QuantumDynamics.jl: A modular approach to simulations of dynamics of
open quantum systems
- URL: http://arxiv.org/abs/2303.11781v1
- Date: Tue, 21 Mar 2023 11:57:13 GMT
- Title: QuantumDynamics.jl: A modular approach to simulations of dynamics of
open quantum systems
- Authors: Amartya Bose
- Abstract summary: We present a new, open-source software framework, QuantumDynamics.jl.
It provides implementations of a variety of perturbative and non-perturbative methods for simulating the dynamics of quantum systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Simulation of non-adiabatic dynamics of a quantum system coupled to
dissipative environments poses significant challenges. New sophisticated
methods are regularly being developed with an eye towards moving to larger
systems and more complicated description of solvents. Many of these methods,
however, are quite difficult to implement and debug. Furthermore, trying to
make the individual algorithms work together through a modular application
programming interface (API) can be quite difficult. We present a new,
open-source software framework, QuantumDynamics.jl, designed to address these
challenges. It provides implementations of a variety of perturbative and
non-perturbative methods for simulating the dynamics of these sytems. Most
prominently, QuantumDynamics.jl supports hierarchical equations of motion and
the family of methods based on path integrals. Effort has been made to ensure
maximum compatibility of interface between the various methods. Additionally,
QuantumDynamics.jl, being built on a high-level programming language, brings a
host of modern features to explorations of systems such as usage of Jupyter
notebooks and high level plotting for exploration of systems, possibility of
leveraging high-performance machine learning libraries for further development.
Thus, while the built-in methods can be used as end-points in themselves, the
package provides an integrated platform for experimentation, exploration, and
method development.
Related papers
- Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers [0.0]
We develop a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware.
We demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems.
arXiv Detail & Related papers (2024-03-13T13:27:40Z) - Non-Markovian Quantum Control via Model Maximum Likelihood Estimation
and Reinforcement Learning [0.0]
We propose a novel approach that incorporates the non-Markovian nature of the environment into a low-dimensional effective reservoir.
We utilize machine learning techniques to learn the effective quantum dynamics more efficiently than traditional tomographic methods.
This approach may not only mitigates the issues of model bias but also provides a more accurate representation of quantum dynamics.
arXiv Detail & Related papers (2024-02-07T18:37:17Z) - Machine Learning Assisted Cognitive Construction of a Shallow Depth
Dynamic Ansatz for Noisy Quantum Hardware [0.0]
We develop a novel protocol that capitalizes on regenerative machine learning methodologies and many-body theoretic measures to construct a highly expressive and shallow ansatz.
The proposed method is highly compatible with state-of-the-art neural error mitigation techniques.
arXiv Detail & Related papers (2023-10-12T16:27:53Z) - Interpretable learning of effective dynamics for multiscale systems [5.754251195342313]
We propose a novel framework of Interpretable Learning Effective Dynamics (iLED)
iLED offers comparable accuracy to state-of-theart recurrent neural network-based approaches.
Our results show that the iLED framework can generate accurate predictions and obtain interpretable dynamics.
arXiv Detail & Related papers (2023-09-11T20:29:38Z) - Learning minimal representations of stochastic processes with
variational autoencoders [52.99137594502433]
We introduce an unsupervised machine learning approach to determine the minimal set of parameters required to describe a process.
Our approach enables for the autonomous discovery of unknown parameters describing processes.
arXiv Detail & Related papers (2023-07-21T14:25:06Z) - FluidLab: A Differentiable Environment for Benchmarking Complex Fluid
Manipulation [80.63838153351804]
We introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics.
At the heart of our platform is a fully differentiable physics simulator, providing GPU-accelerated simulations and gradient calculations.
We propose several domain-specific optimization schemes coupled with differentiable physics.
arXiv Detail & Related papers (2023-03-04T07:24:22Z) - Guaranteed Conservation of Momentum for Learning Particle-based Fluid
Dynamics [96.9177297872723]
We present a novel method for guaranteeing linear momentum in learned physics simulations.
We enforce conservation of momentum with a hard constraint, which we realize via antisymmetrical continuous convolutional layers.
In combination, the proposed method allows us to increase the physical accuracy of the learned simulator substantially.
arXiv Detail & Related papers (2022-10-12T09:12:59Z) - Engineering dynamically decoupled quantum simulations with trapped ions [0.0]
An external drive can improve the coherence of a quantum many-body system by averaging out noise sources.
It can also be used to realize models that are inaccessible in the static limit.
We develop the requirements needed for a pulse sequence to decouple a quantum many-body system from an external field.
arXiv Detail & Related papers (2022-09-12T18:01:05Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations.
Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff.
For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
arXiv Detail & Related papers (2021-09-07T20:01:22Z) - PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable
Physics [89.81550748680245]
We introduce a new differentiable physics benchmark called PasticineLab.
In each task, the agent uses manipulators to deform the plasticine into the desired configuration.
We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark.
arXiv Detail & Related papers (2021-04-07T17:59:23Z)
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.