def test_realistic(self):
seed_rand(42)
ham = MPO_ham_heis(20)
dmrg = DMRG2(ham, bond_dims=[2, 4])
dmrg.solve()
rho_ab = dmrg.state.ptr(range(6, 14))
xf = approx_spectral_function(rho_ab, lambda x: x,
tol=0.1, verbosity=2)
assert_allclose(1.0, xf, rtol=0.6, atol=0.001)
def test_realistic(self):
ham = MPO_ham_heis(20)
dmrg = DMRG2(ham, bond_dims=[4, 8])
dmrg.solve()
sysa, sysb = range(3, 9), range(12, 17)
rho_ab_pt = PTPTLazyMPS(dmrg.state, sysa, sysb)
xf = approx_spectral_function(rho_ab_pt, lambda x: x, beta_tol=1e-6,
tol=0.05, verbosity=2, max_bond=20)
assert_allclose(1, xf, rtol=0.5, atol=0.1)
def test_realistic_ent(self):
n = 12
sysa, sysb = range(3, 6), range(6, 8)
ham = MPO_ham_heis(n)
dmrg = DMRG2(ham, bond_dims=[2, 4])
dmrg.solve()
rho_ab_pt = PTPTLazyMPS(dmrg.state, sysa, sysb)
psi0 = dmrg.state.to_dense()
lne = logneg_subsys(psi0, [2] * n, sysa=sysa, sysb=sysb)
lnx = log2(
approx_spectral_function(rho_ab_pt, abs, tol=0.1, verbosity=2))
assert_allclose(lne, lnx, rtol=0.5, atol=0.1)
def test_realistic_ent(self):
n = 12
sysa, sysb = range(3, 6), range(6, 8)
sysab = (*sysa, *sysb)
ham = MPO_ham_heis(n)
dmrg = DMRG2(ham, bond_dims=[10])
dmrg.solve()
psi0 = dmrg.state.to_dense()
lne = logneg_subsys(psi0, [2] * n, sysa=sysa, sysb=sysb)
rho_ab = dmrg.state.ptr(sysab, rescale_sites=True)
rho_ab_pt = rho_ab.partial_transpose(range(3))
lnx = log2(
approx_spectral_function(rho_ab_pt, abs, tol=0.1, verbosity=2))
assert_allclose(lne, lnx, rtol=0.6, atol=0.1)
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)