Efficient formulation of multitime generalized quantum master equations: Taming the cost of simulating 2D spectra

• URL: http://arxiv.org/abs/2310.20022v1
• Date: Mon, 30 Oct 2023 21:16:04 GMT
• Title: Efficient formulation of multitime generalized quantum master equations: Taming the cost of simulating 2D spectra
• Authors: Thomas Sayer and Andr\'es Montoya-Castillo
• Abstract summary: We present a formulation that greatly simplifies and reduces the computational cost of previous work that extended the GQME framework. Specifically, we remove the time derivatives of quantum correlation functions from the modified Mori-Nakajima-Zwanzig framework. We are also able to decompose the spectra into 1-, 2-, and 3-time correlations, showing how and when the system enters a Markovian regime.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern 4-wave mixing spectroscopies are expensive to obtain experimentally and computationally. In certain cases, the unfavorable scaling of quantum dynamics problems can be improved using a generalized quantum master equation (GQME) approach. However, the inclusion of multiple (light-matter) interactions complicates the equation of motion and leads to seemingly unavoidable cubic scaling in time. In this paper, we present a formulation that greatly simplifies and reduces the computational cost of previous work that extended the GQME framework to treat arbitrary numbers of quantum measurements. Specifically, we remove the time derivatives of quantum correlation functions from the modified Mori-Nakajima-Zwanzig framework by switching to a discrete-convolution implementation inspired by the transfer-tensor approach. We then demonstrate the method's capabilities by simulating 2D electronic spectra for the excitation-energy-transfer dimer model. In our method, the resolution of the data can be arbitrarily coarsened, especially along the $t_2$ axis, which mirrors how the data are obtained experimentally. Even in a modest case, this demands $\mathcal{O}(10^3)$ fewer data points. We are further able to decompose the spectra into 1-, 2-, and 3-time correlations, showing how and when the system enters a Markovian regime where further measurements are unnecessary to predict future spectra and the scaling becomes quadratic. This offers the ability to generate long-time spectra using only short-time data, enabling access to timescales previously beyond the reach of standard methodologies.

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