Add python interface tests

.ci-scripts/build_and_test.sh

@@ -263,7 +263,7 @@ then
     timed_message "Run regression tests"
 
     mpi_exe=$(grep 'MPIEXEC_EXECUTABLE' "${hostconfig_path}" | cut -d'"' -f2 | sed 's/;/ /g')
-    pytest -v -s -m regression --mpi-exec="${mpi_exe}"
+    pytest -v -s -m "not performance" --mpi-exec="${mpi_exe}"
 
     timed_message "Quandary tests completed"
 fi

.github/workflows/test.yml

@@ -50,7 +50,7 @@ jobs:
       run: |
         spack env activate .spack_env
         python -m pip install -e . --prefer-binary
-        pytest -v -s tests/regression --mpi-opt="--oversubscribe"
+        pytest -v -s -m "not performance" --mpi-opt="--oversubscribe"
 
     - name: Push packages and update index
       env:

.pytest.ini

@@ -1,5 +1,5 @@
 [pytest]
-addopts = --ignore=blt --ignore=uberenv_libs
+addopts = --ignore=blt --ignore=uberenv_libs --ignore=.spack_env --ignore=spack
 
 markers =
     regression: mark a test as a regression test

quandary.py

@@ -1426,12 +1426,11 @@ def execute(*, runtype="simulation", ncores=1, config_filename="config.cfg", dat
     if not verbose:
         runcommand += " --quiet"
     # If parallel run, specify runcommand. Default is 'mpirun -np ', unless batch args are given, then currently using 'srun -n', see end of this file for changing that to other batch systems.
-    if ncores > 1:
-        if len(batchargs)>0:
-            myrun = batch_run  # currently set to "srun -n"
-        else:
-            myrun = mpi_exec
-        runcommand = f"{myrun} {ncores} " + runcommand
+    if len(batchargs)>0:
+        myrun = batch_run  # currently set to "srun -n"
+    else:
+        myrun = mpi_exec
+    runcommand = f"{myrun} {ncores} " + runcommand
     if verbose:
         print("Running Quandary ... ")
 

tests/python/conftest.py

@@ -0,0 +1,13 @@
+"""Shared pytest fixtures for Python tests."""
+
+import pytest
+
+
+@pytest.fixture
+def mpi_exec(request):
+    """Get MPI executor and options from pytest options."""
+    executor = request.config.getoption("--mpi-exec")
+    options = request.config.getoption("--mpi-opt")
+    if executor != "mpirun":
+        return f"{executor} {options} -n"
+    return f"mpirun {options} -np"

tests/python/test_env_variable.py

@@ -120,15 +120,6 @@ def test_file_as_base_directory(quandary, request, tmp_path, clean_env_var, mpi_
     assert file_path in str(excinfo.value)
 
 
-@pytest.fixture
-def mpi_exec(request):
-    """Get MPI executor from pytest option."""
-    executor = request.config.getoption("--mpi-exec")
-    if executor != "mpirun":
-        return f"{executor} -n "
-    return "mpirun -np "
-
-
 @pytest.fixture
 def cd_tmp_path(tmp_path):
     """Change to a temporary directory for the test and return afterward."""

tests/python/test_evalControls.py

@@ -0,0 +1,37 @@
+import numpy as np
+import os
+from pytest import approx
+from quandary import Quandary
+
+
+def test_evalControls_updates_timestep(mpi_exec, tmp_path, request):
+    """
+    Test that evalControls properly updates timestep (dT) to match sampling rate.
+    """
+    datadir_path = os.path.join(tmp_path, request.node.name)
+
+    T = 5.0
+    quandary = Quandary(
+        Ne=[2],
+        freq01=[4.0],
+        T=T,
+        verbose=False
+    )
+
+    original_dT = quandary.dT
+    original_nsteps = quandary.nsteps
+
+    # Test evalControls with different sampling rate
+    points_per_ns = 2
+    time, _, _ = quandary.evalControls(points_per_ns=points_per_ns, datadir=datadir_path, mpi_exec=mpi_exec)
+
+    expected_nsteps = int(np.floor(T * points_per_ns))
+    expected_dT = T / expected_nsteps
+
+    assert time[0] == approx(0.0)
+    assert time[-1] == approx(T)
+    assert time[1] - time[0] == approx(expected_dT)
+
+    # Verify original settings are restored
+    assert quandary.dT == approx(original_dT)
+    assert quandary.nsteps == original_nsteps

