Warning
This repository tracks an actively developed version of Qamomile. The version currently available on PyPI is not the same as this branch. APIs in this branch may still change, including breaking changes, while active development continues.
Qamomile is a typed quantum programming SDK for writing quantum kernels in Python, inspecting them as Qamomile IR, estimating resources symbolically, and transpiling them to concrete execution quantum SDK such as Qiskit, QURI Parts, CUDA-Q. Furthremore, as a backend for Qiskit, we support qBraid.
The current workflow is:
@qkernel define -> draw() / estimate_resources() -> transpile() -> sample() / run() -> .result()
- Write quantum programs as typed Python functions with
@qkernel. - Use typed handles such as
Qubit,Bit,Float,UInt, andObservable. - Inspect kernels before execution with
draw()andestimate_resources(). - Build parameterized circuits and reuse a transpiled executable with different runtime bindings.
- Run measured programs with
sample()and expectation-value programs withrun(). - Express circuit structure with classical control flow such as
qmc.range(),qmc.items(),if, andwhile. - Reuse circuit logic with helper kernels and
@composite_gate.
This README describes the current source tree, not the older PyPI release. If you want this version, install from source.
Requirements:
- Python 3.12+
uv
Clone the repository:
git clone https://github.com/Jij-Inc/Qamomile.git
cd QamomileChoose the installation style that matches your use case.
Full development environment:
uv syncThis installs the default development dependency group.
In the current pyproject.toml, that gives you the core Qiskit-based environment together with documentation and test tooling.
Optional backend integrations such as QURI Parts, qBraid, and CUDA-Q still need their corresponding extras.
Runtime-only environment from source:
uv sync --no-devRuntime-only environment from source with QURI Parts support:
uv sync --no-dev --extra quri_partsRuntime-only environment from source with qBraid support:
uv sync --no-dev --extra qbraidRuntime-only environment from source with CUDA-Q v0.14.0 support:
uv sync --no-dev --extra cudaq-cu12 # for CUDA 12.x
uv sync --no-dev --extra cudaq-cu13 # for CUDA 13.x (or MacOS)CUDA-Q v0.14.0 currently supports Linux, macOS ARM64 (Apple Silicon), and Windows via WSL2. For MacOS, please use cudaq-cu13.
Note
Why cudaq-cu12 / cudaq-cu13 instead of cudaq?
The upstream cudaq meta-package provides only an sdist whose setup.py dynamically computes install_requires.
This causes uv pip install cudaq to silently install the package without its dependencies on the first attempt
(astral-sh/uv#12759,
NVIDIA/cuda-quantum#3616).
To avoid this issue, Qamomile specifies the concrete wheel packages cuda-quantum-cu12 / cuda-quantum-cu13 directly
as optional dependencies, split by CUDA version.
If you prefer an explicit editable install inside your environment, this also works from the cloned repository:
pip install -e .
pip install -e ".[quri_parts]" # optional
pip install -e ".[qbraid]" # optional
pip install -e ".[cudaq-cu12]" # optional, CUDA 12.x
pip install -e ".[cudaq-cu13]" # optional, CUDA 13.xIf you intentionally want the latest published release instead, pip install qamomile installs the PyPI package, not this work-in-progress branch.
import math
import qamomile.circuit as qmc
from qamomile.qiskit import QiskitTranspiler
@qmc.qkernel
def biased_coin(theta: qmc.Float) -> qmc.Bit:
q = qmc.qubit(name="q")
q = qmc.ry(q, theta)
return qmc.measure(q)
# Inspect the kernel before execution
biased_coin.draw(theta=0.6)
est = biased_coin.estimate_resources()
print("qubits:", est.qubits)
print("total gates:", est.gates.total)
# Transpile once, keep theta as a runtime parameter
transpiler = QiskitTranspiler()
exe = transpiler.transpile(biased_coin, parameters=["theta"])
# Execute with a concrete binding
result = exe.sample(
transpiler.executor(),
shots=256,
bindings={"theta": math.pi / 4},
).result()
print(result.results)If a kernel returns measured bits, use sample().
If it returns a qmc.Float from qmc.expval(...), use run() instead.
qamomile.circuit: the main entry point for typed quantum kernels, gates, control flow, drawing, and resource estimationqamomile.observable: Hamiltonians and Pauli observables used with expectation-value workflowsqamomile.qiskit: Qiskit transpiler and executor supportqamomile.cudaq: optional CUDA-Q transpiler, executor, and observable conversion (supports both static sampling and runtime control-flow modes)qamomile.qbraid: optional qBraid executor support for running Qiskit circuits on qBraid-supported devicesqamomile.quri_parts: optional QURI Parts transpiler and executor supportqamomile.optimization: optimization-oriented functionality retained for continuity with older Qamomile workflows
Qamomile still supports the optimization-oriented workflow that older versions focused on.
That functionality lives under qamomile.optimization, including QAOA, FQAOA, and QRAO-related modules.
This README focuses on the current circuit-first API, but optimization support remains part of the project.
- Documentation: English and Japanese
- Tutorials: English and Japanese
- API reference: https://jij-inc-qamomile.readthedocs-hosted.com/latest/en/api/
- Repository: https://github.com/Jij-Inc/Qamomile
Contributions, bug reports, and feedback are welcome via GitHub Issues and pull requests.
Qamomile is released under the Apache 2.0 License.
If you use Qamomile in your research, please cite:
@INPROCEEDINGS{11249901,
author={Huang, Wei-Hao and Matsuyama, Hiromichi and Tam, Wai-Hong and Sato, Keisuke and Yamashiro, Yu},
booktitle={2025 IEEE International Conference on Quantum Computing and Engineering (QCE)},
title={Qamomile: A Cross-SDK Bridge for Quantum Optimization},
year={2025},
volume={02},
pages={516-517},
doi={10.1109/QCE65121.2025.10423}
}