Non-Stationary Long-Term Dynamics via Selected Incomplete Dual Bases
- URL: http://arxiv.org/abs/2306.07407v2
- Date: Tue, 22 Oct 2024 17:00:36 GMT
- Title: Non-Stationary Long-Term Dynamics via Selected Incomplete Dual Bases
- Authors: Hsiao-Han Chuang, Abhijit Pendse,
- Abstract summary: We propose an SU(2) coherent state basis and deriving equations of motion for both time-independent and time-dependent Hamiltonian.
We evaluate this method through numerical simulations of a seven-qubit system.
Our conclusion suggests that the selected incomplete dual basis method can efficiently capture both short-term and long-term dynamics.
- Score: 0.0
- License:
- Abstract: Simulating the dynamics of non-stationary, long-term many-body quantum systems poses significant challenges due to the large size of the state space. We are examining how traditional basis sets struggle to accurately represent long-term dynamics when using incomplete sets. To address this issue, we propose using an SU(2) coherent state basis and deriving equations of motion for both time-independent and time-dependent Hamiltonian. This methodology involves a sampling approach, where a subset of relevant configurations is chosen based on energy criteria, and a projection method is used to enhance the accuracy of wavefunction propagation while reducing computational cost. We evaluate this method through numerical simulations of a seven-qubit system, calculating key physical observables such as state probabilities and domain-wall densities. Our results indicate that while complete basis sets offer accurate dynamics, selected incomplete sets can recover essential features, especially with the assistance of a projector. Our conclusion suggests that the selected incomplete dual basis method can efficiently capture both short-term and long-term dynamics.
Related papers
- Space and Time Continuous Physics Simulation From Partial Observations [0.0]
Data-driven methods based on large-scale machine learning promise high adaptivity by integrating long-range dependencies more directly and efficiently.
We focus on fluid dynamics and address the shortcomings of a large part of the literature, which are based on fixed support for computations and predictions in the form of regular or irregular grids.
We propose a novel setup to perform predictions in a continuous spatial and temporal domain while being trained on sparse observations.
arXiv Detail & Related papers (2024-01-17T13:24:04Z) - Neural Time-Reversed Generalized Riccati Equation [60.92253836775246]
Hamiltonian equations offer an interpretation of optimality through auxiliary variables known as costates.
This paper introduces a novel neural-based approach to optimal control, with the aim of working forward-in-time.
arXiv Detail & Related papers (2023-12-14T19:29:37Z) - Stochastic parameter optimization analysis of dynamical quantum critical phenomena in long-range transverse-field Ising chain [0.0]
We explore the quantum phase transition of the one-dimensional long-range transverse-field Ising model.
In our simulations, the simulator automatically determines the parameters to sample from, even without prior knowledge of the critical point and universality class.
We successfully obtained numerical evidence supporting $sigma = 7/4$ as the universality boundary between the latter two.
arXiv Detail & Related papers (2023-05-23T14:46:16Z) - Quantifying spatio-temporal patterns in classical and quantum systems
out of equilibrium [0.0]
A rich variety of non-equilibrium dynamical phenomena and processes unambiguously calls for the development of general numerical techniques.
By the example of the discrete time crystal realized in non-equilibrium quantum systems we provide a complete low-level characterization of this nontrivial dynamical phase with only processing bitstrings.
arXiv Detail & Related papers (2023-02-28T13:27:45Z) - A two stages Deep Learning Architecture for Model Reduction of
Parametric Time-Dependent Problems [0.0]
Parametric time-dependent systems are of crucial importance in modeling real phenomena.
We present a general two-stages deep learning framework able to perform that generalization with low computational effort in time.
Results are obtained applying the framework to incompressible Navier-Stokes equations in a cavity.
arXiv Detail & Related papers (2023-01-24T11:24:18Z) - Predicting the State of Synchronization of Financial Time Series using
Cross Recurrence Plots [75.20174445166997]
This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series.
We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state.
We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance.
arXiv Detail & Related papers (2022-10-26T10:22:28Z) - On optimization of coherent and incoherent controls for two-level
quantum systems [77.34726150561087]
This article considers some control problems for closed and open two-level quantum systems.
The closed system's dynamics is governed by the Schr"odinger equation with coherent control.
The open system's dynamics is governed by the Gorini-Kossakowski-Sudarshan-Lindblad master equation.
arXiv Detail & Related papers (2022-05-05T09:08:03Z) - Long-time integration of parametric evolution equations with
physics-informed DeepONets [0.0]
We introduce an effective framework for learning infinite-dimensional operators that map random initial conditions to associated PDE solutions within a short time interval.
Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model.
This introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations.
arXiv Detail & Related papers (2021-06-09T20:46:17Z) - Fast and differentiable simulation of driven quantum systems [58.720142291102135]
We introduce a semi-analytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical methods.
We show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
arXiv Detail & Related papers (2020-12-16T21:43:38Z) - Stochastically forced ensemble dynamic mode decomposition for
forecasting and analysis of near-periodic systems [65.44033635330604]
We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system.
We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony.
Results are presented for a test case using load data from an electrical grid.
arXiv Detail & Related papers (2020-10-08T20:25:52Z) - Supporting Optimal Phase Space Reconstructions Using Neural Network
Architecture for Time Series Modeling [68.8204255655161]
We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties.
Our approach is either as competitive as or better than most state-of-the-art strategies.
arXiv Detail & Related papers (2020-06-19T21:04:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.