RUST-NATIVE · PYTHON-FIRST · TENSORCIRCUIT

Pauli structure,
made practical.

TenCirPauli turns structured quantum objects into practical computation and evidence: Pauli algebra, Hamiltonians, symmetry and code analysis, observable execution, and classical-shadow estimation for the TensorCircuit ecosystem.

Start with Python Browse the API

structured
quantum work

One package, one workflow. Describe the structure, organize it for the task, compute in the right runtime, then inspect the result as a value, estimate, or diagnosis.

The library in four verbs

01DescribeOperators, circuits, Hamiltonians, and code spaces.

→

02OrganizeCanonicalize, map, group, reduce, and choose a plan.

→

03ComputeBuild, apply, propagate, differentiate, or sample.

→

04InspectRead out values, estimates, syndromes, and logical errors.

What it covers

01 / DESCRIBE

Build quantum structure

Pauli and structured operators, Hamiltonians, circuits, stabilizer codes, and measurement states.

02 / ORGANIZE

Exploit the structure

Canonicalize, map, group, taper, restrict to sectors, and prepare execution or measurement plans.

03 / COMPUTE

Run the workload

Use dense, sparse, matrix-free, native, TensorCircuit, or JAX execution with values and gradients.

04 / INSPECT

Turn computation into evidence

Estimate observables with classical shadows, reconstruct grouped measurements, and diagnose syndromes and logical errors.

A first result

import tencirpauli as tcp

hamiltonian = tcp.PauliOperator.from_terms(
    2, [("XX", 0.5), ("ZI", -1.25j)]
)

matrix = hamiltonian.dense()
print(matrix.shape)
# (4, 4)

The public surface stays in Python; the computationally dense work is handled behind one coarse native boundary. Read the concepts page for the mental model, or go straight to the quickstart when you already know what you need.

Pauli structure,made practical.