stim

library1.16.0pypypi✓ verified 85d ago

Stim is a fast Python library for high-performance simulation and analysis of quantum stabilizer circuits, particularly those used in quantum error correction (QEC). It focuses on providing low-level, fast building blocks for simulating circuits with thousands of qubits and millions of operations, enabling rapid Monte Carlo sampling. The library maintains a regular release cadence, with minor versions typically released every few months, ensuring active development and improvements.

ai-ml data

pip install stim

github ↗package ↗

INSTALL

IMPORT

SIG · STIM

stim

ai-mlpythonv1.16.0

Install

3.9s avg

Import

10ms

Disk

99MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.16.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 4.13s

py 3.11

✕ build_error

✓ 3.98s

py 3.12

✕ build_error

✓ 3.68s

py 3.13

✕ build_error

✓ 3.85s

py 3.9

✕ build_error

1/4 runs

99MB installed

● package 99MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Circuit

✓ from stim import Circuit

TableauSimulator

✓ from stim import TableauSimulator

PauliString

✓ from stim import PauliString

DetectorErrorModel

✓ from stim import DetectorErrorModel

This quickstart demonstrates how to create a basic quantum circuit, add gates, compile a sampler for efficient measurement shot generation, and extract a detector error model from a noisy circuit. The example includes fundamental operations and a simple noise model.

import stim # Create a quantum circuit circuit = stim.Circuit() circuit.append("H", [0]) circuit.append("CNOT", [0, 1]) circuit.append("M", [0, 1]) # Compile a sampler for the circuit sampler = circuit.compile_sampler() # Sample measurement shots shots = sampler.sample(shots=10) print("Circuit:\n", circuit) print("Sampled shots (first 5):\n", shots[:5]) # Example of adding noise and extracting a detector error model noisy_circuit = stim.Circuit() noisy_circuit.append("H", [0]) noisy_circuit.append("DEPOLARIZE1(0.001)", [0]) noisy_circuit.append("CNOT", [0, 1]) noisy_circuit.append("DEPOLARIZE2(0.002)", [0, 1]) noisy_circuit.append("M", [0, 1]) noisy_circuit.append("DETECTOR", [0]) # Add a simple detector at qubit 0 dem = noisy_circuit.detector_error_model() print("\nDetector Error Model (first few lines):\n", str(dem).splitlines()[0:5])

stim --version

Debug

Known issues

breakingThe default value for `ignore_ungraphlike_errors` in `stim.DetectorErrorModel.shortest_graphlike_error` changed from `False` to `True` in v1.11.0. This means the method will now ignore ungraphlike errors by default, which may change behavior for existing code.

fix

Explicitly set `ignore_ungraphlike_errors=False` if you require the old behavior, or review your logic to account for the new default.

affects: >=1.11.0

breakingWhen reading or writing data using the `"ptb64"` format, the shot count is strictly required to be a multiple of 64 since v1.9.0. Previously, missing shots might have been padded.

fix

Ensure all operations involving `"ptb64"` format data handle shot counts that are exact multiples of 64.

affects: >=1.9.0

gotchaStim's C++ API makes no compatibility guarantees and may change arbitrarily and catastrophically between minor versions. Only the Python API and command-line API promise backwards compatibility within major versions.

fix

When interacting with Stim's C++ components directly, always pin to a specific Stim version and be prepared for API changes with updates. For stability, use the Python API.

affects: All versions

gotchaThe `HERALDED_PAULI_CHANNEL_1` gate had a bug prior to v1.12.1 where it targeted fixed indices instead of the given qubits. This could lead to incorrect simulation results without explicit error messages.

fix

Upgrade to Stim v1.12.1 or newer to ensure correct behavior of `HERALDED_PAULI_CHANNEL_1`. If using older versions, thoroughly validate circuits involving this gate. [cite: -Github release v1.12.1]

affects: <1.12.1

gotchaStim's `Circuit` class primarily supports Pauli noise channels and single-control Pauli feedback. For more complex noise models (e.g., amplitude decay) or multi-control feedback, you must manually drive a `stim.TableauSimulator`.

fix

For advanced noise or feedback scenarios, use `stim.TableauSimulator` and implement custom logic for state evolution.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'stim'

The Stim library is not installed in the current Python environment, or the environment is not active.

fix

Install Stim using pip: `pip install stim`

ValueError: shot_count must be a multiple of 64 for 'ptb64' format.

When reading or writing measurement results using the 'ptb64' format (e.g., with `Circuit.compile_sampler().sample(..., format='ptb64')`), the number of shots provided is not a multiple of 64.

fix

Adjust the `shots` parameter to be a multiple of 64. For example, `sampler.sample(shots=128, format='ptb64')`.

TypeError: Cannot apply H to qubit 0 1: expected a single qubit target or a list of single qubit targets, not a list of 2 targets

An incorrect number or type of targets was provided for a gate. For example, applying a single-qubit gate like 'H' to multiple qubits in a single instruction when it expects a list of individual qubit targets.

fix

Consult the Stim documentation for the specific gate's target format. For single-qubit gates, pass qubits individually (e.g., `circuit.append("H", [0]); circuit.append("H", [1])`) or as separate entries. For multi-qubit gates, ensure the list format matches (e.g., `circuit.append("CNOT", [0, 1])`).

Upgrade

Version history

1.16.0latest on PyPI · released May 22, 2026

Audit

Dependencies

pymatchingoptionalCommonly used for decoding Detector Error Models generated by Stim.

Agent activity

29 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/quantumlib/stim ↗

packagehttps://pypi.org/project/stim/ ↗