def approx_propagator(cls, mpo, dt, thresh=0):
"""
e^-iHdt : approximate propagator MPO from Runge-Kutta methods
"""
mps = Mps()
mps.mol_list = mpo.mol_list
mps.dim = [1] * (mpo.site_num + 1)
mps.qn = [[0]] * (mpo.site_num + 1)
mps.qnidx = mpo.site_num - 1
mps.qntot = 0
mps.threshold = thresh
for impo in range(mpo.site_num):
ms = xp.ones((1, mpo[impo].shape[1], 1),
dtype=backend.complex_dtype)
mps.append(ms)
approx_mpo_t0 = cls.from_mps(mps)
approx_mpo = approx_mpo_t0.evolve(mpo, dt)
return approx_mpo
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
