Forecasting long-time dynamics in quantum many-body systems by dynamic mode decomposition

• URL: http://arxiv.org/abs/2403.19947v2
• Date: Thu, 23 Jan 2025 15:52:33 GMT
• Title: Forecasting long-time dynamics in quantum many-body systems by dynamic mode decomposition
• Authors: Ryui Kaneko, Masatoshi Imada, Yoshiyuki Kabashima, Tomi Ohtsuki,
• Abstract summary: We use short-time data of physical quantities to forecast long-time behavior of strongly entangled systems.<n>The method, though simple, enables accurate forecasts amazingly at time as long as nearly an order of magnitude longer than that of the short-time training data.
• Score: 6.381013699474244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable numerical computation of quantum dynamics is a fundamental challenge when the long-ranged quantum entanglement plays essential roles as in the cases governed by quantum criticality in strongly correlated systems. Here we apply a method that utilizes reliable short-time data of physical quantities to accurately forecast long-time behavior of the strongly entangled systems. We straightforwardly employ the simple dynamic mode decomposition (DMD), which is commonly used in fluid dynamics. Despite the simplicity of the method, the effectiveness and applicability of the DMD in quantum many-body systems such as the Ising model in the transverse field at the critical point are demonstrated, even when the time evolution at long time exhibits complicated features such as a volume-law entanglement entropy and consequential power-law decays of correlations characteristic of systems with long-ranged quantum entanglements unlike fluid dynamics. The present method, though simple, enables accurate forecasts amazingly at time as long as nearly an order of magnitude longer than that of the short-time training data. Effects of noise on the accuracy of the forecast are also investigated, because they are important especially when dealing with the experimental data. We find that a few percentages of noise do not affect the prediction accuracy destructively.

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