many body systems in two and higher dimensions, arXiv:cond-mat/0407066.
  15. F. Verstraete and J. I. Cirac, Valence-bond states for quantum computation, Phys. Rev. A 70, 060302 (2004).
  16. G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355, 602 (2017).
  17. D.-L. Deng, X. Li, and S. Das Sarma, Quantum entanglement in neural network states, Phys. Rev. X 7, 021021 (2017).
  18. M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, Variational quantum algorithms, Nat. Rev. Phys. 3, 625 (2021).
  19. K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, and A. Aspuru-Guzik, Noisy intermediate-scale quantum algorithms, Rev. Mod. Phys. 94, 015004 (2022).
  20. A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
  21. J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth, and J. Tennyson, The variational quantum eigensolver: A review of methods and best practices, Phys. Rep. 986, 1 (2022).
  22. E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv:1411.4028.
  23. See Supplemental Material at http://link.aps.org/supplemental/10.1103/zpjv-bm5c for an introduction to the different approximate quantum state ansatzes, the training dynamics towards ground state and imaginary-time evolved states, results from different optimization methods, a discussion on the observable consequences and scalable measurement of effective temperature, and the hyperparameters used for variational optimization.
  24. S.-X. Zhang, J. Allcock, Z.-Q. Wan, S. Liu, J. Sun, H. Yu, X.-H. Yang, J. Qiu, Z. Ye, Y.-Q. Chen, C.-K. Lee, Y.-C. Zheng, S.-K. Jian, H. Yao, C.-Y. Hsieh, and S. Zhang, TensorCircuit: A quantum software framework for the NISQ era, Quantum 7, 912 (2023).
  25. P. Weinberg and M. Bukov, QuSpin: A python package for dynamics and exact diagonalisation of quantum many body systems. Part I: Spin chains, SciPost Phys. 2, 003 (2017).
  26. S. Yunoki, Numerical study of the spin-flop transition in anisotropic spin-1/2 antiferromagnets, Phys. Rev. B 65, 092402 (2002).
  27. P. Díez-Valle, D. Porras, and J. J. García-Ripoll, Quantum approximate optimization algorithm pseudo-Boltzmann states, Phys. Rev. Lett. 130, 050601 (2023).
  28. P. C. Lotshaw, G. Siopsis, J. Ostrowski, R. Herrman, R. Alam, S. Powers, and T. S. Humble, Approximate Boltzmann distributions in quantum approximate optimization, Phys. Rev. A 108, 042411 (2023).
  29. J. M. Silvester, G. Carleo, and S. R. White, Unusual energy spectra of matrix product states, arXiv:2408.13616.
  30. H. Yokoyama and H. Shiba, Variational Monte-Carlo studies of Hubbard model. I, J. Phys. Soc. Jpn. 56, 1490 (1987).
  31. S.-X. Zhang, Z.-Q. Wan, C.-K. Lee, C.-Y. Hsieh, S. Zhang, and H. Yao, Variational quantum-neural hybrid eigensolver, Phys. Rev. Lett. 128, 120502 (2022).
  32. X. Yuan, J. Sun, J. Liu, Q. Zhao, and Y. Zhou, Quantum simulation with hybrid tensor networks, Phys. Rev. Lett. 127, 040501 (2021).
  33. https://github.com/sxzgroup/et.

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