def test_offset(scheme):
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m = Mol(Quantity(0), [ph] * 2)
mlist = MolList([m] * 2, Quantity(17), scheme=scheme)
mpo1 = Mpo(mlist)
assert mpo1.is_hermitian()
f1 = mpo1.full_operator()
evals1, _ = np.linalg.eigh(f1.asnumpy())
offset = Quantity(0.123)
mpo2 = Mpo(mlist, offset=offset)
f2 = mpo2.full_operator()
evals2, _ = np.linalg.eigh(f2.asnumpy())
assert np.allclose(evals1 - offset.as_au(), evals2)
def test_scheme4():
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m1 = Mol(Quantity(0), [ph])
m2 = Mol(Quantity(0), [ph]*2)
mlist1 = MolList([m1, m2], Quantity(17), 4)
mlist2 = MolList([m1, m2], Quantity(17), 3)
mpo4 = Mpo(mlist1)
assert mpo4.is_hermitian()
# for debugging
f = mpo4.full_operator()
mpo3 = Mpo(mlist2)
assert mpo3.is_hermitian()
# makeup two states
mps4 = Mps()
mps4.mol_list = mlist1
mps4.use_dummy_qn = True
mps4.append(np.array([1, 0]).reshape((1,2,1)))
mps4.append(np.array([0, 1]).reshape((1,2,1)))
mps4.append(np.array([0.707, 0.707]).reshape((1,2,1)))
mps4.append(np.array([1, 0]).reshape((1,2,1)))
mps4.build_empty_qn()
e4 = mps4.expectation(mpo4)
mps3 = Mps()
mps3.mol_list = mlist2
mps3.append(np.array([1, 0]).reshape((1,2,1)))
mps3.append(np.array([1, 0]).reshape((1,2,1)))
mps3.append(np.array([0, 1]).reshape((1,2,1)))
mps3.append(np.array([0.707, 0.707]).reshape((1,2,1)))
mps3.append(np.array([1, 0]).reshape((1,2,1)))
e3 = mps3.expectation(mpo3)
assert pytest.approx(e4) == e3
def test_scheme4():
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m1 = Mol(Quantity(0), [ph])
m2 = Mol(Quantity(0), [ph] * 2)
model4 = HolsteinModel([m1, m2], Quantity(17), 4)
model3 = HolsteinModel([m1, m2], Quantity(17), 3)
mpo4 = Mpo(model4)
assert mpo4.is_hermitian()
# for debugging
f = mpo4.full_operator()
mpo3 = Mpo(model3)
assert mpo3.is_hermitian()
# makeup two states
mps4 = Mps()
mps4.model = model4
mps4.append(np.array([1, 0]).reshape((1, 2, 1)))
mps4.append(np.array([0, 0, 1]).reshape((1, -1, 1)))
mps4.append(np.array([0.707, 0.707]).reshape((1, 2, 1)))
mps4.append(np.array([1, 0]).reshape((1, 2, 1)))
mps4.build_empty_qn()
e4 = mps4.expectation(mpo4)
mps3 = Mps()
mps3.model = model3
mps3.append(np.array([1, 0]).reshape((1, 2, 1)))
mps3.append(np.array([1, 0]).reshape((1, 2, 1)))
mps3.append(np.array([0, 1]).reshape((1, 2, 1)))
mps3.append(np.array([0.707, 0.707]).reshape((1, 2, 1)))
mps3.append(np.array([1, 0]).reshape((1, 2, 1)))
e3 = mps3.expectation(mpo3)
assert pytest.approx(e4) == e3
def test_symbolic_mpo(nsites, nterms):
possible_operators = ["sigma_+", "sigma_-", "sigma_z"]
ham_terms = []
for i in range(nterms):
op_list = [
Op(random.choice(possible_operators), j) for j in range(nsites)
]
ham_terms.append(Op.product(op_list) * random.random())
basis = [BasisHalfSpin(i) for i in range(nsites)]
model = Model(basis, ham_terms)
mpo = Mpo(model)
dense_mpo = mpo.full_operator()
qutip_ham = get_spin_hamiltonian(ham_terms)
assert np.allclose(dense_mpo, qutip_ham.data.todense())