def test_add_mpo(self):
h = MPO_rand_herm(12, 5)
h2 = h + h
assert max(h2[6].shape) == 10
t = h.trace()
t2 = h2.trace()
assert_allclose(2 * t, t2)
def test_mpo_rand_herm_trace_and_identity_like(self):
op = MPO_rand_herm(20, bond_dim=5, phys_dim=3, upper_ind_id='foo{}')
t = op.trace()
assert t != 0.0
Id = MPO_identity_like(op)
assert_allclose(Id.trace(), 3**20)
Id.site[0] *= 3 / 3**20
op += Id
assert_allclose(op.trace(), t + 3)
def test_mpo_rand_herm_and_trace(self, dtype, cyclic):
op = MPO_rand_herm(20,
bond_dim=5,
phys_dim=3,
dtype=dtype,
cyclic=cyclic)
assert_allclose(op.H @ op, 1.0)
tr_val = op.trace()
assert tr_val != 0.0
assert_allclose(tr_val.imag, 0.0, atol=1e-14)
def test_sites_mpo_mps_product(self, cyclic):
k = MPS_rand_state(13, 7, cyclic=cyclic)
X = MPO_rand_herm(3, 5, sites=[3, 6, 7], nsites=13, cyclic=cyclic)
b = k.H
align_TN_1D(k, X, b, inplace=True)
assert (k & X & b) ^ ...
def test_sites_mpo_mps_product(self, cyclic):
k = MPS_rand_state(13, 7, cyclic=cyclic)
X = MPO_rand_herm(3, 5, sites=[3, 6, 7], nsites=13, cyclic=cyclic)
b = k.H
k.align_(X, b)
assert (k & X & b) ^ ...
def test_approx_fn(self, fn):
A = MPO_rand_herm(10, 7, normalize=True)
xe = sum(fn(eigvalsh(A.to_dense())))
xf = approx_spectral_function(A, fn, tol=0.1, verbosity=2)
assert_allclose(xe, xf, rtol=0.5)
def test_constructing_tridiag_works(self):
A = MPO_rand_herm(10, 7)
for _ in construct_lanczos_tridiag_MPO(A, 5):
pass