PyTeNet
PyTeNet is a Python implementation of quantum tensor network operations and simulations within the matrix product state framework, using NumPy to handle tensors.
Example usage for TDVP time evolution:
import pytenet as ptn
# number of lattice sites (1D with open boundary conditions)
nsites = 10
# construct matrix product operator representation of the
# Heisenberg XXZ Hamiltonian (arguments are nsites, J, \Delta, h)
h_mpo = ptn.heisenberg_xxz_1d_mpo(nsites, 1.0, 0.8, -0.1)
h_mpo.zero_qnumbers()
# initial wavefunction as MPS with random entries
# maximally allowed virtual bond dimensions
b = [1, 2, 4, 8, 16, 28, 16, 8, 4, 2, 1]
psi = ptn.MPS(h_mpo.qsite, [bi*[0] for bi in b], fill="random")
# effectively clamp virtual bond dimension of initial state
b_init = 8
for i in range(nsites):
psi.a[i][:, b_init:, :] = 0
psi.a[i][:, :, b_init:] = 0
psi.orthonormalize(mode="left")
# time step can have both real and imaginary parts;
# for real time evolution use purely imaginary dt!
dt = 0.01 - 0.05j
numsteps = 100
# run TDVP time evolution
ptn.tdvp_singlesite(h_mpo, psi, dt, numsteps, numiter_lanczos=5)
# psi now stores the (approximated) time-evolved state exp(-dt*numsteps H) psiFeatures
matrix product state and operator classes
construct common Hamiltonians as MPOs, straightforward to adapt to custom Hamiltonians
convert arbitrary operator chains to MPOs
TDVP time evolution (single- and two-site, both real and imaginary time)
generate vector / matrix representations of matrix product states / operators
Krylov subspace methods for local operations, like local energy minimization
single- and two-site DMRG algorithm
built-in support for additive quantum numbers
Installation
To install PyTeNet from PyPI, call
python3 -m pip install pytenetAlternatively, you can clone the repository and install it in development mode via
python3 -m pip install -e <path/to/repo>Documentation
The full documentation is available at pytenet.readthedocs.io.
Directory structure
pytenet: source code of the actual PyTeNet package
doc: documentation and tutorials
test: unit tests (might serve as detailed documentation, too)
experiments: numerical experiments on more advanced, in-depth topics
paper: JOSS manuscript
Contributing
Feature requests, discussions and code contributions to PyTeNet in the form of pull requests are of course welcome. Creating an issue might be a good starting point. New code should be well documented (Google style docstrings) and unit-tested (see the test/ subfolder). For questions and additional support, fell free to contact christian.b.mendl@gmail.com
Citing
PyTeNet is published in the Journal of Open Source Software - if it’s ever useful for a research project please consider citing it:
@Article{pytenet,
author = {Mendl, Christian B.},
title = {{PyTeNet}: A concise {P}ython implementation of quantum tensor network algorithms},
journal = {Journal of Open Source Software},
year = {2018},
volume = {3},
number = {30},
pages = {948},
doi = {10.21105/joss.00948},
}License
PyTeNet is licensed under the BSD 2-Clause license.
References
- U. SchollwöckThe density-matrix renormalization group in the age of matrix product states
- J. Haegeman, C. Lubich, I. Oseledets, B. Vandereycken, F. VerstraeteUnifying time evolution and optimization with matrix product states
- I. P. McCullochFrom density-matrix renormalization group to matrix product states
- T. BarthelPrecise evaluation of thermal response functions by optimized density matrix renormalization group schemes
1 maintainer
Metadata
1 maintainer
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