def test_UI(bc_MPS, g=0.5):
# Test a time evolution against exact diagonalization for finite bc
L = 10
dt = 0.01
if bc_MPS == 'finite':
L = 6
model_pars = dict(L=L,
Jx=0.,
Jy=0.,
Jz=-4.,
hx=2. * g,
bc_MPS=bc_MPS,
conserve=None)
M = SpinChain(model_pars)
state = ([[1 / np.sqrt(2), -1 / np.sqrt(2)]] * L
) # pointing in (-x)-direction
psi = tenpy.networks.mps.MPS.from_product_state(M.lat.mps_sites(),
state,
bc=bc_MPS)
psi.test_sanity()
U = make_U(M.calc_H_MPO(), dt * 1j, which='I')
if bc_MPS == 'finite':
ED = tenpy.algorithms.exact_diag.ExactDiag(M)
ED.build_full_H_from_mpo()
ED.full_diagonalization()
psiED = ED.mps_to_full(psi)
psiED /= psiED.norm()
UED = ED.exp_H(dt)
for i in range(30):
psi = apply_mpo(psi, U, {})
psiED = npc.tensordot(UED, psiED, ('ps*', [0]))
assert (np.abs(np.abs(npc.inner(psiED, ED.mps_to_full(psi))) - 1) <
1e-2)
if bc_MPS == 'infinite':
psiTEBD = psi.copy()
TEBD_params = {'dt': dt, 'N_steps': 1}
EngTEBD = tenpy.algorithms.tebd.Engine(psiTEBD, M, TEBD_params)
for i in range(30):
EngTEBD.run()
psi = apply_mpo(psi, U, {})
print(np.abs(psi.overlap(psiTEBD) - 1))
print(psi.norm)
#This test fails
assert (np.abs(np.abs(psi.overlap(psiTEBD)) - 1) < 1e-2)
def test_apply_mpo(bc_MPS):
L = 4
g = 0.5
model_pars = dict(L=L,
Jx=0.,
Jy=0.,
Jz=-4.,
hx=2. * g,
bc_MPS=bc_MPS,
conserve=None)
M = SpinChain(model_pars)
state = ([[1 / np.sqrt(2), -1 / np.sqrt(2)]] * L
) # pointing in (-x)-direction
psi = tenpy.networks.mps.MPS.from_product_state(M.lat.mps_sites(),
state,
bc=bc_MPS)
H = M.calc_H_MPO()
Eexp = H.expectation_value(psi)
psi2 = apply_mpo(psi, H, {})
Eapply = psi2.overlap(psi)
#The following norm is false. Don't know how to avoid this due to guessed Svalues
print(psi2.norm)
if bc_MPS == 'infinite': Eapply /= L
assert np.abs(Eexp - Eapply) < 1e-5
