def test_dmrg_shared_exec_iterative_solve_one_sweep():
max_mps_rank = 5
num = 7
mpo_rank = 5
size = 5
num_iter = 10
T.set_backend("numpy")
h = qtn.MPO_rand_herm(num, mpo_rank, size)
dmrg_quimb = qtn.DMRG2(h, bond_dims=[max_mps_rank])
h_tensors = load_quimb_tensors(h)
mps_tensors = load_quimb_tensors(dmrg_quimb.state)
# dmrg based on ad
mps_tensors, energy = dmrg_shared_exec_iterative_solve(
h_tensors, mps_tensors, num_iter=num_iter, max_mps_rank=max_mps_rank)
# dmrg based on quimb
opts = {'max_bond': max_mps_rank}
for _ in range(num_iter):
quimb_energy = dmrg_quimb.sweep_right(canonize=True,
verbosity=0,
**opts)
# We only test on energy (lowest eigenvalue of h), rather than the output
# mps (eigenvector), because the eigenvectors can vary a lot while keeping the
# eigenvalue unchanged.
assert (abs(energy - quimb_energy) < 1e-5)
def test_no_default_term(self):
N = 10
builder = qtn.SpinHam(1 / 2)
for i in range(N - 1):
builder[i, i + 1] += 1.0, 'Z', 'Z'
H = builder.build_mpo(N)
dmrg = qtn.DMRG2(H)
dmrg.solve(verbosity=1)
assert dmrg.energy == pytest.approx(-2.25)
def test_dmrg_one_sweep():
max_mps_rank = 5
num = 4
T.set_backend("numpy")
h = qtn.MPO_ham_heis(num)
dmrg_quimb = qtn.DMRG2(h, bond_dims=[max_mps_rank])
h_tensors = load_quimb_tensors(h)
mps_tensors = load_quimb_tensors(dmrg_quimb.state)
# dmrg based on ad
mps_tensors, energy = dmrg(h_tensors,
mps_tensors,
max_mps_rank=max_mps_rank)
# dmrg based on quimb
opts = {'max_bond': max_mps_rank}
quimb_energy = dmrg_quimb.sweep_right(canonize=True, verbosity=0, **opts)
# We only test on energy (lowest eigenvalue of h), rather than the output
# mps (eigenvector), because the eigenvectors can vary a lot while keeping the
# eigenvalue unchanged.
assert (abs(energy - quimb_energy) < 1e-8)