Time-Compressed Algorithms

Tensor-network experiments focused on PEPO/PEPS compression and time-evolution optimizations. Scripts implement cooling/optimization routines and utilities; notebooks capture exploratory runs and plots.

Layout

• src/tcompress/: Python package for reusable code.
  • algo_cooling.py: cooling and tensor-network routines used across experiments.
  • p_pepo.py, p_pepo_su.py: PEPO optimization flows (standard and SU variants).
  • Uoptimize.py, gate_arb.py: optimization helpers and arbitrary-geometry tensor utilities.
  • quf.py, register_.py: shared utilities and registrations.
• notebooks/pepo/: PEPO/PEPS-focused experiments and trotter reductions.
• notebooks/mps/: MPS simulations.
• notebooks/plotting/: plotting/visualization explorations.
• notebooks/analysis/: analysis/scratch runs.
• data/outputs/: cached artifacts and reference data (ignored).
• requirements.txt: Python dependencies for experiments.

Setup

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# Option A: editable install
pip install -e .
# Option B: point Python to the package when running notebooks/scripts
# export PYTHONPATH=\"$PWD/src:$PYTHONPATH\"
# For GPU JAX/PyTorch, install vendor wheels as needed.

Notes

• .gitattributes marks notebooks as binary to avoid noisy diffs; use nbdiff or screenshots for review.
• .gitignore excludes checkpoints, caches, and data/outputs/. Keep transient data there or outside the repo.
• Large generated data should stay out of version control or use Git LFS if required.