Mitigating the sign problem by quantum computing

• URL: http://arxiv.org/abs/2509.13017v2
• Date: Wed, 17 Sep 2025 01:42:14 GMT
• Title: Mitigating the sign problem by quantum computing
• Authors: Kwai-Kong Ng, Min-Fong Yang,
• Abstract summary: The notorious sign problem severely limits the applicability of quantum Monte Carlo (QMC) simulations.<n>A recent proposal of a quantum-computing series expansion (qc-SSE) method suggested that the problem could be avoided by introducing constant energy shifts into the Hamiltonian.<n>Here we critically examine this framework and show that it does not strictly resolve the sign problem for Hamiltonians with non-commuting terms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The notorious sign problem severely limits the applicability of quantum Monte Carlo (QMC) simulations, as statistical errors grow exponentially with system size and inverse temperature. A recent proposal of a quantum-computing stochastic series expansion (qc-SSE) method suggested that the problem could be avoided by introducing constant energy shifts into the Hamiltonian. Here we critically examine this framework and show that it does not strictly resolve the sign problem for Hamiltonians with non-commuting terms. Instead, it provides a practical mitigation strategy that suppresses the occurrence of negative weights. Using the antiferromagnetic anisotropic XY chain as a test case, we analyze the dependence of the average sign on system size, temperature, anisotropy, and shift parameters. An operator contraction method is introduced to improve efficiency. Our results demonstrate that moderate shifts optimally balance sign mitigation and statistical accuracy, while large shifts amplify errors, leaving the sign problem unresolved but alleviated.

Related papers

• A scalable flow-based approach to mitigate topological freezing [34.54607280864912]
  We present a flow-based strategy to remove topological artifacts from Markov Chain Monte Carlo simulations.<n>The strategy is based on a Normalizing Flow (SNF) that alternates non-equilibrium Monte Carlo updates with localized, stout-equivariant defect layers.<n>We show that defect SNFs achieve better performances than reproducing non-equilibrium methods at comparable cost.
  arXiv  Detail & Related papers  (2026-01-28T15:40:46Z)
• Performance Guarantees for Quantum Neural Estimation of Entropies [31.955071410400947]
  Quantum neural estimators (QNEs) combine classical neural networks with parametrized quantum circuits.<n>We study formal guarantees for QNEs of measured relative entropies in the form of non-asymptotic error risk bounds.<n>Our theory aims to facilitate principled implementation of QNEs for measured relative entropies.
  arXiv  Detail & Related papers  (2025-11-24T16:36:06Z)
• Calibration of Quantum Devices via Robust Statistical Methods [45.464983015777314]
  We numerically analyze advanced statistical methods for Bayesian inference against the state-of-the-art in quantum parameter learning.<n>We show advantages of these approaches over existing ones, namely under multi-modality and high dimensionality.<n>Our findings have applications in challenging quantumcharacterization tasks namely learning the dynamics of open quantum systems.
  arXiv  Detail & Related papers  (2025-07-09T15:22:17Z)
• Quantum Annealing Algorithms for Estimating Ising Partition Functions [2.8311048083168657]
  Estimating partition functions of Ising spin glasses is crucial in statistical physics, optimization, and machine learning.<n>This work bridges quantum dynamics with computational complexity, offering a practical pathway to quantum advantage in spin glass thermodynamics.
  arXiv  Detail & Related papers  (2025-04-30T14:09:40Z)
• Low-temperature Quantum Metrology Enhanced by Strong Couplings [3.543616009111265]
  We propose a strategy to eliminate the error-divergence problem by utilizing the strong coupling effects.<n>Our result reveals the importance of the non-Markovianity in quantum technologies.
  arXiv  Detail & Related papers  (2024-12-02T01:22:08Z)
• Entanglement and operator correlation signatures of many-body quantum Zeno phases in inefficiently monitored noisy systems [49.1574468325115]
  The interplay between information-scrambling Hamiltonians and local continuous measurements hosts platforms for exotic measurement-induced phase transition. We identify a non-monotonic dependence on the local noise strength in both the averaged entanglement and operator correlations. The analysis of scaling with the system size in a finite length chain indicates that, at finite efficiency, this effect leads to distinct MiPTs for operator correlations and entanglement.
  arXiv  Detail & Related papers  (2024-07-16T13:42:38Z)
• Symmetry-protection Zeno phase transition in monitored lattice gauge theories [0.0]
  We show the existence of a sharp transition, triggered by the measurement rate, between a protected gauge-theory regime and an irregular regime.<n>Our results shed light on the dissipative criticality of strongly-interacting, highly-constrained quantum systems.
  arXiv  Detail & Related papers  (2024-05-28T18:18:06Z)
• Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems [49.1574468325115]
  We propose a physics-informed machine learning method for uncertainty quantification in high-dimensional inverse problems. We show analytically and through comparison with Hamiltonian Monte Carlo that the rPICKLE posterior converges to the true posterior given by the Bayes rule.
  arXiv  Detail & Related papers  (2023-12-11T07:33:16Z)
• Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer [27.84599956781646]
  We study the operation of a quantum-classical time-series algorithm on a present-day quantum computer. Specifically, we measure the Loschmidt amplitude for the Fermi-Hubbard model on a $16$-site ladder geometry (32 orbitals) on the Quantinuum H2-1 trapped-ion device. We numerically analyze the influence of noise on the full operation of the quantum-classical algorithm by measuring expectation values of local observables at finite energies.
  arXiv  Detail & Related papers  (2023-09-19T11:59:36Z)
• Macroscopic noise amplification by asymmetric dyads in non-Hermitian optical systems for generative diffusion models [55.2480439325792]
  asymmetric non-Hermitian dyads are promising candidates for efficient sensors and ultra-fast random number generators. integrated light emission from such asymmetric dyads can be efficiently used for all-optical degenerative diffusion models of machine learning.
  arXiv  Detail & Related papers  (2022-06-24T10:19:36Z)
• Lefschetz Thimble Quantum Monte Carlo for Spin Systems [0.0]
  We use Lefschetz thimbles to overcome the intrinsic sign problem in spin coherent state path integral Monte Carlo. We demonstrate its effectiveness at lessening the sign problem in this setting, despite the fact that the initial mapping to spin coherent states introduces its own sign problem.
  arXiv  Detail & Related papers  (2021-10-20T18:00:04Z)
• Stoquasticity in circuit QED [78.980148137396]
  We show that scalable sign-problem free path integral Monte Carlo simulations can typically be performed for such systems. We corroborate the recent finding that an effective, non-stoquastic qubit Hamiltonian can emerge in a system of capacitively coupled flux qubits.
  arXiv  Detail & Related papers  (2020-11-02T16:41:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.