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Move QCQMC code out of conftest.py to recirq/qcqmc/conftest.py #474

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mhucka merged 2 commits into from
Apr 22, 2026
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Move QCQMC code out of conftest.py to recirq/qcqmc/conftest.py #474

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3b80c62
Move QCQMC code to recirq/qcqmc/conftest.py
mhucka Apr 21, 2026
ac0638d
Update instructions about running pytest
mhucka Apr 21, 2026
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29 changes: 18 additions & 11 deletions CONTRIBUTING.md
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,8 @@ first create a virtual Python environment. You can use either Python's built-in
[`venv`](https://docs.python.org/3/library/venv.html) module or another tool
that you are comfortable with.

Then, in that virtual environment, install the ReCirq dependencies using `pip`:
Then, in that virtual environment, install the core ReCirq dependencies using
`pip`:

```shell
pip install -r requirements.txt
Expand All @@ -90,27 +91,33 @@ need to install its extra dependencies like this:
pip install -r recirq/optimize/extra-requirements.txt
```

Finally, if you are going to edit any of the Jupyter notebooks, install the
additional requirements needed to run the notebook format checks:
Once the environment is set up, you can do your work and `git commit` your
changes to your branch as needed.

#### Running tests

To test your code when working on the module for a given experiment, run
`pytest` with the path to the module. Continuing with the `recirq/optimize`
module as an example, you would run the following command:

```shell
pip install -r dev_tools/requirements/deps/tensorflow-docs.txt
pytest recirq/optimize
```

Once the environment is set up, you can do your work and `git commit` your
changes to your branch as needed.
#### Writing and editing notebooks

To test notebooks for proper formatting and other issues, run the following
command:
If you are going to edit any of the Jupyter notebooks, install the
additional requirements needed to run the notebook format checks:

```shell
dev_tools/nbformat
pip install -r dev_tools/requirements/deps/tensorflow-docs.txt
```

To test your code, run
After that, you can test notebooks for proper formatting and other issues with
this command:

```shell
pytest recirq
dev_tools/nbformat
```

### Pull requests and code reviews
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175 changes: 0 additions & 175 deletions conftest.py
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Expand Up @@ -11,184 +11,9 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pathlib
from typing import Dict, Tuple

import numpy as np
import pytest

from recirq.qcqmc import analysis, blueprint, data, experiment, qubit_maps
from recirq.qcqmc.hamiltonian import (
HamiltonianData,
HamiltonianFileParams,
)
from recirq.qcqmc.trial_wf import (
PerfectPairingPlusTrialWavefunctionParams,
TrialWavefunctionData,
)


@pytest.fixture(scope="package")
def package_tmp_path(tmp_path_factory: pytest.TempPathFactory) -> pathlib.Path:
return tmp_path_factory.mktemp("data", numbered=True)


@pytest.fixture(scope="package")
def fixture_4_qubit_ham(package_tmp_path) -> HamiltonianData:
params = HamiltonianFileParams(
name="test hamiltonian 4 qubits",
integral_key="fh_sto3g",
n_orb=2,
n_elec=2,
path_prefix=str(package_tmp_path),
)

hamiltonian_data = HamiltonianData.build_hamiltonian_from_file(params)

return hamiltonian_data


@pytest.fixture(scope="package")
def fixture_8_qubit_ham(package_tmp_path) -> HamiltonianData:
params = HamiltonianFileParams(
name="test hamiltonian 8 qubits",
integral_key="h4_sto3g",
n_orb=4,
n_elec=4,
path_prefix=str(package_tmp_path),
)

hamiltonian_data = HamiltonianData.build_hamiltonian_from_file(params)

return hamiltonian_data


@pytest.fixture(scope="package")
def fixture_12_qubit_ham(package_tmp_path) -> HamiltonianData:
params = HamiltonianFileParams(
name="test hamiltonian 12 qubits",
integral_key="diamond_dzvp/cas66",
n_orb=6,
n_elec=6,
do_eri_restore=True,
path_prefix=str(package_tmp_path),
)

hamiltonian_data = HamiltonianData.build_hamiltonian_from_file(params)

