Add python test case with constant initial control

tests/python/test_example_cnot_const_init_ctrl.py

@@ -0,0 +1,169 @@
+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 = 3.6979557855976175e-05
+
+EXPECTED_PT = [
+    [
+        69.4004584740497, -56.4748587229514, 14.6194226065187, 41.3022123079845, -67.73628866574009,
+        62.065452347372606, -38.9624100460397, -12.200805256966099, 55.4407922797055, -70.0787542955629
+    ],
+    [
+        73.229765747718, -51.530356030475104, 10.797743240407, 32.9999831592717, -65.9215400319986,
+        68.1157477500591, -36.665422937239306, -22.0688444356676, 53.2241318694266, -70.762850942261
+    ],
+]
+
+EXPECTED_QT = [
+    [
+        69.6429908212518, -56.1952283280093, 17.2100586110721, 40.5719025432599, -67.2782353436477,
+        62.8808620909873, -35.1388165423998, -12.2276107374885, 56.0566382776629, -69.8661090578335
+    ],
+    [
+        74.3860436073452, -49.769725517584696, 14.8400227183943, 35.952563400735, -66.7343819618446,
+        67.61236549795551, -33.3579362154448, -15.5147165351889, 55.498385570977604, -68.6915883259813
+    ],
+]
+
+EXPECTED_ENERGY = [
+    [
+        [
+            0.0, 0.146434155154378, 0.527396222930252, 0.8333688467604, 0.178103890140801,
+            0.520428421328945, 0.341607833241572, 0.430496355266566, 0.414851527192667, 6.96474616175235e-06
+        ],
+        [
+            0.0, 0.299436918554029, 0.623281309650759, 0.727281777811903, 0.29392649895835,
+            0.460459280349417, 0.338491303457183, 0.148098740071313, 0.542120540677145, 6.90391354963638e-06
+        ],
+        [
+            1.0, 0.912189864480567, 0.489755781185604, 0.120883431289255, 0.704407057315191,
+            0.362090662066901, 0.708332294696574, 0.819241990240916, 0.446192955946767, 0.99999708945145
+        ],
+        [
+            1.0, 0.641939061924855, 0.359566686519967, 0.318465944618273, 0.823562554277443,
+            0.657021637296419, 0.611568569732679, 0.602162915288405, 0.596834977772345, 0.999989043578022
+        ],
+    ],
+    [
+        [
+            0.0, 0.849674867882648, 0.311347334072333, 0.920830873299062, 0.267622719176619,
+            0.264811363494957, 0.684475235376846, 0.8088259589714, 0.725432021094394, 7.02591458519224e-06
+        ],
+        [
+            1.0, 0.181716864915869, 0.59806074182981, 0.205466706171419, 0.700728634090143,
+            0.65181512506587, 0.236588536813724, 0.124726329034347, 0.282435940220941, 0.99998895407904
+        ],
+        [
+            0.0, 0.714160901457531, 0.146494043882507, 0.475422969060653, 0.538326634571238,
+            0.878584814887598, 0.248620647645974, 0.189155370811277, 0.412335586770598, 0.999986055714482
+        ],
+        [
+            1.0, 0.254447366037388, 0.944097880652566, 0.398279452064191, 0.493322012669281,
+            0.204788697406206, 0.83031558115938, 0.877292342095324, 0.579796452768217, 1.79659758716049e-05
+        ],
+    ],
+]
+
+EXPECTED_POPULATION = [
+    [
+        [
+            1.0, 0.85356584488177, 0.472603777164144, 0.166631153403343, 0.821896110096733,
+            0.479571579021539, 0.658392167182505, 0.569503645283894, 0.585148473450691, 0.999993035982757
+        ],
+        [
+            1.0, 0.700563081508297, 0.376718690549668, 0.272718222510303, 0.706073501460846,
+            0.539540720136406, 0.66150869709237, 0.851901260630598, 0.457879460111683, 0.999993096979301
+        ],
+        [
+            0.0, 0.0878101355829006, 0.51024421898866, 0.879116569013979, 0.295592943063672,
+            0.63790933838759, 0.291667705819821, 0.180758010409327, 0.553807044816912, 2.91134124264776e-06
+        ],
+        [
+            0.0, 0.358060938162397, 0.640433313690709, 0.681534055688347, 0.176437446110549,
+            0.342978363166151, 0.388431430809871, 0.397837085391043, 0.403165023007325, 1.0957318515284e-05
+        ],
+    ],
+    [
+        [
+            1.0, 0.150325132153501, 0.688652666022062, 0.0791691268646811, 0.732377281060915,
+            0.735188636855528, 0.31552476504723, 0.19117404157906, 0.274567979548965, 0.999992974814333
+        ],
+        [
+            0.0, 0.818283135146458, 0.401939258370617, 0.794533294150787, 0.299271366329053,
+            0.348184875419952, 0.763411463735828, 0.875273671667564, 0.717564060567887, 1.10468138109779e-05
+        ],
+        [
+            1.0, 0.285839098605937, 0.853505956291756, 0.52457703124258, 0.461673365807625,
+            0.121415185566892, 0.75137935287042, 0.810844629838966, 0.587664413993081, 1.39450782101164e-05
+        ],
+        [
+            0.0, 0.745552634049864, 0.0559021195581093, 0.601720548242429, 0.506677987718712,
+            0.795211303056364, 0.16968441938317, 0.122707658584124, 0.420203548011452, 0.999982034920666
+        ],
+    ],
+]
+
+# 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_const_init_ctrl(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,
+        initctrl_MHz=100.0,
+        randomize_init_ctrl=False,
+    )
+
+    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
+    )