PRISM-Q

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PRISM-Q is a Rust quantum circuit simulator built for speed. It dispatches across multiple specialized backends, runs a multi pass fusion pipeline, and uses AVX2, FMA, and BMI2 SIMD kernels in the inner loop. CPU kernels are the default path, with optional CUDA support for statevector and experimental stabilizer workloads. Input is OpenQASM 3.0 with backward compatible 2.0 syntax.

For which CPU and GPU architectures each backend supports, see the capability and support matrix.

Documentation

Full documentation is published at https://abecoull.github.io/prism-q/. The generated API reference is on docs.rs.

Installation

Add PRISM-Q to a Rust project:

cargo add prism-q

Rayon parallelism and the faer SVD path are on by default. For a single-threaded, minimal-dependency build, opt out:

cargo add prism-q --no-default-features

For CUDA support, install CUDA Toolkit 12.x or newer, then build with:

cargo build --release --features "parallel gpu"

Building from source or pinning to a git revision is covered in CONTRIBUTING.md.

Python

Python bindings (PyO3 + maturin) live in bindings/python and expose core simulation, noise, and QEC with NumPy output:

import prism_q

circuit = prism_q.CircuitBuilder(2).h(0).cx(0, 1).build()
print(prism_q.simulate(circuit).seed(42).run().probabilities)  # [0.5, 0, 0, 0.5]

Count keys and measurement bits are LSB-first (q[0] is the least-significant qubit), reversed relative to Qiskit. Invalid indices (for example a qubit index outside the register) raise an exception. See bindings/python/README.md.

Quick start

use prism_q::run_qasm;

let qasm = r#"
    OPENQASM 3.0;
    include "stdgates.inc";
    qubit[2] q;
    bit[2] c;
    h q[0];
    cx q[0], q[1];
    c[0] = measure q[0];
    c[1] = measure q[1];
"#;

let result = run_qasm(qasm, 42).unwrap();
println!("{:?}", result.probabilities);
// Bell state: ~50% |00⟩, ~50% |11⟩

Shot-based sampling

use prism_q::{bitstring, circuit::openqasm, simulate};

let circuit = openqasm::parse(qasm).unwrap();
let result = simulate(&circuit).seed(42).shots(1024).unwrap();
println!("{result}");
// 00: 512
// 11: 512

let counts = simulate(&circuit)
    .seed(42)
    .sample_counts(1024)
    .unwrap();
for (bits, count) in counts.into_counts() {
    println!("{}: {count}", bitstring(&bits, circuit.num_classical_bits));
}

Backend dispatch

use prism_q::{circuit::openqasm, simulate, BackendKind};

let circuit = openqasm::parse(qasm).unwrap();

// Auto picks the optimal backend based on circuit properties.
let auto = simulate(&circuit).seed(42).run().unwrap();

// Or choose explicitly.
let stab = simulate(&circuit)
    .backend(BackendKind::Stabilizer)
    .seed(42)
    .run()
    .unwrap();
let mps = simulate(&circuit)
    .backend(BackendKind::Mps { max_bond_dim: 64 })
    .seed(42)
    .run()
    .unwrap();
let sparse = simulate(&circuit)
    .backend(BackendKind::Sparse)
    .seed(42)
    .run()
    .unwrap();

Programmatic circuit construction

use prism_q::CircuitBuilder;

let result = CircuitBuilder::new(3)
    .h(0)
    .cx(0, 1)
    .cx(1, 2)
    .run(42)
    .unwrap();

CircuitBuilder chains gate, control, and execution methods. For lower-level access, use Circuit directly:

use prism_q::{simulate, Circuit, gates::Gate};

let mut c = Circuit::new(3, 0);
c.add_gate(Gate::H, &[0]);
c.add_gate(Gate::Cx, &[0, 1]);
c.add_gate(Gate::Cx, &[1, 2]);
let result = simulate(&c).seed(42).run().unwrap();

Backends

BackendKind::Auto selects at dispatch time. Non-entangling circuits go to Product State; all-Clifford circuits go to Stabilizer, or Factored Stabilizer when a large circuit splits into independent blocks; circuits past the statevector memory budget go to Sparse when sparse-friendly and otherwise to MPS with bond dimension 256; partially independent circuits go to Factored; everything else runs on Statevector. Clifford+T circuits with few T gates route through the stabilizer rank and Pauli propagation engines before this tree. The memory budget is dynamic, derived from available RAM at dispatch time, and can be overridden with PRISM_MAX_SV_QUBITS.

