wavepacket

We have hosted the application wavepacket in order to run this application in our online workstations with Wine or directly.

Quick description about wavepacket:

WavePacket is a program package for numerical quantum-mechanics of distinguishable particles. It can be used to solve single or coupled time-independent or time-dependent (linear) Schr�dinger and Liouville-von Neumann-equations, as well as classical or quantum-classical Liouville equations. Optionally accounting for the interaction with external electric fields within the semiclassical dipole approximation, WavePacket can be used to simulate modern experiments involving ultrashort light pulses in photo-induced physics or chemistry, including quantum optimal control. With its visualization of quantum dynamics generated 'on the fly', WavePacket is suitable for teaching quantum mechanics as well as for research projects, see also the numerous demonstration examples .

While the Matlab/Octave version is already in a mature state, the C++/Python version is still under construction. Using tensor train techniques, the additional WaveTrain package aims at beating the curse of dimensionality.

Features:

• Time-independent Schr�dinger equation
• Time-dependent Schr�dinger equation
• Liouville-von Neumann equation (Lindblad dissipation)
• Quantum-classical dynamics (surface hopping trajectories)
• Optimal control by external fields (semiclassical)
• Dimension reduction by balanced truncation
• Comes with extensive Wiki documentation
• Many demonstration examples with animated graphics
• Matlab/Octave version: Version 7.1.0 released on 02-Mar-2023
• C++/Python version: Version 0.3.4 released on 09-Jan-2022
• WaveTrain (Python version): Version 1.0.7 released on 11-Feb-2023
• See our first WavePacket publication: https: //doi.org/10.1016/j.cpc.2016.12.007
• See our second WavePacket publication: https: //doi.org/10.1016/j.cpc.2018.02.022
• See our third WavePacket publication: https: //doi.org/10.1002/JCC.26045
• See our WaveTrain publication: https: //arxiv.org/abs/2302.03725

Audience: Science/Research, Advanced End Users.

Programming Language: MATLAB, Python, C++.
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