Projects | Bose Research Group

QuantumDynamicsCLI.jl

Tue, 11 Mar 2025 16:07:46 +0530

Documentation

What is QuantumDynamicsCLI?

Simulating the dynamics of quantum systems is a challenging task with a multitude of complicated computational methods. The QuantumDynamics.jl package provides modular open-source implementations of an increasingly growing number of these methods, while remaining a flexible platform for further development. However, owing primarily to its exceptionally flexible nature, the usage of QuantumDynamics.jl happens through short Julia scripts. This means that for the most common simulation jobs, one needs to effectively rewrite the same code multiple times increasing the chances of errors. As a means to making some of the common types of simulations more facile, we now offer the QuantumDynamicsCLI.jl package which installs the qdsim application as a sister code of the QuantumDynamics.jl package. As the framework grows, so will this application grow to accommodate the new methods and their most common use cases.

Installation

QuantumDynamicsCLI.jl is a registered package. Installation is a simple procedure. It can be done either through the Pkg REPL:

~ julia

julia> ]
pkg> add QuantumDynamicsCLI

or by using the Pkg package manager in a script as follows:

julia> using Pkg
julia> Pkg.add("QuantumDynamicsCLI")

After the package gets built, an executable called qdsim will be placed in $HOME/.julia/bin along with the code completions for the shell in $HOME/.julia/completions. Please add $HOME/.julia/bin to your path and source the correct completions file.

While QuantumDynamicsCLI.jl builds on top of the QuantumDynamics.jl package, separate installation of that package is unnecessary. Just installing QuantumDynamicsCLI.jl would install QuantumDynamics.jl as a dependency.

Basic Usage

qdsim comes as a single program with multiple sub-components. These components can call each other, but are mostly meant for the end-user, and are used for running simulations and post-processing the data. The general syntax for running any particular component is as follows:

> qdsim <component_name> <command_name> <arguments>

Currently, two commands are supported:

simulate: gives access to various techniques for simulating the dynamics
post: provides post-processing tools for the output

The most important sub-command of simulate is run, and for post is get-observable.

Types of Simulations

The primary focus of the qdsim application provided by the QuantumDynamicsCLI.jl package and the underlying QuantumDynamics.jl package is the simulation of dynamics and spectra of open quantum systems. New methods are consistently added and the support for old methods improved. Currently the following methods are supported:

Iterative Quasi-adiabatic Propagators Path Integral (iQuAPI)
Blip QuAPI
Time-Evolved Matrix Product Operators (TEMPO)
Pairwise-Connected Tensor Network Path Integral (PC-TNPI)
standard Hierarchical Equations of Motion (HEOM)
scaled HEOM
Transfer Tensor Method coupled with any of the path integral methods
Generalized Quantum Master Equation (GQME)
Multichromophore Incoherent Forster Theory
Bloch-Redfield Master Equation
Lindblad Master Equation

QuantumDynamics.jl

Fri, 05 May 2023 00:00:00 +0000

Documentation	Build Status	Citation

QuantumDynamics is an open-source software for the simulation of open quantum systems. Though written with performance in mind, QuantumDynamics provides a high throughput platform for experimentation with state-of-the-art approaches to method development.

The primary problem that QuantumDynamics is aimed at solving is the simulation of the dynamics of a relatively small quantum system coupled to a dissipative environment. Such a system-solvent decomposed problem can typically be represented by the Hamiltonian: $$\hat{H} = \hat{H}_0 + \hat{H}_\text{env}$$ where $\hat{H}_0$ is the Hamiltonian of the isolated system and $\hat{H}_\text{env}$ is the Hamiltonian corresponding to the environment and the interaction between the system and the environment.

As demonstrated in the tutorials and the example codes, QuantumDynamics provides some approximate methods for simulating the dynamics of the system. However, the goal of this package is to provide access to more state-of-the-art techniques based on path integrals, tensor networks and other ideas in such a manner that all of these methods can be used as far as possible in a composable manner.

Installation

The QuantumDynamics.jl package is registered. The installation can either be done by going into the Pkg REPL mode for Julia

~ julia

julia> ]
pkg> add QuantumDynamics

or by using the Pkg package manager in a script:

julia> using Pkg
julia> Pkg.add("QuantumDynamics")

This installs the latest stable release of QuantumDynamics.jl. Currently new features are being implemented quite regularly. The stable release may not always be up-to-date. Please add the bleeding edge release version to take advantage of the new features by adding the git repository:

~ julia

julia> ]
pkg> add https://github.com/amartyabose/QuantumDynamics.jl

Various parts of QuantumDynamics.jl depends on the BLAS and LAPACK libraries for efficient implementation of linear algebra routines. Julia generally uses OpenBLAS as a default implementation. The most common alternative is Intel’s Math Kernel Library (MKL), which can be used with QuantumDynamics.jl by first installing MKL.jl. In the actual script, MKL.jl should be loaded before loading QuantumDynamics.jl:

julia> using MKL
julia> using QuantumDynamics

Citation

If you use QuantumDynamics in your work, please cite the QuantumDynamics.jl paper:

