def test_apply_spinful_fermionop(self):
"""
Make sure the spin-orbital reordering is working by comparing
apply operation
"""
wfn = Wavefunction([[2, 0, 2]])
wfn.set_wfn(strategy='random')
wfn.normalize()
cirq_wf = to_cirq(wfn).reshape((-1, 1))
op_to_apply = FermionOperator()
test_state = copy.deepcopy(wfn)
test_state.set_wfn('zero')
for p, q, r, s in product(range(2), repeat=4):
op = FermionOperator(
((2 * p, 1), (2 * q + 1, 1), (2 * r + 1, 0), (2 * s, 0)),
coefficient=numpy.random.randn())
op_to_apply += op + hermitian_conjugated(op)
test_state += wfn.apply(op + hermitian_conjugated(op))
opmat = get_sparse_operator(op_to_apply, n_qubits=4).toarray()
new_state_cirq = opmat @ cirq_wf
# this part is because we need to pass a normalized wavefunction
norm_constant = new_state_cirq.conj().T @ new_state_cirq
new_state_cirq /= numpy.sqrt(norm_constant)
new_state_wfn = from_cirq(new_state_cirq.flatten(), thresh=1.0E-12)
new_state_wfn.scale(numpy.sqrt(norm_constant))
self.assertTrue(
numpy.allclose(test_state.get_coeff((2, 0)),
new_state_wfn.get_coeff((2, 0))))
def test_evolve_spinful_fermionop(self):
"""
Make sure the spin-orbital reordering is working by comparing
time evolution
"""
wfn = Wavefunction([[2, 0, 2]])
wfn.set_wfn(strategy='random')
wfn.normalize()
cirq_wf = to_cirq(wfn).reshape((-1, 1))
op_to_apply = FermionOperator()
for p, q, r, s in product(range(2), repeat=4):
op = FermionOperator(
((2 * p, 1), (2 * q + 1, 1), (2 * r + 1, 0), (2 * s, 0)),
coefficient=numpy.random.randn())
op_to_apply += op + hermitian_conjugated(op)
opmat = get_sparse_operator(op_to_apply, n_qubits=4).toarray()
dt = 0.765
new_state_cirq = scipy.linalg.expm(-1j * dt * opmat) @ cirq_wf
new_state_wfn = from_cirq(new_state_cirq.flatten(), thresh=1.0E-12)
test_state = wfn.time_evolve(dt, op_to_apply)
self.assertTrue(
numpy.allclose(test_state.get_coeff((2, 0)),
new_state_wfn.get_coeff((2, 0))))
def test_wick(self):
"""Check that wick performs the proper restructuring of the
density matrix given a string of indexes.
"""
norb = 4
nele = 4
s_z = 0
wfn = Wavefunction([[nele, s_z, norb]])
numpy.random.seed(seed=1)
wfn.set_wfn(strategy='random')
wfn.normalize()
rdms = wfn._compute_rdm(4)
out1 = wick.wick('k j^', list(rdms), True)
two = numpy.eye(norb, dtype=out1.dtype) * 2.0
self.assertRaises(ValueError, wick.wick, 'k0 j', list(rdms))
self.assertTrue(numpy.allclose(two - out1.T, rdms[0]))
self.assertRaises(ValueError, wick.wick, 'k^ l i^ j', list(rdms), True)
out2 = wick.wick('k l i^ j^', list(rdms), True)
h_1 = numpy.zeros_like(out1)
for i in range(norb):
h_1[:, :] += out2[:, i, :, i] / (norb * 2 - nele - 1)
self.assertAlmostEqual(numpy.std(out1 + h_1), 0.)
out2a = wick.wick('k l^ i^ j', list(rdms), True)
self.assertAlmostEqual(out2a[2, 3, 0, 1], -rdms[1][0, 3, 2, 1])
out3 = wick.wick('k l m i^ j^ n^', list(rdms), True)
h_2 = numpy.zeros_like(out2)
for i in range(norb):
h_2[:, :, :, :] += out3[:, i, :, :, i, :] / (norb * 2 - nele - 2)
self.assertAlmostEqual(numpy.std(out2 - h_2), 0.)
out4 = wick.wick('k l m x i^ j^ n^ y^', list(rdms), True)
h_3 = numpy.zeros_like(out3)
for i in range(norb):
h_3[:, :, :, :, :, :] += out4[:, i, :, :, :, i, :, :] / (norb * 2 -
nele - 3)
self.assertAlmostEqual(numpy.std(out3 + h_3), 0.)