Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Journal of Statistical Physics

Statistical and systematic errors in Monte Carlo sampling

Alan M. Ferrenberg, David Paul Landau, Kurt Binder

Published Jun 1, 1991• 97 citations
Publisher
DOI
10.1007/bf01029988

Related papers

Quantum advantage of Monte Carlo option pricing

Zoltán Udvarnoki, Gábor Fáth, Norbert Fogarasi

Abstract Quantum computers have the potential to provide quadratic speedup for Monte Carlo methods currently used in various classical applications. In this work, we examine the advantage of quantum computers for financial option pricing with the Monte Carlo method. Systematic and statistical errors are handled in a joint framework, and a relationship to quantum gate error is established. New metrics are introduced for the assessment of quantum advantage based on sample count and optimized error handling. We implement and analyze a Fourier series based approach and demonstrate its benefit over the more traditional rescaling method in function approximation. Our numerical calculations reveal the unpredictable nature of systematic errors, making consistent quantum advantage difficult with current quantum hardware. Our results indicate that very low noise levels, a two-qubit gate error rate below 10−6, are necessary for the quantum method to outperform the classical one, but a low number of logical qubits (ca. 20) may be sufficient to see quantum advantage already.

Journal of Physics Communications•May 1, 2023•9 citations

Extending Monte Carlo samples

Bernard B. Beard

Nuclear Physics B - Proceedings Supplements•May 1, 2003•1 citation

Bernstein Numbers and Lower Bounds for the Monte Carlo Error

Robert J. Kunsch

Springer proceedings in mathematics & statistics•Jan 1, 2016•6 citations

Beyond Walkers in Stochastic Quantum Chemistry: Reducing Error Using Fast Randomized Iteration.

Samuel M. Greene, Robert J. Webber, Jonathan Weare +1 more

, 547; DOI: 10.1137/15M1040827 ]. These methods, which we term "FCI-FRI", stochastically impose sparsity during iterations of the power method and can be viewed as a generalization of full configuration interaction quantum Monte Carlo (FCIQMC) without walkers. In addition to the multinomial scheme commonly used to sample excitations in FCIQMC, we present a systematic scheme where excitations are not sampled independently. Performing ground-state calculations on five small molecules at fixed cost, we find that the systematic FCI-FRI scheme is 11-45 times more statistically efficient than the multinomial FCI-FRI scheme, which is in turn 1.4-178 times more statistically efficient than the original FCIQMC algorithm.

•Sep 10, 2019•23 citations

Beyond Walkers in Stochastic Quantum Chemistry: Reducing Error Using Fast Randomized Iteration

Samuel M. Greene, Robert J. Webber, Jonathan Weare +1 more

We introduce a family of methods for the full configuration interaction problem in quantum chemistry, based on the fast randomized iteration (FRI) framework [Lim, L.-H.; Weare, J. SIAM Rev. 2017, 59, 547; DOI: 10.1137/15M1040827 ]. These methods, which we term “FCI-FRI”, stochastically impose sparsity during iterations of the power method and can be viewed as a generalization of full configuration interaction quantum Monte Carlo (FCIQMC) without walkers. In addition to the multinomial scheme commonly used to sample excitations in FCIQMC, we present a systematic scheme where excitations are not sampled independently. Performing ground-state calculations on five small molecules at fixed cost, we find that the systematic FCI-FRI scheme is 11–45 times more statistically efficient than the multinomial FCI-FRI scheme, which is in turn 1.4–178 times more statistically efficient than the original FCIQMC algorithm.

arXiv:1905.00995•Journal of Chemical Theory and Computation•Aug 7, 2019•5 citations

Optimization of Monte Carlo simulations

Hans-Otto Heuer

Physica A Statistical Mechanics and its Applications•Apr 1, 1992•2 citations
Abstract Quantum computers have the potential to provide quadratic speedup for Monte Carlo methods currently used in various classical applications. In this work, we examine the advantage of quantum computers for financial option pricing with the Monte Carlo method. Systematic and statistical errors are handled in a joint framework, and a relationship to quantum gate error is established. New metrics are introduced for the assessment of quantum advantage based on sample count and optimized error handling. We implement and analyze a Fourier series based approach and demonstrate its benefit over the more traditional rescaling method in function approximation. Our numerical calculations reveal the unpredictable nature of systematic errors, making consistent quantum advantage difficult with current quantum hardware. Our results indicate that very low noise levels, a two-qubit gate error rate below 10−6, are necessary for the quantum method to outperform the classical one, but a low number of logical qubits (ca. 20) may be sufficient to see quantum advantage already.
, 547; DOI: 10.1137/15M1040827 ]. These methods, which we term "FCI-FRI", stochastically impose sparsity during iterations of the power method and can be viewed as a generalization of full configuration interaction quantum Monte Carlo (FCIQMC) without walkers. In addition to the multinomial scheme commonly used to sample excitations in FCIQMC, we present a systematic scheme where excitations are not sampled independently. Performing ground-state calculations on five small molecules at fixed cost, we find that the systematic FCI-FRI scheme is 11-45 times more statistically efficient than the multinomial FCI-FRI scheme, which is in turn 1.4-178 times more statistically efficient than the original FCIQMC algorithm.
We introduce a family of methods for the full configuration interaction problem in quantum chemistry, based on the fast randomized iteration (FRI) framework [Lim, L.-H.; Weare, J. SIAM Rev. 2017, 59, 547; DOI: 10.1137/15M1040827 ]. These methods, which we term “FCI-FRI”, stochastically impose sparsity during iterations of the power method and can be viewed as a generalization of full configuration interaction quantum Monte Carlo (FCIQMC) without walkers. In addition to the multinomial scheme commonly used to sample excitations in FCIQMC, we present a systematic scheme where excitations are not sampled independently. Performing ground-state calculations on five small molecules at fixed cost, we find that the systematic FCI-FRI scheme is 11–45 times more statistically efficient than the multinomial FCI-FRI scheme, which is in turn 1.4–178 times more statistically efficient than the original FCIQMC algorithm.