def test_inner(self, backend):
psi = statevector.computational_zeros(6, backend=backend)
psi.apply_circuit([
Gate('H', [], [0]),
Gate('CX', [], [0, 3]),
Gate('H', [], [3]),
])
phi = statevector.computational_zeros(6, backend=backend)
self.assertTrue(np.isclose(psi.inner(phi), 0.5))
def test_probablity(self, backend):
qstate = statevector.computational_zeros(6, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0, 3]),
Gate('CX', [], [1, 4]),
Gate('S', [], [1]),
])
self.assertTrue(
np.isclose(qstate.probability([1, 0, 0, 1, 0, 0]), 1 / 2))
self.assertTrue(
np.isclose(qstate.probability([1, 1, 0, 1, 1, 0]), 1 / 2))
def test_amplitude(self, backend):
qstate = statevector.computational_zeros(6, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0, 3]),
Gate('CX', [], [1, 4]),
Gate('S', [], [1]),
])
self.assertTrue(
np.isclose(qstate.amplitude([1, 0, 0, 1, 0, 0]), 1 / np.sqrt(2)))
self.assertTrue(
np.isclose(qstate.amplitude([1, 1, 0, 1, 1, 0]), 1j / np.sqrt(2)))
def test_expectation(self, backend):
qstate = statevector.computational_zeros(6, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('CX', [], [0, 3]),
Gate('H', [], [2]),
])
observable = 1.5 * Observable.sum([
Observable.Z(0) * 2,
Observable.Z(1),
Observable.Z(2) * 2,
Observable.Z(3),
])
self.assertTrue(np.isclose(qstate.expectation(observable), -3))
def test_norm(self, backend):
qstate = statevector.computational_zeros(6, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0, 3]),
Gate('CX', [], [1, 4]),
Gate('S', [], [1]),
])
self.assertTrue(np.isclose(qstate.norm(), 1))
qstate *= 2
self.assertTrue(np.isclose(qstate.norm(), 2))
qstate /= 2j
self.assertTrue(np.isclose(qstate.norm(), 1))
def test_add(self, backend):
psi = statevector.computational_zeros(6, backend=backend)
phi = statevector.computational_ones(6, backend=backend)
self.assertTrue(np.isclose((psi + phi).norm(), np.sqrt(2)))