Constructing tensor network influence functionals for general quantum dynamics
- Creators
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Ye, Erika
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Chan, Garnet Kin-Lic
Abstract
We describe an iterative formalism to compute influence functionals that describe the general quantum dynamics of a subsystem beyond the assumption of linear coupling to a quadratic bath. We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of its bond dimension and time-like entanglement in the tensor network description. We study two numerical models, the spin-boson model and a model of interacting hard-core bosons in a 1D harmonic trap. We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes, which allows the quantum dynamics to be accurately computed for longer times than with direct time evolution methods. However, as one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.
Additional Information
© 2021 Published under an exclusive license by AIP Publishing. Submitted: 11 February 2021; Accepted: 6 July 2021; Published Online: 26 July 2021. G.K.C. was supported by the Center for Molecular Magnetic Quantum Materials, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0019330. E.Y. was primarily supported by the Google PhD fellowship program with supplemental salary support from the Center for Molecular Magnetic Quantum Materials. Support for industrial mentorship for E.Y. was provided via the QISE-NET program, funded via NSF Award No. DMR-1747426.Attached Files
Published - 5.0047260.pdf
Submitted - 2101.05466.pdf
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Additional details
- Eprint ID
- 108450
- Resolver ID
- CaltechAUTHORS:20210316-101643523
- Department of Energy (DOE)
- DE-SC0019330
- Google PhD Fellowship
- NSF
- DMR-1747426
- Created
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2021-03-19Created from EPrint's datestamp field
- Updated
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2022-07-05Created from EPrint's last_modified field