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PRISM-Q is a Rust quantum circuit simulator focused on practical throughput.

Automatic dispatch selects the simulation strategy that best fits each circuit's structure. CPU kernels use SIMD, with optional CUDA paths for statevector and experimental stabilizer workloads. Input is OpenQASM 3.0, with backward-compatible 2.0 syntax.

Add PRISM-Q to a Rust project:

cargo add prism-q

Enable Rayon parallelism for larger circuits:

cargo add prism-q --features parallel

Use the latest repository version:

cargo add prism-q --git https://github.com/AbeCoull/prism-q --features parallel

Build from source:

git clone https://github.com/AbeCoull/prism-q.git
cd prism-q
cargo build --release --features parallel

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

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

The crate exposes library APIs under prism_q.

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⟩

use prism_q::{circuit::openqasm, run_shots};

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

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

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

// Auto picks the optimal backend based on circuit properties.
let auto   = run_with(BackendKind::Auto, &circuit, 42).unwrap();

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

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::{Circuit, sim, 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 = sim::run(&c, 42).unwrap();

BackendKind::Auto selects at dispatch time. Non-entangling circuits go to Product State, all-Clifford circuits go to Stabilizer, large circuits fall through to MPS with bond dimension 256 once they exceed the statevector memory budget, and everything else runs on Statevector. The memory budget is dynamic, derived from available RAM at dispatch time, and can be overridden with PRISM_MAX_SV_QUBITS.

Covers the standard OpenQASM stdgates.inc set, common controlled and multi-controlled variants, decomposed multi-instruction gates, and IBM legacy u1/u2/u3 syntax. Modifiers inv @, ctrl @, pow(k) @ chain arbitrarily, 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.

cargo build --release
cargo test --all-features
cargo clippy --all-targets --all-features -- -D warnings
cargo fmt --check
cargo doc --no-deps --all-features

For Rayon parallelism on larger circuits:

cargo build --release --features parallel

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

The gpu feature enables a CUDA statevector path.

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

Requires the CUDA toolkit (12.x or newer) and a CUDA-capable device. PTX is compiled at runtime via NVRTC against the device's compute capability. Every Gate variant is covered by a dedicated kernel, including batched kernels for BatchPhase, BatchRzz, DiagonalBatch, and both diagonal and non-diagonal MultiFused. Golden tests in tests/golden_gpu.rs verify amplitude equivalence against the CPU statevector within 1e-10.

BackendKind::Auto does not yet route to GPU. Opt in explicitly. The recommended entry point is run_with_gpu, which dispatches through BackendKind::StatevectorGpu so the circuit picks up fusion plus independent-subsystem decomposition and applies a size-aware crossover (default: GPU only for ≥ gpu::MIN_QUBITS_DEFAULT qubit sub-circuits, overridable via PRISM_GPU_MIN_QUBITS):

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

let ctx = GpuContext::new(0)?;
let result = run_with_gpu(&circuit, 42, ctx)?;

Introspect whether the default GPU dispatch footprint is likely to fit before dispatching a large circuit:

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

if gpu::is_available() {
    let ctx = GpuContext::new(0)?;
    if ctx.fits_statevector(28)? {
        // the 28-qubit state plus the default probabilities scratch buffer
        // should fit in current free VRAM
    }
}

For kernel experiments where every gate must hit the device, use StatevectorBackend::new(seed).with_gpu(ctx) directly. That bypasses the dispatch crossover by design.

BackendKind::StabilizerGpu and run_with_stabilizer_gpu route Clifford circuits through CUDA. Gate application uses one batched kernel (stab_apply_batch). Measurement and reset stay on the device, including pivot search, row operations, phase fixup, and deterministic outcomes. Golden tests cover 100 to 5000 qubits.

Compiled BTS sampling can use the GPU through run_shots_compiled_with_gpu or CompiledSampler::with_gpu(ctx). The GPU path activates only for flat sparse parity data and shot counts at or above gpu::BTS_MIN_SHOTS_DEFAULT (131_072 by default, override with PRISM_GPU_BTS_MIN_SHOTS). The sampler caches parity data and reusable scratch on the device across repeated calls. DevicePackedShots::marginals() and DevicePackedShots::counts() reduce on the device before falling back to a full shot copy.

The stabilizer GPU dispatch crossover is conservative by default: STABILIZER_MIN_QUBITS_DEFAULT = 100_000. Set PRISM_STABILIZER_GPU_MIN_QUBITS for experiments.

Benchmark direct stabilizer backend groups for throughput claims. probabilities(), export_tableau(), and export_statevector() are diagnostic readback helpers, not the throughput path.

See docs/architecture.md for the kernel design and crossover analysis.

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.

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
cargo bench --features "parallel,bench-fast"                 # quick smoke test

bench_gpu includes direct stabilizer backend groups that time StabilizerBackend::apply_instructions without probability readback. It also includes GPU BTS marginal and device count groups for the reduced transfer compiled sampler path. Use direct stabilizer groups for GPU crossover and throughput claims. Treat run_with groups as public API timings.

Always use --features parallel. Baselines were taken with Rayon enabled. Never run two cargo bench invocations at the same time on the same machine. Rayon thread pools fight for cores and produce large swings in results.

# Save a baseline.
cargo bench --features parallel
./scripts/bench_check.sh save --name "before"         # unix
.\scripts\bench_check.ps1 save -Name "before"         # windows

# Make changes, bench again.
cargo bench --features parallel

# Compare (exits 1 on regression).
./scripts/bench_check.sh compare --baseline "before"
.\scripts\bench_check.ps1 compare -Baseline "before"

# Markdown table for PRs.
./scripts/bench_check.sh table --baseline "before"
.\scripts\bench_check.ps1 table -Baseline "before"

Needs cargo install flamegraph:

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

SVGs land in bench_results/ (gitignored).

• Expanded OpenQASM 3.0: reset instruction, for loop unrolling, def subroutines.
• Expectation values: <psi|O|psi> for Pauli strings (VQE and QAOA).
• Density matrix backend: mixed-state simulation for noise and decoherence modeling.
• GPU auto-dispatch: thread a GPU context into BackendKind::Auto so large circuits route to GPU without an explicit BackendKind::StatevectorGpu. Crossover and decomposition already work through the explicit variant.

See docs/architecture.md for the full picture: layered design, backend trait contract, SIMD strategy, fusion pipeline, compiled samplers, and how to add a new backend.

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