Gates and OpenQASM support

Covers the standard OpenQASM stdgates.inc set, common controlled and multi-controlled variants, Qiskit exporter gates, IonQ and Google/Cirq native gate names, decomposed multi-instruction gates, and IBM legacy u1/u2/u3 syntax. Modifiers inv @, ctrl @, pow(k) @ chain arbitrarily for direct gates, and user-defined gate declarations are supported.

The authoritative list of supported gate keywords, language features, and modifiers lives in the parser at src/circuit/openqasm.rs. See resolve_gate() and resolve_decomposed_gate(). Smoke tests in tests/smoke_openqasm.rs exercise each feature end to end.

Build and test

cargo build --release
cargo test --all-features
cargo clippy --all-targets --all-features -- -D warnings -D clippy::undocumented_unsafe_blocks
cargo fmt --check
cargo doc --no-deps --features "parallel gpu distributed"

For Rayon parallelism on larger circuits:

cargo build --release --features parallel

Thread count defaults to logical cores. Set RAYON_NUM_THREADS to override.

GPU backend (optional)

The gpu feature enables CUDA paths for the statevector and (experimentally) the stabilizer backend. Requires CUDA Toolkit 12.x or newer and a CUDA capable device. PTX is compiled at runtime via NVRTC against the device's compute capability.

cargo build --release --features "parallel gpu"

Opt in through the simulation builder. The circuit still goes through fusion and independent subsystem decomposition, and a size aware crossover keeps small sub circuits on the CPU:

use prism_q::{gpu::GpuContext, simulate};

let ctx = GpuContext::new(0)?;
let result = simulate(&circuit).gpu(ctx).seed(42).run()?;

BackendKind::StabilizerGpu runs Clifford circuits on the device, and CompiledSampler::with_gpu(ctx) accelerates large shot counts for compiled BTS sampling. Crossover thresholds are conservative by default and can be tuned through PRISM_GPU_MIN_QUBITS, PRISM_STABILIZER_GPU_MIN_QUBITS, and PRISM_GPU_BTS_MIN_SHOTS.

simulate(&circuit).gpu_auto(ctx) runs automatic backend selection with the device opted in: statevector and stabilizer workloads that clear the qubit crossover and fit in VRAM run on the device, and everything else takes the identical CPU path. See docs/guides/gpu.md for kernel design, crossover analysis, and the full set of tuning knobs.

Coverage

Requires rustup component add llvm-tools-preview and cargo install cargo-llvm-cov.

cargo llvm-cov --all-features                # terminal summary
cargo llvm-cov --all-features --html --open  # browseable HTML report

CI generates coverage on every push and PR, and updates the badge automatically.

Benchmarks

cargo bench --bench circuits     --features parallel         # circuit macrobenchmarks
cargo bench --bench bench_driver --features parallel         # gate microbenchmarks
cargo bench --bench bench_gpu    --features "parallel gpu"   # GPU dispatch benchmarks

Always use --features parallel; baselines were taken with Rayon enabled. Do not run two cargo bench invocations concurrently on the same machine — Rayon thread pools contend for cores and skew results.

Baseline capture, regression checks, and the markdown table workflow used in PRs live in CONTRIBUTING.md.

Profiling

Needs cargo install flamegraph:

./scripts/flamegraph.sh "qft_textbook/16"              # unix
.\scripts\flamegraph.ps1 "qft_textbook/16"             # windows

SVGs land in bench_results/ (gitignored).

Roadmap

• Expanded classical control: mid circuit branching beyond the current if form, and parameterized circuit reuse for variational workloads.
• Multi GPU and distributed GPU execution: a GPU context currently binds a single device, and the distributed backend is CPU only.
• ROCm (AMD GPU) ports of the CUDA statevector and stabilizer kernels.
• Distributed noisy shots: noise models are rejected on the distributed backend because trajectory execution is not lockstep across ranks.

Architecture

See the architecture reference for the full picture: layered design, backend trait contract, SIMD strategy, fusion pipeline, and compiled samplers. The published docs site is at https://abecoull.github.io/prism-q/.

Contributing

See CONTRIBUTING.md for the build, test, and benchmark workflow. The pull request template at .github/PULL_REQUEST_TEMPLATE.md captures the required checklist.