Added tests for Hamiltonians, propagators and signals

Author: nwittler

test/test_two_qubits.py

@@ -3,6 +3,7 @@
 """
 
 import copy
+import pickle
 import numpy as np
 
 # Main C3 objects
@@ -422,6 +423,11 @@
 
 opt.set_exp(exp)
 
+with open("test/two_qubit_data.pickle", "rb") as filename:
+    test_data = pickle.load(filename)
+
+gen_signal, ts = generator.generate_signals(X90p_q1)
+
 
 def run_optim() -> float:
     """
@@ -442,65 +448,19 @@ def test_signals() -> None:
     """
     Test if the generated signals are correct at the first and last sample and at sample #12.
     """
-    gen_signal, ts = generator.generate_signals(X90p_q1)
-    assert np.max(gen_signal["d1"]["values"]) == 442864262.3882865
-    assert gen_signal["d1"]["values"][0] == 0
-    assert gen_signal["d1"]["values"][12] == -18838613.771749068
-    assert gen_signal["d1"]["values"][-1] == 47702706.02427947
-    assert ts[0] == 5e-12
-    assert ts[12] == 1.25e-10
-    assert ts[-1] == 6.995e-09
-
-
-def test_hamiltonian_of_t() -> None:
-    signal, ts = generator.generate_signals(X90p_q1)
-    cflds_t = signal["d1"]["values"][16]
-    hdrift, hks = model.get_Hamiltonians()
-    hamiltonian = hdrift.numpy() + cflds_t.numpy() * hks["d1"].numpy()
-    precomp_hamiltonian = [
-        [-2.37105674e+05+0.j, -4.39488626e+05+0.j,  6.42557156e-09+0.j,
-        -1.39929598e+07+0.j,  4.80420449e-08+0.j, -5.12932000e+01+0.j,
-        -9.18583639e-10+0.j, -7.52868617e+02+0.j, -4.60493681e-10+0.j,],
-        [-4.39488626e+05+0.j,  3.51895367e+10+0.j, -1.05404930e+06+0.j,
-        -4.21166702e-06+0.j, -1.39659734e+07+0.j, -3.38001933e-07+0.j,
-        -1.70280246e+05+0.j, -3.42845091e-07+0.j, -1.12262365e+03+0.j,],
-        [ 8.21276864e-09+0.j, -1.05404930e+06+0.j,  6.88776096e+10+0.j,
-        -2.87659692e+01+0.j, -4.35040944e-06+0.j, -1.40273221e+07+0.j,
-        6.11788641e-06+0.j, -5.67966729e+05+0.j, -5.92691374e-06+0.j,],
-        [-1.39929598e+07+0.j, -4.06274727e-06+0.j, -2.87659692e+01+0.j,
-        3.14112585e+10+0.j, -1.97916311e+05+0.j, -2.68973468e-05+0.j,
-        -1.97926130e+07+0.j, -5.91563241e-06+0.j, -1.02890811e+02+0.j,],
-        [ 5.04042505e-08+0.j, -1.39659734e+07+0.j, -1.84797479e-06+0.j,
-        -1.97916311e+05+0.j,  6.65933163e+10+0.j, -2.45595202e+05+0.j,
-        1.03870858e-05+0.j, -1.97587093e+07+0.j,  1.03713136e-05+0.j,],
-        [-5.12932000e+01+0.j, -4.57618676e-07+0.j, -1.40273221e+07+0.j,
-        -2.68926902e-05+0.j, -2.45595202e+05+0.j,  1.00297674e+11+0.j,
-        3.15118982e+04+0.j,  7.62930650e-06+0.j, -1.97559869e+07+0.j,],
-        [-1.21110840e-09+0.j, -1.70280246e+05+0.j,  6.09999393e-06+0.j,
-        -1.97926130e+07+0.j,  1.07057873e-05+0.j,  3.15118982e+04+0.j,
-        6.15061801e+10+0.j,  6.36236697e+05+0.j, -2.25524516e-05+0.j,],
-        [-7.52868617e+02+0.j, -3.44707736e-07+0.j, -5.67966729e+05+0.j,
-        -5.93053357e-06+0.j, -1.97587093e+07+0.j,  8.58298081e-06+0.j,
-        6.36236697e+05+0.j,  9.66811548e+10+0.j,  1.30140463e+06+0.j,],
-        [-5.01826729e-10+0.j, -1.12262365e+03+0.j, -5.92318845e-06+0.j,
-        -1.02890811e+02+0.j,  1.04309182e-05+0.j, -1.97559869e+07+0.j,
-        -2.25571082e-05+0.j,  1.30140463e+06+0.j,  1.30377086e+11+0.j,],
-    ]
 
-    assert (hamiltonian - precomp_hamiltonian < 1e-15).any()
+    for key in gen_signal:
+        assert (gen_signal[key]["values"].numpy() == test_data["signal"][key]["values"].numpy()).all()
+    assert (ts.numpy() == test_data["ts"].numpy()).all()
+
+
+def test_hamiltonians() -> None:
+    hdrift, hks = model.get_Hamiltonians()
+    assert (hdrift.numpy() == test_data["hdrift"].numpy()).any()
+    for key in hks:
+        assert (hks[key].numpy() == test_data["hks"][key].numpy()).all()
 
 
 def test_propagation() -> None:
-    signal, ts = generator.generate_signals(X90p_q1)
-    propagator = exp.propagation(signal, ts, "X90p:Id")
-    precomputed = np.array(
-        [
-            [-5.42470209e-02 - 7.55118401e-01j, 1.31475007e-04 - 1.66893081e-04j,
-             5.81482026e-08 + 4.56344704e-08j, 6.51945328e-01 - 3.21728860e-02j,
-             1.20669716e-04 + 5.09331324e-05j, 3.19775048e-08 + 1.04614907e-07j,
-             -9.41458634e-03 - 2.63990622e-02j, 3.55609615e-05 - 2.18958182e-04j,
-             -6.42952047e-07 - 1.07156481e-06j]
-        ]
-     )
-
-    assert ((propagator.numpy()[3] - precomputed) < 1e-8).all()
+    propagator = exp.propagation(gen_signal, ts, "X90p:Id")
+    assert (propagator.numpy() == test_data["propagator"].numpy()).all()