TenCirPauli

Rust-native Pauli algebra and structured quantum execution for TensorCircuit-NG.

TenCirPauli moves Pauli-heavy quantum workflows from Python object graphs into compact native data and Rust execution. It is the TensorCircuit-NG-facing layer for Hamiltonians, measurement grouping, symmetry reduction, restricted sectors, structured operators, classical-shadow snapshots, and observable propagation.

What it does

Why it is fast

• Scalable symbolic work stays in Rust over packed Pauli, occupation, charge, and structured representations.
• Public results are lazy native-backed handles by default; Python materializes terms or arrays only when an API explicitly asks for them.
• Matrix-free and restricted-sector plans avoid dense matrices and full Hilbert spaces when the workload has useful structure.
• TensorCircuit-NG and JAX can remain at the backend boundary through reusable backend_mvp_plan() plans, while native CPU paths use coarse-grained Rust execution and default parallelism.

Performance snapshots

These are representative runs from the linked research studies, not universal maxima. The benchmark page collects the workload definitions and runnable examples.

Choose an entry point

mode="general" is available for algebraic commuting groups, but its result is intentionally measurement_ready=False; use QWC grouping when the output must directly describe product-basis measurements.

TensorCircuit-NG ecosystem

TenCirPauli is designed as a TensorCircuit-NG companion, not a replacement for its circuit frontend. TensorCircuit-NG circuits can be converted with U1Circuit.from_circuit() or PropagationCircuit.from_circuit(), and Pauli backend plans can be called through TensorCircuit-NG's NumPy or JAX backend. The Rust core itself has no Python or TensorCircuit-NG dependency.

Research examples

The research index links to reproducible, manual studies covering Fermi–Hubbard, Holstein, SYK Majorana, BCH convergence, Lie closure, fermion mapping, U(1) VQE, Pauli propagation, measurement grouping, and classical shadows.

Install

python -m pip install tencirpauli

Released wheels cover common CPython 3.9+ platforms. Source builds require Rust, Cargo, and maturin; chemistry interop is optional via python -m pip install 'tencirpauli[chemistry]'.

Documentation and development

Read the documentation for concepts and API details, performance notes for benchmark context, and CONTRIBUTING.md for local development. The local quality gate is python scripts/check.py --benchmark smoke.

License

Apache License 2.0.