return hamiltonian_data


@pytest.fixture(scope="package")
def fixture_4_qubit_ham_and_trial_wf(
fixture_4_qubit_ham: HamiltonianData,
) -> Tuple[HamiltonianData, TrialWavefunctionData]:
params = PerfectPairingPlusTrialWavefunctionParams(
name="pp_test_wf_1",
hamiltonian_params=fixture_4_qubit_ham.params,
heuristic_layers=tuple(),
do_pp=True,
restricted=True,
path_prefix=fixture_4_qubit_ham.params.path_prefix,
)

trial_wf = TrialWavefunctionData.build_pp_plus_trial_from_dependencies(
params, dependencies={fixture_4_qubit_ham.params: fixture_4_qubit_ham}
)

return fixture_4_qubit_ham, trial_wf


@pytest.fixture(scope="package")
def fixture_8_qubit_ham_and_trial_wf(
fixture_8_qubit_ham: HamiltonianData,
) -> Tuple[HamiltonianData, TrialWavefunctionData]:
params = PerfectPairingPlusTrialWavefunctionParams(
name="pp_test_wf_qchem",
hamiltonian_params=fixture_8_qubit_ham.params,
heuristic_layers=tuple(),
initial_orbital_rotation=None,
initial_two_body_qchem_amplitudes=np.asarray([0.3, 0.4]),
do_optimization=False,
path_prefix=fixture_8_qubit_ham.params.path_prefix,
)

trial_wf = TrialWavefunctionData.build_pp_plus_trial_from_dependencies(
params, dependencies={fixture_8_qubit_ham.params: fixture_8_qubit_ham}
)

return fixture_8_qubit_ham, trial_wf


@pytest.fixture(scope="package")
def fixture_4_qubit_ham_trial_wf_and_blueprint(
fixture_4_qubit_ham_and_trial_wf,
) -> Tuple[HamiltonianData, TrialWavefunctionData, blueprint.BlueprintData]:
ham_data, trial_wf_data = fixture_4_qubit_ham_and_trial_wf
trial_wf_params = trial_wf_data.params

blueprint_params = blueprint.BlueprintParamsTrialWf(
name="blueprint_test",
trial_wf_params=trial_wf_params,
n_cliffords=17,
qubit_partition=(
tuple(qubit_maps.get_qubits_a_b_reversed(n_orb=trial_wf_params.n_orb)),
),
seed=1,
path_prefix=ham_data.params.path_prefix,
)

bp = blueprint.BlueprintData.build_blueprint_from_dependencies(
blueprint_params, dependencies={trial_wf_params: trial_wf_data}
)

return ham_data, trial_wf_data, bp


@pytest.fixture(scope="package")
def fixture_4_qubit_ham_trial_wf_and_overlap_analysis(
fixture_4_qubit_ham_trial_wf_and_blueprint,
) -> Tuple[HamiltonianData, TrialWavefunctionData, analysis.OverlapAnalysisData]:
"""Construct fixtures for the hamiltonian, trial wavefunction and overlap analysis.

Returns:
ham_data: The hamiltonian for the 4 qubit test system.
trial_wf_data: The trial wavefunction data for the 4 qubit system.
ovlp_analysis: The overlap analysis data used to reconstruct the
wavefunction via shadow tomography.
"""
ham_data, trial_wf_data, bp_data = fixture_4_qubit_ham_trial_wf_and_blueprint
simulated_experiment_params = experiment.SimulatedExperimentParams(
name="test_1",
blueprint_params=bp_data.params,
noise_model_name="None",
noise_model_params=(0,),
n_samples_per_clifford=10,
seed=1,
path_prefix=ham_data.params.path_prefix,
)
exp = experiment.ExperimentData.build_experiment_from_dependencies(
params=simulated_experiment_params, dependencies={bp_data.params: bp_data}
)

analysis_params = analysis.OverlapAnalysisParams(
"TEST_analysis",
experiment_params=exp.params,
k_to_calculate=(1,),
path_prefix=ham_data.params.path_prefix,
)
all_dependencies: Dict[data.Params, data.Data] = {
ham_data.params: ham_data,
trial_wf_data.params: trial_wf_data,
bp_data.params: bp_data,
simulated_experiment_params: exp,
}
ovlp_analysis = analysis.OverlapAnalysisData.build_analysis_from_dependencies(
analysis_params, dependencies=all_dependencies
)

return ham_data, trial_wf_data, ovlp_analysis


def pytest_addoption(parser):
parser.addoption("--skipslow", action="store_true", help="skips slow tests")
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