def test_svd_on_averaged(self):
"""Use IQ data gathered from the hardware."""
# This data is primarily oriented along the real axis with a slight tilt.
# There is a large offset in the imaginary dimension when comparing qubits
# 0 and 1. The data below is averaged IQ data on two qubits.
iq_data = [
[[-6.20601501e14, -1.33257051e15], [-1.70921324e15, -4.05881657e15]],
[[-5.80546502e14, -1.33492509e15], [-1.65094637e15, -4.05926942e15]],
[[-4.04649069e14, -1.33191056e15], [-1.29680377e15, -4.03604815e15]],
[[-2.22203874e14, -1.30291309e15], [-8.57663429e14, -3.97784973e15]],
[[-2.92074029e13, -1.28578530e15], [-9.78824053e13, -3.92071056e15]],
[[1.98056981e14, -1.26883024e15], [3.77157017e14, -3.87460328e15]],
[[4.29955888e14, -1.25022995e15], [1.02340118e15, -3.79508679e15]],
[[6.38981344e14, -1.25084614e15], [1.68918514e15, -3.78961044e15]],
[[7.09988897e14, -1.21906634e15], [1.91914171e15, -3.73670664e15]],
[[7.63169115e14, -1.20797552e15], [2.03772603e15, -3.74653863e15]],
]
self.create_experiment_data(iq_data)
iq_svd = SVD()
iq_svd.train(np.asarray([datum["memory"] for datum in self.iq_experiment.data()]))
np.testing.assert_array_almost_equal(
iq_svd.parameters.main_axes[0], np.array([0.99633018, 0.08559302])
)
np.testing.assert_array_almost_equal(
iq_svd.parameters.main_axes[1], np.array([0.99627747, 0.0862044])
)
def test_simple_data(self):
"""
A simple setting where the IQ data of qubit 0 is oriented along (1,1) and
the IQ data of qubit 1 is oriented along (1,-1).
"""
iq_data = [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [-1.0, 1.0]],
[[-1.0, -1.0], [1.0, -1.0]]]
self.create_experiment(iq_data)
iq_svd = SVD()
iq_svd.train([datum["memory"] for datum in self.iq_experiment.data()])
# qubit 0 IQ data is oriented along (1,1)
self.assertTrue(
np.allclose(iq_svd._main_axes[0],
np.array([-1, -1]) / np.sqrt(2)))
# qubit 1 IQ data is oriented along (1, -1)
self.assertTrue(
np.allclose(iq_svd._main_axes[1],
np.array([-1, 1]) / np.sqrt(2)))
processed, _ = iq_svd(np.array([[1, 1], [1, -1]]))
expected = np.array([-1, -1]) / np.sqrt(2)
self.assertTrue(np.allclose(processed, expected))
processed, _ = iq_svd(np.array([[2, 2], [2, -2]]))
self.assertTrue(np.allclose(processed, expected * 2))
# Check that orthogonal data gives 0.
processed, _ = iq_svd(np.array([[1, -1], [1, 1]]))
expected = np.array([0, 0])
self.assertTrue(np.allclose(processed, expected))
def test_on_single_shot(self):
"""Test the SVD node on single shot data."""
# The data has the shape
iq_data = [
# Circuit no. 1, 5 shots
[
[[-84858304.0, -111158232.0]],
[[-92671216.0, -74032944.0]],
[[-74049176.0, -22372804.0]],
[[-87495592.0, -72437616.0]],
[[-52787048.0, -63746976.0]],
],
# Circuit no. 2, 5 shots
[
[[-70452328.0, -91318008.0]],
[[-82281464.0, -72478736.0]],
[[-107760368.0, -77817680.0]],
[[-47410012.0, -48451952.0]],
[[68308432.0, -72074976.0]],
],
# Circuit no. 3, 5 shots
[
[[47855768.0, -52185604.0]],
[[-64009220.0, -79507104.0]],
[[51899032.0, -80737864.0]],
[[118873272.0, -43621036.0]],
[[24438894.0, -84970704.0]],
],
]
self.create_experiment_data(iq_data, single_shot=True)
iq_svd = SVD()
iq_svd.train(
np.asarray(
[datum["memory"] for datum in self.iq_experiment.data()]))
processed_data = iq_svd(np.array(iq_data))
# Test the output of the axis
self.assertEqual(len(iq_svd.parameters.main_axes), 1)
self.assertTrue(
np.allclose(iq_svd.parameters.main_axes[0],
[0.92727304, 0.37438577]))
# Test the output data
self.assertEqual(processed_data.shape, (3, 5, 1))
test_values = np.array(processed_data[0].flatten(), dtype=float)
expected = np.array(
[-0.4982860, -0.4383349, -0.10852355, -0.38971727, -0.07045186],
dtype=float)
self.assertTrue(np.allclose(test_values, expected, atol=1e-06))
# Test in a data processor, will catch, e.g., unumpy issues
data_processor = DataProcessor("memory", [SVD()])
data_processor.train(self.iq_experiment.data())
processed_data = data_processor(self.iq_experiment.data())
self.assertEqual(processed_data.shape, (3, 5, 1))
def test_simple_data(self):
"""
A simple setting where the IQ data of qubit 0 is oriented along (1,1) and
the IQ data of qubit 1 is oriented along (1,-1).
"""
iq_data = [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [-1.0, 1.0]],
[[-1.0, -1.0], [1.0, -1.0]]]
self.create_experiment(iq_data)
iq_svd = SVD()
iq_svd.train(
np.asarray(
[datum["memory"] for datum in self.iq_experiment.data()]))
# qubit 0 IQ data is oriented along (1,1)
np.testing.assert_array_almost_equal(iq_svd.parameters.main_axes[0],
np.array([-1, -1]) / np.sqrt(2))
# qubit 1 IQ data is oriented along (1, -1)
np.testing.assert_array_almost_equal(iq_svd.parameters.main_axes[1],
np.array([-1, 1]) / np.sqrt(2))
# This is n_circuit = 1, n_slot = 2, the input shape should be [1, 2, 2]
# Then the output shape will be [1, 2] by reducing the last dimension
processed_data = iq_svd(np.array([[[1, 1], [1, -1]]]))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed_data),
np.array([[-1, -1]]) / np.sqrt(2),
)
processed_data = iq_svd(np.array([[[2, 2], [2, -2]]]))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed_data),
2 * np.array([[-1, -1]]) / np.sqrt(2),
)
# Check that orthogonal data gives 0.
processed_data = iq_svd(np.array([[[1, -1], [1, 1]]]))
np.testing.assert_array_almost_equal(
unp.nominal_values(processed_data),
np.array([[0, 0]]),
)