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Time series prediction of open quantum system dynamics
Quantum Physics · Submitted 12 Jan 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim These findings suggests that TSP is a valuable tool for the prediction of the dynamics in open quantum systems.
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The abstract, sentence by sentence
Time series prediction (TSP) has been widely used in various fields, such as life sciences and finance, to forecast future trends based on historical data. However, to date, there has been relatively little research conducted on the TSP for quantum physics. In this paper, we explore the potential application of TSP in forecasting the dynamical evolution of open quantum systems. We employ deep learning techniques to train a TSP model and evaluate its performance by comparison with exact solution. We use the ratio of the prediction step length and the sequence length to define short and long-term forecasting. Our results show that the trained model has the ability to effectively capture the inherent characteristics of time series for both short-term and long-term forecasting. Accurate predictions for different coupling intensities and initial states are obtained. Furthermore, we use our method to train another model and find that it can successfully predict the steady state of the system. These findings suggests that TSP is a valuable tool for the prediction of the dynamics in open quantum systems.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Time series prediction (TSP) has been widely used in various fields, such as life sciences and finance, to forecast future trends based on historical data. | background 1.00 | 0.03 | 0.01 | asserted 1.00 |
| s1 | However, to date, there has been relatively little research conducted on the TSP for quantum physics. | gap 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s2 | In this paper, we explore the potential application of TSP in forecasting the dynamical evolution of open quantum systems. | contribution 1.00 | 0.72 | 0.04 | hedged possibility 0.68 |
| s3 | We employ deep learning techniques to train a TSP model and evaluate its performance by comparison with exact solution. | method 0.98 | 0.96 | 0.03 | asserted 0.97 |
| s4 | We use the ratio of the prediction step length and the sequence length to define short and long-term forecasting. | method 0.99 | 0.91 | 0.02 | asserted 0.99 |
| s5 | Our results show that the trained model has the ability to effectively capture the inherent characteristics of time series for both short-term and long-term forecasting. | result 0.99 | 0.97 | 0.35 | asserted 0.97 |
| s6 | Accurate predictions for different coupling intensities and initial states are obtained. | result 1.00 | 0.94 | 0.09 | asserted 1.00 |
| s7 | Furthermore, we use our method to train another model and find that it can successfully predict the steady state of the system. | result 0.98 | 0.97 | 0.10 | asserted 1.00 |
| s8 | These findings suggests that TSP is a valuable tool for the prediction of the dynamics in open quantum systems. | interpretation 1.00 | 0.68 | 0.91 | hedged possibility 0.99 |
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The paper
Original abstract
Time series prediction (TSP) has been widely used in various fields, such as life sciences and finance, to forecast future trends based on historical data. However, to date, there has been relatively little research conducted on the TSP for quantum physics. In this paper, we explore the potential application of TSP in forecasting the dynamical evolution of open quantum systems. We employ deep learning techniques to train a TSP model and evaluate its performance by comparison with exact solution. We use the ratio of the prediction step length and the sequence length to define short and long-term forecasting. Our results show that the trained model has the ability to effectively capture the inherent characteristics of time series for both short-term and long-term forecasting. Accurate predictions for different coupling intensities and initial states are obtained. Furthermore, we use our method to train another model and find that it can successfully predict the steady state of the system. These findings suggests that TSP is a valuable tool for the prediction of the dynamics in open quantum systems.
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How the literature uses it
2 arXiv papers cite this in 2 sentences
Papers citing it
Cites it as background 2 papers
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Various architectures have been explored, such as physics-informed neural networks [ref] , deep quantum neural network [ref] , variation methods [ref] , autoregressive NN [ref] , time series prediction [ref] , and recurrent neural networks (RNN) [ref] .
Optimal control of open-quantum-system dynamics predicted by long short-term memory · Introduction · 0.92
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There has been a growing interest in predicting the dynamics of observables in quantum many-body systems using machine learning and related techniques [ref] .
Forecasting long-time dynamics in quantum many-body systems by dynamic mode decomposition · Introduction · 0.82
What it cites · 2 sentences without a confident label
Uses the method or tool of 1 paper
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The Mean Squared Error loss function is used, and the model parameters are updated using the Adam optimizer [ref] with a learning rate 0.00001.
Adam: A Method for Stochastic Optimization · TSP AND DEEP LEARNING MODEL · 1.00
Cites as background 3 papers
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Moreover, quantum physics plays a significant role in TSP [ref] .
Quantum Machine Learning in Finance: Time Series Forecasting · Introduction. · 0.93
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Moreover, quantum physics plays a significant role in TSP [ref] .
One-Step Time Series Forecasting Using Variational Quantum Circuits · Introduction. · 0.93
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It finds applications in various domains, including weather forecasting [ref] , stock market trend forecasting [ref] , solar power generation forecasting [ref] , and biological sequence data analysis [ref] .
Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting · Introduction. · 0.81
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