def test_inner(self, backend):
psi = peps.computational_zeros(2, 3, backend=backend)
psi.apply_circuit([
Gate('H', [], [0]),
Gate('CX', [], [0, 3]),
Gate('H', [], [3]),
])
phi = peps.computational_zeros(2, 3, backend=backend)
self.assertTrue(np.isclose(psi.inner(phi), 0.5))
def test_inner_approx(self, backend):
psi = peps.computational_zeros(2, 3, backend=backend)
psi.apply_circuit([
Gate('H', [], [0]),
Gate('CX', [], [0,3]),
Gate('H', [], [3]),
], update_option=peps.DirectUpdate(ImplicitRandomizedSVD(rank=2)))
phi = peps.computational_zeros(2, 3, backend=backend)
contract_option = peps.BMPS(ReducedSVD(rank=2))
self.assertTrue(backend.isclose(psi.inner(phi, contract_option), 0.5))
def test_amplitude_nonlocal(self, backend):
update_options = [
None,
peps.DirectUpdate(ImplicitRandomizedSVD(rank=2)),
peps.QRUpdate(rank=2),
peps.LocalGramQRUpdate(rank=2),
peps.LocalGramQRSVDUpdate(rank=2),
]
for option in update_options:
with self.subTest(update_option=option):
qstate = peps.computational_zeros(2, 3, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0, 5]),
Gate('CX', [], [1, 3]),
Gate('S', [], [1]),
],
update_option=option)
self.assertTrue(
np.isclose(qstate.amplitude([1, 0, 0, 0, 0, 1]),
1 / np.sqrt(2)))
self.assertTrue(
np.isclose(qstate.amplitude([1, 1, 0, 1, 0, 1]),
1j / np.sqrt(2)))
def test_amplitude(self, backend):
qstate = peps.computational_zeros(2, 3, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0,3]),
Gate('CX', [], [1,4]),
Gate('S', [], [1]),
])
self.assertTrue(backend.isclose(qstate.amplitude([1,0,0,1,0,0]), 1/np.sqrt(2)))
self.assertTrue(backend.isclose(qstate.amplitude([1,1,0,1,1,0]), 1j/np.sqrt(2)))
def test_probablity(self, backend):
qstate = peps.computational_zeros(2, 3, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0,3]),
Gate('CX', [], [1,4]),
Gate('S', [], [1]),
])
self.assertTrue(backend.isclose(qstate.probability([1,0,0,1,0,0]), 1/2))
self.assertTrue(backend.isclose(qstate.probability([1,1,0,1,1,0]), 1/2))
def test_amplitude_local_gram_qr_svd_update(self, backend):
qstate = peps.computational_zeros(2, 3, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0,3]),
Gate('CX', [], [1,4]),
Gate('S', [], [1]),
], update_option=peps.LocalGramQRSVDUpdate(rank=2))
contract_option = peps.BMPS(ReducedSVD(rank=2))
self.assertTrue(backend.isclose(qstate.amplitude([1,0,0,1,0,0], contract_option), 1/np.sqrt(2)))
self.assertTrue(backend.isclose(qstate.amplitude([1,1,0,1,1,0], contract_option), 1j/np.sqrt(2)))
def test_amplitude_approx(self, backend):
qstate = peps.computational_zeros(2, 3, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('H', [], [1]),
Gate('CX', [], [0,3]),
Gate('CX', [], [1,4]),
Gate('S', [], [1]),
], update_option=peps.DirectUpdate(ImplicitRandomizedSVD(rank=2)))
contract_option = peps.BMPS(ReducedSVD(rank=2))
self.assertTrue(backend.isclose(qstate.amplitude([1,0,0,1,0,0], contract_option), 1/np.sqrt(2)))
self.assertTrue(backend.isclose(qstate.amplitude([1,1,0,1,1,0], contract_option), 1j/np.sqrt(2)))
def test_expectation(self, backend):
qstate = peps.computational_zeros(2, 3, 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 = peps.computational_zeros(2, 3, 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_expectation_use_cache_approx(self, backend):
qstate = peps.computational_zeros(2, 3, backend=backend)
qstate.apply_circuit([
Gate('X', [], [0]),
Gate('CX', [], [0,3]),
Gate('H', [], [2]),
], update_option=peps.DirectUpdate(ImplicitRandomizedSVD(rank=2)))
observable = 1.5 * Observable.sum([
Observable.Z(0) * 2,
Observable.Z(1),
Observable.Z(2) * 2,
Observable.Z(3),
])
contract_option = peps.BMPS(ReducedSVD(rank=2))
self.assertTrue(backend.isclose(qstate.expectation(observable, use_cache=True, contract_option=contract_option), -3))
def test_expectation_single_layer(self, backend):
qstate = peps.computational_zeros(2, 3, 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),
])
contract_option = peps.SingleLayer(ImplicitRandomizedSVD(rank=2))
self.assertTrue(backend.isclose(qstate.expectation(observable, contract_option=contract_option), -3))
def test_expectation_use_cache(self, backend):
qstate = peps.computational_zeros(2, 3, 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(backend.isclose(qstate.expectation(observable, use_cache=True), -3))
cache = peps.make_expectation_cache(qstate, qstate)
self.assertTrue(backend.isclose(qstate.expectation(observable, use_cache=cache), -3))
self.assertTrue(backend.isclose(qstate.norm(cache=cache), 1))
def test_add(self, backend):
psi = peps.computational_zeros(2, 3, backend=backend)
phi = peps.computational_ones(2, 3, backend=backend)
self.assertTrue(np.isclose((psi + phi).norm(), np.sqrt(2)))