Noise and QEC

PRISM-Q models noise and quantum error correction without falling back to a dense statevector per shot. The machinery is the compiled samplers and the native QEC program IR; this guide shows how they fit together.

Noisy shot sampling

Attach a NoiseModel and sample:

#![allow(unused)]
fn main() {
use prism_q::{simulate, BackendKind, NoiseModel};

let noise = NoiseModel::uniform_depolarizing(&circuit, 0.001);
let result = simulate(&circuit)
    .backend(BackendKind::Statevector)
    .noise(noise)
    .seed(42)
    .shots(1024)
    .unwrap();
}

NoiseModel carries per-instruction depolarizing channels (NoiseOp { qubit, px, py, pz }) and supports readout error and amplitude damping. For Clifford circuits, the noisy compiled sampler propagates noise sensitivity rows and XORs fired channels into each sample, avoiding per-shot state evolution entirely.

Detector sampling

For repeated syndrome extraction, compile_detector_sampler compiles a Clifford circuit with measurement and reset reuse into a packed sampler, then derives detector and observable records as parity rows over the measurement record. Reset reuse becomes fresh qubit aliases, so there is no per-shot tableau replay.

Native QEC programs

When you need detectors, logical observables, postselection, and Pauli-noise annotations as first-class constructs, use the native QEC program IR rather than a Circuit:

#![allow(unused)]
fn main() {
use prism_q::{parse_qec_program, run_qec_program};

let program = parse_qec_program(qec_text).unwrap();
let result = run_qec_program(&program).unwrap();
}

run_qec_program lowers Clifford-compatible programs into the packed compiled sampler. run_qec_program_reference is the per-shot statevector oracle for validating small programs.

Homological sampling

run_shots_homological and ErrorChainComplex model the GF(2) chain complex over noise locations, identifying undetectable error cycles. noisy_marginals_analytical computes marginals in closed form from the parity matrix and noise rates, with no Monte Carlo.