tests/python/test_example_cnot.py

@@ -0,0 +1,168 @@
+import os
+import pytest
+import numpy as np
+from quandary import Quandary
+from utils import assert_results_equal
+
+# Mark all tests in this file as regression tests
+pytestmark = pytest.mark.regression
+
+EXPECTED_LENGTH = 1222
+EXPECTED_INFIDELITY = 8.878046657001537e-05
+
+EXPECTED_PT = [
+    [
+        2.87734000820681, 10.2084828702097, 3.56051692161081, 10.024110887425099, 0.119689588026969,
+        -0.562436390448981, -8.93345535901571, 3.44484827705617, 2.06286670752315, 3.89249555192154
+    ],
+    [
+        -2.9180159231296896, 12.2087515999371, -23.089156431207, -4.19328393381954, -5.69791415120672,
+        -2.6886978940074, 11.226563801034, -5.036756985169349, 10.5696800602371, -1.62524585211734
+    ],
+]
+
+EXPECTED_QT = [
+    [
+        -0.348841624741643, 8.53241095717569, -4.4376504465481, 3.8174295155143803, 10.4326608744893,
+        -0.095498326189503, 5.25168638153997, -2.8110244912535003, -7.59465730686269, 2.47175448714252
+    ],
+    [
+        6.427777598244551, -3.3248790239003, -4.5100523360834295, 6.087543657080141, -10.646609746997699,
+        11.392012292061, -3.3794131918228403, -8.74872063545075, 7.451333086132051, -0.0766950105907232
+    ],
+]
+
+EXPECTED_ENERGY = [
+    [
+        [
+            0.0, 0.34901327142301, 0.473011956850604, 0.555285026597096, 0.069842096071947,
+            0.0494333733507312, 0.0412389950511918, 0.0449879240940497, 0.092441027177265, 8.40973352444999e-05
+        ],
+        [
+            0.0, 0.329712745694427, 0.631427344995695, 0.705222483334731, 0.195021533108613,
+            0.105515613439278, 0.049564530621508, 0.0440871191855571, 0.0578785086924327, 4.80529018699317e-05
+        ],
+        [
+            1.0, 0.683972616987854, 0.513519662449962, 0.428832898801532, 0.811508875591578,
+            0.899674588776469, 0.940734433368898, 0.96799101447616, 0.931984272724262, 0.99993177546283
+        ],
+        [
+            1.0, 0.637301365961018, 0.382041036035193, 0.310659591977753, 0.923627496026638,
+            0.94537642527642, 0.968462041771141, 0.942933942897476, 0.917696192253395, 0.999936075198039
+        ],
+    ],
+    [
+        [
+            0.0, 0.987133111411216, 0.562433193103199, 0.769075465888702, 0.351891781635455,
+            0.349514600195487, 0.377439464911376, 0.245808211210325, 0.739043033057218, 8.03904470221222e-05
+        ],
+        [
+            1.0, 0.0333701947461882, 0.408345432340385, 0.275536717225252, 0.607727573743172,
+            0.65903560390506, 0.615696981888637, 0.734508095105381, 0.256558085054453, 0.999939827908553
+        ],
+        [
+            0.0, 0.958580676348588, 0.583890977393659, 0.788362984366934, 0.433827024077578,
+            0.929364744019182, 0.419775139103579, 0.871558222749287, 0.211581398264166, 0.99991222208567
+        ],
+        [
+            1.0, 0.0209160175402728, 0.445330397327355, 0.167024833245191, 0.606553620954251,
+            0.0620850525500678, 0.587088414384345, 0.148125472053068, 0.792817484475808, 6.75603725578727e-05
+        ],
+    ],
+]
+
+EXPECTED_POPULATION = [
+    [
+        [
+            1.0, 0.650986728594546, 0.526988043322036, 0.444714973597238, 0.930157904163221,
