Skip to main content

A package for the propagation of quantum-mechanical wave functions.

Project description

Description

Wavepacket is a Python package to define and simulate small quantum systems. Or, more technically, it allows you to numerically solve Schrödinger and Liouville-von-Neumann equations for distinguishable particles.

The full documentation can be found under https://wavepacket.readthedocs.io.

There are many different quantum systems and consequently approaches to solve them. Here we focus on a particular niche:

  • Wavepacket solves the differential equations directly. This simplifies the maths, but limits the system size to few degrees of freedom. If you want to deal with larger systems, look out for MCTDH.

  • Wavepacket uses the DVR approximation heavily. This allows you to directly define your potentials as functions of real-space coordinates instead of setting up opaque operator matrices. The latter approach is simpler an more concise if you are only interested in harmonic oscillators or qubits, though.

  • Wavepacket is a Python-only package relying chiefly on numpy. This is slower than natively implemented code, but you gain great tooling support, for example matplotlib, Jupyter notebooks or integrated documentation.

  • Most of the code can handle both wave functions and density operators. This allows you to convert a closed system into an open system with minimal fuss.

For example use cases, we have been using various precursors of this package for simulating small molecular systems and for teaching. Besides examples shipped with this package, see https://sourceforge.net/p/wavepacket/wiki/Demos.Main for more applications.

The project is currently a first iteration to flesh out everything. Once 0.1 is released, I plan to quickly translate the existing C++ code from a precursor project and reach a stable state. More can be found on the project homepage https://github.com/ulflor/wavepacket

Support

If you lack a feature that you would like to have, open an issue at our issue tracker. Depending on the complexity of the feature, this will lead to an immediate, rapid, or prioritized implementation.

Contribution

I currently lack a formal procedure for new contributors, but you are very welcome to contribute to the project. If you do not know what to do, feel free to contact one of the developers; there is enough work for multiple developers, it is just not documented yet.

History

The original version of Wavepacket was written in Matlab and is still maintained under https://sourceforge.net/p/wavepacket/matlab. It is stable, battle-tested and works.

However, Matlab is pretty expensive, so not all interested users had access to it. Also, the project’s architecture did not support some advanced use cases without digging deep into the code. Finally, C++11 had just come out and looked cool, so I started a reimplementation in C++ around 2013, adding Python bindings as an afterthought. The C++ project will be superseded by this Python package, but can be found under https://sourceforge.net/p/wavepacket/cpp.

This worked really well. However, deploying C++ code is difficult. In particular, there was no cheap route towards building a “good” Python package, or towards easily building a Windows version. Also, the underlying tensor library was slowly getting fewer and fewer commits over the years, so I am currently moving to a Python-only package. The Python version is slower by a factor of two to three compared to C++-backed code. This is, however, often cancelled by a parallelization of the tensor operations, and the tooling is better by orders of magnitude.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

wavepacket-0.3.0.tar.gz (88.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

wavepacket-0.3.0-py3-none-any.whl (51.7 kB view details)

Uploaded Python 3

File details

Details for the file wavepacket-0.3.0.tar.gz.

File metadata

  • Download URL: wavepacket-0.3.0.tar.gz
  • Upload date:
  • Size: 88.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.11

File hashes

Hashes for wavepacket-0.3.0.tar.gz
Algorithm Hash digest
SHA256 4d0ae5e64a747146c813ab7137899e49716969159d58276cb8f48823cf9e72d0
MD5 10123a231fe208442dc7674cce851689
BLAKE2b-256 6f1eb2b084a09df9875b2db8f7ef73478a6c375f30a7a645981946d96a012aad

See more details on using hashes here.

File details

Details for the file wavepacket-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: wavepacket-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 51.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.11

File hashes

Hashes for wavepacket-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 b8dd6490e8eadbc85e786450ae481e9d0d1af5aa3d8562c70bb162b4681cd89c
MD5 84d56ad2984788c717c2f6d051ef3a36
BLAKE2b-256 649d89b742c541dbfd7cf7feb5e70c84230b40d688c1e8a27f003f159aea3adf

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page