def test_solve_bigger(self):
n = 14
chi = 16
ham = MPO_ham_mbl(n, dh=8, seed=42)
p0 = MPS_computational_state('00110111000101')
dmrgx = DMRGX(ham, p0, chi)
assert dmrgx.solve(tol=1e-5, sweep_sequence='R')
assert dmrgx.state[0].dtype == float
def test_explicit_sweeps(self):
# import pdb; pdb.set_trace()
n = 8
chi = 16
ham = MPO_ham_mbl(n, dh=5, run=42)
p0 = MPS_neel_state(n).expand_bond_dimension(chi)
b0 = p0.H
align_TN_1D(p0, ham, b0, inplace=True)
en0 = np.asscalar(p0 & ham & b0 ^ ...)
dmrgx = DMRGX(ham, p0, chi)
dmrgx.sweep_right()
en1 = dmrgx.sweep_left(canonize=False)
assert en0 != en1
dmrgx.sweep_right(canonize=False)
en = dmrgx.sweep_right(canonize=True)
# check normalized
assert_allclose(dmrgx._k.H @ dmrgx._k, 1.0)
k = dmrgx._k.to_dense()
h = ham.to_dense()
el, ev = eigsys(h)
# check variance very low
assert np.abs((k.H @ h @ h @ k) - (k.H @ h @ k)**2) < 1e-12
# check exactly one eigenvalue matched well
assert np.sum(np.abs(el - en) < 1e-12) == 1
# check exactly one eigenvector is matched with high fidelity
ovlps = (ev.H @ k).A**2
big_ovlps = ovlps[ovlps > 1e-12]
assert_allclose(big_ovlps, [1])
# check fully
assert is_eigenvector(k, h)
def test_explicit_sweeps(self):
n = 8
chi = 16
ham = MPO_ham_mbl(n, dh=4, seed=42)
p0 = MPS_rand_state(n, 2).expand_bond_dimension(chi)
b0 = p0.H
p0.align_(ham, b0)
en0 = (p0 & ham & b0) ^ ...
dmrgx = DMRGX(ham, p0, chi)
dmrgx.sweep_right()
en1 = dmrgx.sweep_left(canonize=False)
assert en0 != en1
dmrgx.sweep_right(canonize=False)
en = dmrgx.sweep_right(canonize=True)
# check normalized
assert_allclose(dmrgx._k.H @ dmrgx._k, 1.0)
k = dmrgx._k.to_dense()
h = ham.to_dense()
el, ev = eigh(h)
# check variance very low
assert np.abs((k.H @ h @ h @ k) - (k.H @ h @ k)**2) < 1e-12
# check exactly one eigenvalue matched well
assert np.sum(np.abs(el - en) < 1e-12) == 1
# check exactly one eigenvector is matched with high fidelity
ovlps = (ev.H @ k).A**2
big_ovlps = ovlps[ovlps > 1e-12]
assert_allclose(big_ovlps, [1])
# check fully
assert is_eigenvector(k, h, tol=1e-10)