Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A simple lower bound for the complexity of estimating partition functions on a quantum computer (2404.02414v2)

Published 3 Apr 2024 in quant-ph, cs.CC, cs.DS, math.ST, and stat.TH

Abstract: We study the complexity of estimating the partition function $\mathsf{Z}(\beta)=\sum_{x\in\chi} e{-\beta H(x)}$ for a Gibbs distribution characterized by the Hamiltonian $H(x)$. We provide a simple and natural lower bound for quantum algorithms that solve this task by relying on reflections through the coherent encoding of Gibbs states. Our primary contribution is a $\varOmega(1/\epsilon)$ lower bound for the number of reflections needed to estimate the partition function with a quantum algorithm. The proof is based on a reduction from the problem of estimating the Hamming weight of an unknown binary string.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (32)
  1. Simpler (classical) and faster (quantum) algorithms for gibbs partition functions. Quantum, 6:789, 2022.
  2. Euclidean gibbs states of quantum lattice systems. Reviews in Mathematical Physics, 14(12):1335–1401, 2002.
  3. Improved bounds for perfect sampling of k-colorings in graphs. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, pages 631–642, 2020.
  4. Quantum amplitude amplification and estimation. Contemporary Mathematics, 305:53–74, 2002.
  5. Accelerating simulated annealing for the permanent and combinatorial counting problems. SIAM Journal on Computing, 37(5):1429–1454, 2008.
  6. Quantum algorithm for estimating volumes of convex bodies. ACM Transactions on Quantum Computing, 4(3):1–60, 2023.
  7. A sublinear-time quantum algorithm for approximating partition functions. In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1245–1264. SIAM, 2023.
  8. Quantum algorithms for sampling log-concave distributions and estimating normalizing constants. Advances in Neural Information Processing Systems, 35:23205–23217, 2022.
  9. Computing partition functions in the one-clean-qubit model. Physical Review A, 103(3):032422, 2021.
  10. Computing the volume of convex bodies: a case where randomness provably helps. Probabilistic combinatorics and its applications, 44(123-170):0754–68052, 1991.
  11. Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, pages 579–586, 2020.
  12. Lov K Grover. A fast quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, pages 212–219, 1996.
  13. Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, pages 193–204, 2019.
  14. Quantum noise: a handbook of Markovian and non-Markovian quantum stochastic methods with applications to quantum optics. Springer Science & Business Media, 2004.
  15. Parameter estimation for gibbs distributions. arXiv preprint arXiv:2007.10824, 2020.
  16. Mark Huber. Approximation algorithms for the normalizing constant of gibbs distributions. The Annals of Applied Probability, pages 974–985, 2015.
  17. Adaptive quantum simulated annealing for bayesian inference and estimating partition functions. In Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 193–212. SIAM, 2020.
  18. Mark Jerrum. A very simple algorithm for estimating the number of k-colorings of a low-degree graph. Random Structures & Algorithms, 7(2):157–165, 1995.
  19. Vladimir Kolmogorov. A faster approximation algorithm for the gibbs partition function. In Conference On Learning Theory, pages 228–249. PMLR, 2018.
  20. Hamiltonian simulation by uniform spectral amplification. arXiv preprint arXiv:1707.05391, 2017.
  21. Ashley Montanaro. Quantum speedup of monte carlo methods. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 471(2181):20150301, 2015.
  22. Exact thresholds for ising–gibbs samplers on general graphs. 2013.
  23. The quantum query complexity of approximating the median and related statistics. In Proceedings of the thirty-first annual ACM symposium on Theory of computing, pages 384–393, 1999.
  24. Linda E Reichl. A modern course in statistical physics. John Wiley & Sons, 2016.
  25. Bin Shi. On the hyperparameters in stochastic gradient descent with momentum. arXiv preprint arXiv:2108.03947, 2021.
  26. On learning rates and schrödinger operators. arXiv preprint arXiv:2004.06977, 2020.
  27. Adaptive simulated annealing: A near-optimal connection between sampling and counting. Journal of the ACM (JACM), 56(3):1–36, 2009.
  28. Open quantum system dynamics and the mean force gibbs state. AVS Quantum Science, 4(1), 2022.
  29. Eric Vigoda. Improved bounds for sampling colorings. In 40th Annual Symposium on Foundations of Computer Science (Cat. No. 99CB37039), pages 51–59. IEEE, 1999.
  30. Matt Weinberg. Advanced algorithm design lecture 3: Concentration bounds, 2018.
  31. Scaling and diabatic effects in quantum annealing with a d-wave device. Physical Review Letters, 124(9):090502, 2020.
  32. Fixed-point quantum search with an optimal number of queries. Physical Review Letters, 113(21):210501, 2014.

Summary

We haven't generated a summary for this paper yet.