A new Monte Carlo method is proposed for fermion systems interacting with classical degrees of freedom. To obtain a weight for each Monte Carlo sample with a fixed configuration of classical variables, the moment expansion of the density of states by Chebyshev polynomials is applied instead of the direct diagonalization of the fermion Hamiltonian. This reduces a cpu time to scale as $O(N_{\rm dim}^{2} \log N_{\rm dim})$ compared to $O(N_{\rm dim}^{3})$ for the diagonalization in the conventional technique; $N_{\rm dim}$ is the dimension of the Hamiltonian. Another advantage of this method is that parallel computation with high efficiency is possible. These significantly save total cpu times of Monte Carlo calculations because the calculation of a Monte Carlo weight is the bottleneck part. The method is applied to the double-exchange model as an example. The benchmark results show that it is possible to make a systematic investigation using a system-size scaling even in three dimensions within a realistic cpu timescale.A new Monte Carlo method is proposed for fermion systems interacting with classical degrees of freedom. To obtain a weight for each Monte Carlo sample with a fixed configuration of classical variables, the moment expansion of the density of states by Chebyshev polynomials is applied instead of the direct diagonalization of the fermion Hamiltonian. This reduces a cpu time to scale as $O(N_{\rm dim}^{2} \log N_{\rm dim})$ compared to $O(N_{\rm dim}^{3})$ for the diagonalization in the conventional technique; $N_{\rm dim}$ is the dimension of the Hamiltonian. Another advantage of this method is that parallel computation with high efficiency is possible. These significantly save total cpu times of Monte Carlo calculations because the calculation of a Monte Carlo weight is the bottleneck part. The method is applied to the double-exchange model as an example. The benchmark results show that it is possible to make a systematic investigation using a system-size scaling even in three dimensions within a realistic cpu timescale.

arXiv:cond-mat/0007407Journal of the Physical Society of JapanDec 15, 199972 citations

OrderNMonte Carlo Algorithm for Fermion Systems Coupled with Fluctuating Adiabatical Fields

Nobuo Furukawa, Yukitoshi Motome

An improved algorithm is proposed for Monte Carlo methods to study fermion systems interacting with adiabatical fields. To obtain a weight for each Monte Carlo sample with a fixed configuration of adiabatical fields, a series expansion using Chebyshev polynomials is applied. By introducing truncations of matrix operations in a systematic and controlled way, it is shown that the cpu time is reduced from O(N^3) to O(N) where N is the system size. Benchmark results show that the implementation of the algorithm makes it possible to perform systematic investigations of critical phenomena using system-size scalings even for an electronic model in three dimensions, within a realistic cpu timescale.An improved algorithm is proposed for Monte Carlo methods to study fermion systems interacting with adiabatical fields. To obtain a weight for each Monte Carlo sample with a fixed configuration of adiabatical fields, a series expansion using Chebyshev polynomials is applied. By introducing truncations of matrix operations in a systematic and controlled way, it is shown that the cpu time is reduced from O(N^3) to O(N) where N is the system size. Benchmark results show that the implementation of the algorithm makes it possible to perform systematic investigations of critical phenomena using system-size scalings even for an electronic model in three dimensions, within a realistic cpu timescale.

arXiv:cond-mat/0308298Journal of the Physical Society of JapanJun 15, 200442 citations

Variational Monte Carlo

James E. Gubernatis, Naoki Kawashima, Philipp Werner

Cambridge University Press eBooksJan 31, 20161 citation

Inchworm Monte Carlo for exact non-adiabatic dynamics. II. Benchmarks and comparison with established methods

Hsing-Ta Chen, Guy Cohen, David R. Reichman

In this second paper of a two part series, we present extensive benchmark results for two different inchworm Monte Carlo expansions for the spin-boson model. Our results are compared to previously developed numerically exact approaches for this problem. A detailed discussion of convergence and error propagation is presented. Our results and analysis allow for an understanding of the benefits and drawbacks of inchworm Monte Carlo compared to other approaches for exact real-time non-adiabatic quantum dynamics.In this second paper of a two part series, we present extensive benchmark results for two different inchworm Monte Carlo expansions for the spin-boson model. Our results are compared to previously developed numerically exact approaches for this problem. A detailed discussion of convergence and error propagation is presented. Our results and analysis allow for an understanding of the benefits and drawbacks of inchworm Monte Carlo compared to other approaches for exact real-time non-adiabatic quantum dynamics.