@article{10.1063/5.0151483,
 author = {Bose, Amartya},
 title = "{QuantumDynamics.jl: A modular approach to simulations of dynamics of open quantum systems}",
 journal = {The Journal of Chemical Physics},
 volume = {158},
 number = {20},
 year = {2023},
 month = {05},
 abstract = "{A simulation of the non-adiabatic dynamics of a quantum system coupled to dissipative environments poses significant challenges. New sophisticated methods are regularly being developed with an eye toward moving to larger systems and more complicated descriptions 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 can be quite difficult as well. 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 systems. Most prominently, QuantumDynamics.jl supports hierarchical equations of motion and methods based on path integrals. An effort has been made to ensure maximum compatibility of the 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 the usage of Jupyter notebooks and high level plotting, the 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.}",
 issn = {0021-9606},
 doi = {10.1063/5.0151483},
 url = {https://doi.org/10.1063/5.0151483},
 note = {204113},
 eprint = {https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0151483/17794821/204113\_1\_5.0151483.pdf},
}

Multisite Tensor Network Path Integral

Fri, 04 Nov 2022 20:27:50 +0530

Simulating the dynamics of a quantum system coupled to a dissipative environment gets challenging because of the non-Markovian memory that needs to be accounted for. While iterative propagation and basic tensor network path integral (TNPI) enable simulation of longer memory times, the dimensionalities of systems that can be simulated are still very limited. For extended systems the dimensionality grows exponentially with the number of sites or monomers, making these techniques untenable. Consider $50$ monomers described by two levels each. That implies that the Hilbert space has a dimensionality of $2^{50}$. This exponential growth of the dimensionality of the Hilbert space may be controlled if the dynamics can be restricted to a significantly smaller subspace. This is the case when considering the single particle subspace in a Frenkel-like process which conserves the number of particles. However, solving the 50-dimensional problem, while more feasible than the $2^{50}$ dimensional problem, is still a significant challenge.

The standard TNPI decomposes the path integral expression only along the temporal dimension. However, to account for these extended systems, one needs to decompose it also along a spatial dimension, separating out the individual sites. Thus a two-dimensional tensor network decomposition is achieved. Contraction of this 2D tensor network after incorporation of the influence functional yields the time-dependent reduced density operator corresponding to the extended system in the presence of a dissipative environment. The simulations happen in the full Hilbert space allowing for a transparent inclusion of more involved effects like multi-photon process, multi-dimensional spectra, etc.

Exciton and Polaritonic Transport

Fri, 04 Nov 2022 17:04:49 +0530

Motivation

The transfer of electronic excitation — the movement of an exciton — is among the most fundamental processes in both nature and technology. In photosynthetic complexes, this process captures sunlight and channels energy with near-unity efficiency. In molecular crystals, conjugated polymers, and organic microcavities, similar mechanisms underlie the performance of optoelectronic and quantum photonic devices. Despite the diversity of these systems, they all share a central theme: energy flow occurs in a quantum mechanical world shaped by structure, coherence, and dissipation.

Our group studies how quantum coherence and environmental interactions organize the flow of excitation energy through complex molecular and hybrid systems. The goal is not only to quantify how efficiently energy moves, but to reveal how it moves — to uncover the competing routes of transport that emerge from the interplay of coherent delocalization, vibrational coupling, and environmental loss.

Conceptual Background

Excitonic transport lies in the fascinating regime between classical diffusion and fully coherent quantum motion. When the coupling between sites in a molecular aggregate is strong compared to environmental noise, excitations delocalize over multiple chromophores, giving rise to wave-like dynamics. Conversely, strong coupling to phonons or solvent fluctuations can localize the excitation and drive it toward incoherent hopping. Real systems — biological, chemical, and synthetic — exist somewhere in between, and the key challenge is to understand how coherence and dissipation cooperate to produce efficient transport.

Traditional theories such as Förster or Redfield describe these two limiting regimes separately. However, they often rely on perturbative or Markovian assumptions that fail in intermediate or strongly coupled regimes. To overcome this, we employ numerically exact open-system simulations based on path integrals, which treat system–environment coupling nonperturbatively and capture long-time memory effects. These methods are discussed in detail in Tensor Network Path Integral, where we developed scalable algorithms to evaluate the Feynman–Vernon influence functional for extended molecular systems.

Here, the focus is on how these rigorous dynamical tools can be used to map the routes of excitonic and polaritonic transport.

Tensor Network Path Integral

Fri, 04 Nov 2022 16:34:44 +0530

Simulations of real-time dynamics of quantum systems coupled with dissipative media is plagued by the curse of dimensionality. As a way to avoid the problem, many methods integrate out the bath and simulate the dynamics of the reduced density matrix. Such simulations are characterized by the presence of non-Markovian memory. Path integrals, through the use of Feynman-Vernon’s influence functional, provides a rigorous way of capturing this non-Markovian effect of the environment on the system.

The most challenging aspect is that the cost of these calculations grow exponentially with the memory length. We classify approaches that utilize tensor networks to make path integral simulations more efficient as belonging to the tensor network path integral (TNPI) family of methods. There can be many different kinds of tensor networks that are used. Time-evolved matrix product operators (TEMPO) is a particular one that uses matrix product states and matrix product operators for simulating real-time dynamics with Feynman-Vernon influence functional. We have developed a method called the pairwise-connected tensor network path integral (PC-TNPI) as a generalization on these approaches. Additionally, the TNPI framework allows for further factorization of the system to deal with extended systems. This extension, called the multisite tensor network path integral (MS-TNPI), is described in its own section.