def test_discrimination(self):
"""
Test that the discriminator can be trained on the simulated data and
that it can properly discriminate between ground and excited sates.
"""
i0, q0, i1, q1 = 0., -1., 0., 1.
discriminator = LinearIQDiscriminator(self.cal_results, self.qubits,
['00', '11'])
excited_predicted = discriminator.discriminate([[i1, q1, i1, q1]])
ground_predicted = discriminator.discriminate([[i0, q0, i0, q0]])
self.assertEqual(excited_predicted[0], '11')
self.assertEqual(ground_predicted[0], '00')
def filter_and_discriminate(self):
"""
Test the process of discriminating and then applying the discriminator
using a filter.
"""
i0, q0, i1, q1 = 0., -1., 0., 1.
discriminator = LinearIQDiscriminator(self.cal_results, self.qubits,
['00', '11'])
iq_filter = DiscriminationFilter(discriminator)
new_result = iq_filter.apply(self.cal_results)
for nr in new_result.results:
self.assertEqual(nr.meas_level, 2)
for state in new_result.results[0].data.counts.to_dict():
self.assertEqual(state, '0x0')
for state in new_result.results[1].data.counts.to_dict():
self.assertEqual(state, '0x3')
self.assertEqual(len(new_result.get_memory(0)), self.shots)
self.qubits = [0]
discriminator = LinearIQDiscriminator(self.cal_results, self.qubits,
['0', '1'])
self.assertEqual(discriminator.discriminate([[i0, q0]])[0], '0')
self.assertEqual(discriminator.discriminate([[i1, q1]])[0], '1')