+            0.950566626905697, 0.958761005215817, 0.955012076207555, 0.907558973131879, 0.999915902999063
+        ],
+        [
+            1.0, 0.670287254332655, 0.368572655214771, 0.294777516899832, 0.804978467167963,
+            0.894484386858014, 0.950435469700482, 0.955912881149872, 0.942121491658557, 0.999951947459561
+        ],
+        [
+            0.0, 0.316027383043538, 0.486480337824313, 0.571167101492937, 0.188491124757645,
+            0.100325411595143, 0.0592655670333261, 0.0320089859467604, 0.0680157277087126, 6.82250014792584e-05
+        ],
+        [
+            0.0, 0.362698634062056, 0.617958964216483, 0.68934040830239, 0.0763725043254758,
+            0.0546235750840321, 0.031537958631141, 0.0570660575129625, 0.0823038081685347, 6.39252396400044e-05
+        ],
+    ],
+    [
+        [
+            1.0, 0.0128668886063392, 0.437566807069441, 0.230924534305633, 0.648108218599712,
+            0.650485400060942, 0.622560535355633, 0.75419178909128, 0.260956967251926, 0.999919609887286
+        ],
+        [
+            0.0, 0.966629805280894, 0.591654567870081, 0.724463283009311, 0.392272426533404,
+            0.340964396392231, 0.384303018433353, 0.265491905230048, 0.743441915296537, 6.01724528776744e-05
+        ],
+        [
+            1.0, 0.0414193236828037, 0.416109022880615, 0.211637015927536, 0.566172976271645,
+            0.0706352563524305, 0.580224861298645, 0.128441777673634, 0.788418602168809, 8.7778378639303e-05
+        ],
+        [
+            0.0, 0.979083982482801, 0.554669602924322, 0.832975167034952, 0.393446379397863,
+            0.937914947810385, 0.412911586017937, 0.851874528357371, 0.207182515946122, 0.999932440065121
+        ],
+    ],
+]
+
+# Compare output to expected result for 10 points
+NUM_SAMPLES = 10
+SAMPLE_INDICES = [int(i * (EXPECTED_LENGTH - 1) / (NUM_SAMPLES - 1)) for i in range(NUM_SAMPLES)]
+
+
+def test_example_cnot(mpi_exec, tmp_path, request):
+    """Test CNOT gate optimization using Python interface."""
+    datadir_path = os.path.join(tmp_path, request.node.name)
+
+    freq01 = [4.80595, 4.8601]
+    favg = sum(freq01)/len(freq01)
+    rotfreq = favg*np.ones(len(freq01))
+    T = 200.0
+
+    unitary = np.identity(4)
+    unitary[2, 2] = 0.0
+    unitary[3, 3] = 0.0
+    unitary[2, 3] = 1.0
+    unitary[3, 2] = 1.0
+    n_osc = 2
+    n_levels = 4
+
+    quandary = Quandary(
+        freq01=freq01,
+        Jkl=[0.005],
+        rotfreq=rotfreq,
+        T=T,
+        targetgate=unitary,
+        verbose=False,
+        rand_seed=1234,
+    )
+
+    t, pt, qt, infidelity, energy, population = quandary.optimize(
+        mpi_exec=mpi_exec,
+        maxcores=2,
+        datadir=datadir_path,
+    )
+
+    assert_results_equal(
+        t=t,
+        pt=pt,
+        qt=qt,
+        infidelity=infidelity,
+        energy=energy,
+        population=population,
+        T=T,
+        n_osc=n_osc,
+        n_levels=n_levels,
+        expected_length=EXPECTED_LENGTH,
+        expected_infidelity=EXPECTED_INFIDELITY,
+        expected_pt=EXPECTED_PT,
+        expected_qt=EXPECTED_QT,
+        expected_energy=EXPECTED_ENERGY,
+        expected_population=EXPECTED_POPULATION,
+        sample_indices=SAMPLE_INDICES
+    )