PRISM-Q (Python)

Python bindings for PRISM-Q, a performance-first quantum circuit simulator written in Rust.

Install

pip install prism-q

Quick start

import prism_q

# Build a Bell state and run it.
circuit = prism_q.CircuitBuilder(2).h(0).cx(0, 1).build()
outcome = prism_q.simulate(circuit).seed(42).run()
print(outcome.probabilities)        # array([0.5, 0., 0., 0.5])

# Parse OpenQASM and sample shots.
qasm = """
OPENQASM 3.0;
qubit[2] q;
bit[2] c;
h q[0];
cx q[0], q[1];
c = measure q;
"""
result = prism_q.simulate(prism_q.parse_qasm(qasm)).seed(7).shots(1000)
print(result.counts())              # {'00': ~500, '11': ~500}

# Exact statevector as a NumPy array.
sv = prism_q.simulate(prism_q.circuits.ghz(3)).seed(1).state_vector()
print(sv.dtype, sv.shape)           # complex128 (8,)

Bit ordering

Count keys and measurement bit indices are LSB-first: in a bitstring key, the leftmost character is classical bit 0, and q[0] is the least-significant qubit. This is reversed relative to Qiskit. For example, the state where only q[0] is set reads as "10...", not "...01".

Features

Fluent CircuitBuilder and OpenQASM 3.0 parsing.
Reusable circuit generators (prism_q.circuits): QFT, GHZ, QAOA, hardware-efficient ansatz, quantum volume, and more.
Backend selection via BackendKind (statevector, stabilizer, sparse, MPS, Pauli propagation, ...).
Parameter sweeps without a rebuild (Parameters, PreparedCircuit) plus adjoint gradients.
Noise models (NoiseModel, NoiseChannel) for shot sampling.
Native QEC programs (QecProgram) with detector and observable sampling.
NumPy output for probabilities, statevectors, and QEC bit matrices.

License

MIT OR Apache-2.0

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