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This paper proposes Liouvillian spectral snapshots, a reduced-basis method for simulating driven-dissipative quantum systems under time-dependent drives and temporally correlated noise. The approach builds a low-dimensional subspace once and reuses it across noise realizations and parameter sweeps, which could cut the cost of long integrations and ensemble averages. For quantum hardware modelers, the key question is how well the method holds up on realistic devices beyond the abstract's claims.
Liouvillian spectral snapshots: A reduced-subspace approach for time-dependent and noisy open quantum systems
arXiv:2610.40808v1 Announce Type: new Abstract: Driven-dissipative quantum devices are routinely operated with time-dependent drives and exposed to temporally correlated classical noise. When these perturbations vary on timescales comparable to the system's intrinsic dynamics, rotating-wave and Markovian approximations can fail, making effective static descriptions unreliable. As a result, accurately modeling systems in this regime often relies on simulating the full time-dependent master equation. This is particularly demanding for processes requiring long integration times, such as rare switching between metastable states, or for stochastic perturbations requiring averaging over many noise realizations. To address these challenges, we introduce Liouvillian spectral snapshots (LSS), a reduced-basis method that enables efficient simulation of such systems by projecting the master equation onto a low-dimensional subspace capturing their coarse-grained dynamics. LSS constructs this subspace by computing the slowly decaying, low-frequency eigenoperators of the Liouvillian at a few adaptively sampled perturbation values and compressing them into a common basis. This basis is built once and can be reused across noise realizations and perturbation parameters, reducing the cost of ensemble averages and parameter sweeps.
Abstract:Driven-dissipative quantum devices are routinely operated with time-dependent drives and exposed to temporally correlated classical noise. When these perturbations vary on timescales comparable to the system's intrinsic dynamics, rotating-wave and Markovian approximations can fail, making effective static descriptions unreliable. As a result, accurately modeling systems in this regime often relies on simulating the full time-dependent master equation. This is particularly demanding for processes requiring long integration times, such as rare switching between metastable states, or for stochastic perturbations requiring averaging over many noise realizations. To address these challenges, we introduce Liouvillian spectral snapshots (LSS), a reduced-basis method that enables efficient simulation of such systems by projecting the master equation onto a low-dimensional subspace capturing their coarse-grained dynamics. LSS constructs this subspace by computing the slowly decaying, low-frequency eigenoperators of the Liouvillian at a few adaptively sampled perturbation values and compressing them into a common basis. This basis is built once and can be reused across noise realizations and perturbation parameters, reducing the cost of ensemble averages and parameter sweeps.
arXiv:2610.40808 [quant-ph]
(or arXiv:2610.40808v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.40808 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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