Accelerated Inchworm Method with Tensor-Train Bath Influence Functional

Geshuo Wang

doi:10.1016/j.cpc.2026.110164

Accelerated Inchworm Method with Tensor-Train Bath Influence Functional

2026-04-15

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DOI：10.1016/j.cpc.2026.110164

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We propose an efficient tensor-train-based algorithm for simulating open quantum systems with the inchworm method, where the reduced dynamics of the open quantum system is expressed as a perturbative series of high-dimensional integrals. Instead of evaluating the integrals with Monte Carlo methods, we approximate the costly bath influence functional (BIF) in the integrand as a tensor train, allowing accurate deterministic numerical quadrature schemes implemented in an iterative manner. Thanks to the low-rank structure of the tensor train, our proposed method has a complexity that scales linearly with the number of dimensions. Our method couples seamlessly with the tensor transfer method, allowing long-time simulations of the dynamics.

Keywords:

Tensor train

Inchworm method

Open quantum systems

Bath influence functional

Numerical quadrature

Accelerated Inchworm Method with Tensor-Train Bath Influence Functional

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