https://github.com/jcmgray/mixpic-bp-quantum-dynamics

Mixed picture tensor network belief propagation quantum dyanmics for arXiv:2308.05077
https://github.com/jcmgray/mixpic-bp-quantum-dynamics

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Mixed picture tensor network belief propagation quantum dyanmics for arXiv:2308.05077

• Host: GitHub
• URL: https://github.com/jcmgray/mixpic-bp-quantum-dynamics
• Owner: jcmgray
• License: agpl-3.0
• Created: 2023-10-25T01:34:48.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-04T12:33:26.000Z (12 months ago)
• Last Synced: 2024-10-15T01:30:01.171Z (3 months ago)
• Language: Jupyter Notebook
• Size: 59.6 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# mixpic-bp-quantum-dynamics

This repository contains the mixed picture, tensor network, lazy belief

propagation based quantum dynamics simulation algorithms for the paper

arXiv:2308.05077.

The methods probably require development versions of:

- [quimb](https://quimb.readthedocs.io)

- [cotengra](https://cotengra.readthedocs.io)

- [xyzpy](https://xyzpy.readthedocs.io)

- [autoray](https://autoray.readthedocs.io)

The notebook [`combo_dynamics.ipynb`](combo_dynamics.ipynb) shows the usage

of the following methods:

- MIX (mixed picture)

- PEPO (fully operator evolution)

- PEPS (fully state evolution)

- Exact (via TN simplification and exact contraction)

- CPU-GPU offloading with dynamic slicing for scaling to large $\chi$