arXiv:1610.09402The Journal of Chemical PhysicsFeb 1, 201766 citations

Efficient Quantum Monte Carlo through Cluster Expansions

Jorge Sánchez-Segovia, Álvaro M. Alhambra

The sign problem remains a major obstacle to extending the success of Monte Carlo methods to generic quantum many-body systems. A powerful alternative relies on cluster expansions, which provide convergent representations of free energies and related quantities, with applications ranging from tensor network contractions to quantum dynamics and circuits. Within their convergence regime, these expansions often yield deterministic polynomial-time algorithms. However, such methods require exhaustive enumeration of connected clusters, leading to a computational overhead of $(N\varepsilon^{-1})^{\mathcal{O}(\logΔ)}$ , where $N$ is the system size, $\varepsilon$ the desired precision, and $Δ$ the interaction-graph degree. This becomes particularly severe in long-range systems, where $Δ\equiv N$ , resulting in quasi-polynomial runtimes. Here, we introduce a Markov chain algorithm that samples cluster expansions directly in the space of connected clusters, replacing deterministic enumeration by Monte Carlo sampling. Because sampling occurs in an abstract cluster space rather than a physical configuration space, the conventional exponential sign problem is absent within the convergence regime. For general polymer models, we establish an FPRAS with runtime $\tilde{\mathcal{O}}(N^4\varepsilon^{-3})$ . For quantum partition functions at temperatures $β\leqβ_{\mathrm{sampling}}\equiv\mathcal{O}(1)$ , this improves to $\tilde{\mathcal{O}}(N^{3+1/D}\varepsilon^{-3})$ , where $D$ is the lattice dimension. The framework directly applies to long-range quantum systems with interactions $\propto1/r^α$ , including $2$ -local models with $α>2D$ .The sign problem remains a major obstacle to extending the success of Monte Carlo methods to generic quantum many-body systems. A powerful alternative relies on cluster expansions, which provide convergent representations of free energies and related quantities, with applications ranging from tensor network contractions to quantum dynamics and circuits. Within their convergence regime, these expansions often yield deterministic polynomial-time algorithms. However, such methods require exhaustive enumeration of connected clusters, leading to a computational overhead of $(N\varepsilon^{-1})^{\mathcal{O}(\logΔ)}$, where $N$ is the system size, $\varepsilon$ the desired precision, and $Δ$ the interaction-graph degree. This becomes particularly severe in long-range systems, where $Δ\equiv N$, resulting in quasi-polynomial runtimes. Here, we introduce a Markov chain algorithm that samples cluster expansions directly in the space of connected clusters, replacing deterministic enumeration by Monte Carlo sampling. Because sampling occurs in an abstract cluster space rather than a physical configuration space, the conventional exponential sign problem is absent within the convergence regime. For general polymer models, we establish an FPRAS with runtime $\tilde{\mathcal{O}}(N^4\varepsilon^{-3})$. For quantum partition functions at temperatures $β\leqβ_{\mathrm{sampling}}\equiv\mathcal{O}(1)$, this improves to $\tilde{\mathcal{O}}(N^{3+1/D}\varepsilon^{-3})$, where $D$ is the lattice dimension. The framework directly applies to long-range quantum systems with interactions $\propto1/r^α$, including $2$-local models with $α>2D$.

quant-pharXiv:2610.03893Oct 6, 20260 citations

O(N_{\rm dim}^{2} \log N_{\rm dim})

compared to

O(N_{\rm dim}^{3})

for the diagonalization in the conventional technique;

N_{\rm dim}

is the dimension of the Hamiltonian. Another advantage of this method is that parallel computation with high efficiency is possible. These significantly save total cpu times of Monte Carlo calculations because the calculation of a Monte Carlo weight is the bottleneck part. The method is applied to the double-exchange model as an example. The benchmark results show that it is possible to make a systematic investigation using a system-size scaling even in three dimensions within a realistic cpu timescale.

(N\varepsilon^{-1})^{\mathcal{O}(\logΔ)}

, where

N

is the system size,

\varepsilon

the desired precision, and

Δ

the interaction-graph degree. This becomes particularly severe in long-range systems, where

Δ\equiv N

, resulting in quasi-polynomial runtimes. Here, we introduce a Markov chain algorithm that samples cluster expansions directly in the space of connected clusters, replacing deterministic enumeration by Monte Carlo sampling. Because sampling occurs in an abstract cluster space rather than a physical configuration space, the conventional exponential sign problem is absent within the convergence regime. For general polymer models, we establish an FPRAS with runtime

\tilde{\mathcal{O}}(N^4\varepsilon^{-3})

. For quantum partition functions at temperatures

β\leqβ_{\mathrm{sampling}}\equiv\mathcal{O}(1)

, this improves to

\tilde{\mathcal{O}}(N^{3+1/D}\varepsilon^{-3})

, where

D

is the lattice dimension. The framework directly applies to long-range quantum systems with interactions

\propto1/r^α

, including

2

-local models with

α>2D