def test_compute_hR_vs_expect(backend, dtype, chi, d): _, _, A_R, H = random_hermitian_system(backend, dtype, chi, d) I = tn.eye(chi, backend=backend, dtype=dtype) hR = ct.compute_hR(A_R, H) result = tn.abs(tn.trace(hR)) compare = ct.twositeexpect([A_R, I, A_R], H) rtol = (2*A_R.size + I.size + H.size) * np.finfo(dtype).eps np.testing.assert_allclose(result.array, compare.array, rtol=rtol)
def test_trace(backend, dtype):
""" Checks that Tensor.trace() works.
"""
shape = (2, 3, 3)
A, _ = testing_utils.safe_randn(shape, backend, dtype)
if A is not None:
np.testing.assert_allclose(
tensornetwork.trace(A).array, A.backend.trace(A.array))
def mpsnorm(mps: ThreeTensors) -> tn.Tensor:
"""
Norm of the MPS.
Args:
mps: The MPS.
Returns:
norm: Its norm.
"""
A_L, C, A_R = mps
A_CR = leftmult(C, A_R)
rho = rholoc(A_L, A_CR)
the_norm = tn.abs(tn.trace(rho))
return the_norm
def vumps_approximate_tm_eigs(C) -> TwoTensors:
"""
Returns the approximate transfer matrix dominant eigenvectors,
rL ~ C^dag C, and lR ~ C Cdag = rLdag, both trace-normalized.
Args:
C: From the MPS.
Returns:
rL, lR: right fixed point of L, left fixed point of R.
"""
rL = C.H @ C
rL /= tn.trace(rL)
lR = rL.H
return rL, lR