QuaSARQ Python Binding

GPU stabilizer simulator. Accepts stim-format circuits directly, so a stim.Circuit (or circuit text) goes straight in:

import quasarq, stim

circuit = stim.Circuit.generated("surface_code:rotated_memory_z", distance=5, rounds=5, after_clifford_depolarization=0.001)

dets, obs = quasarq.compile_detector_sampler(circuit, seed=1).sample(100_000, separate_observables=True)

measurements = quasarq.compile_sampler(circuit, seed=1).sample(100_000)

Installing

pip install quasarq

The budled wheels are for CPython 3.10 through 3.13. The wheel contains the CUDA runtime and device code for every major architecture from Pascal onwards, so it asks for nothing but a driver (CUDA toolkit is not needed):

	requirement
GPU	Pascal (sm_60) or newer, up to Blackwell
driver	525 or newer
glibc	2.28 or newer (RHEL 8, Ubuntu 20.04, Debian 11, …)

GPUs newer than the compiled set are covered by PTX, which the driver compiles on first use.

Building from source instead

pip falls back to a source build wherever no wheel matches, and drives the whole thing itself:

pip install .            # or: pip install -e .   for a development install

import quasarq then works from any directory, with no PYTHONPATH. The build compiles the CUDA core, cuarena and the extension through the project Makefiles, in parallel (-j8 by default), taking roughly half a minute. It targets only the building machine's GPU by default — see QUASARQ_CUDA_ARCH below.

make binding still works if you would rather build without installing; put src/binding on PYTHONPATH in that case. The published wheels are built by .github/workflows/wheels.yml inside the image described in .github/docker/manylinux-cuda.Dockerfile.

What must be present at build time

The CUDA toolkit (nvcc). Set CUDA_PATH if it is not at /usr/local/cuda.
make and cmake.
cuarena, found in this order: $CUARENA_DIR, then extern/cuarena, then ~/cuarena. Fetch the bundled copy with
```
git submodule update --init --recursive
```

Build options

variable	default	meaning
`QUASARQ_CUDA_ARCH`	`native`	GPU target. `native` builds only for the machine's own GPU. Use `all-major` for a binary that runs on every architecture this nvcc supports, or a comma-separated list ordered lowest to highest, such as `sm_80,sm_90` — the last entry also gets PTX, so newer GPUs still run.
`QUASARQ_BUILD_JOBS`	`8`	parallel compile jobs
`QUASARQ_WORD_SIZE`	`64`	tableau word size
`CUARENA_DIR`	–	explicit path to cuarena

The default -arch=native makes the result not portable to another GPU architecture. Build a redistributable binary with:

QUASARQ_CUDA_ARCH=all pip wheel .

That takes considerably longer, since every kernel is compiled for every architecture.

GPU memory

The pool is sized per run from the circuit and the shot count, which keeps a run from locking the whole device against other processes. Override it if you need to:

quasarq.set_max_device_memory("auto")   # default: size it from the circuit
quasarq.set_max_device_memory(512)      # fixed cap in MB
quasarq.set_max_device_memory(0)        # take whatever is free

Results never depend on the cap: a smaller pool only changes how many shots are simulated per chunk. auto sizes for the whole request so it does not split shots, growing the pool if a later request needs more.

quasarq.compile_detector_sampler(circuit, *, seed=None) → CompiledDetectorSampler
quasarq.compile_sampler(circuit, *, seed=None) → CompiledMeasurementSampler
quasarq.sinter — a sinter.Sampler adapter that decodes with pymatching
set_verbosity, set_chunk_shots, set_kernel_config, device_name, version

kernel.config holds per-size kernel launch geometry and is copied next to the extension at build time; the core locates it relative to the shared object.

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1.7.2

Aug 25, 2026 5 files

1.7.1

Aug 14, 2026 5 files

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Aug 13, 2026 5 files

quasarq 1.7.0

QuaSARQ Python Binding

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