def test_canonical_flip(self):
mpiprint(
0, '\n' + '=' * 50 + '\nCanonical Peps Flipping test\n' + '-' * 50)
from cyclopeps.tools.peps_tools import PEPS
Nx = 5
Ny = 5
d = 2
D = 3
chi = 10
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
normalize=False,
canonical=True)
norm0 = peps.calc_norm()
peps.flip()
norm1 = peps.calc_norm()
peps.flip()
norm2 = peps.calc_norm()
mpiprint(0, 'Norms = {},{},{}'.format(norm0, norm1, norm2))
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-3)
self.assertTrue(abs((norm0 - norm2) / norm0) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_canonical_rotate_z2(self):
mpiprint(
0, '\n' + '=' * 50 +
'\nCanonical Peps Rotation test (Z2 Symmetry)\n' + '-' * 50)
from cyclopeps.tools.peps_tools import PEPS
Nx = 3
Ny = 3
d = 2
D = 3
chi = 10
Zn = 2
backend = 'numpy'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False,
canonical=True)
norm0 = peps.calc_norm()
peps.rotate()
norm1 = peps.calc_norm()
peps.rotate(clockwise=False)
norm2 = peps.calc_norm()
mpiprint(0, 'Norms = {},{},{}'.format(norm0, norm1, norm2))
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-3)
self.assertTrue(abs((norm0 - norm2) / norm0) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_rotate_ctf(self):
mpiprint(0,
'\n' + '=' * 50 + '\nPeps Rotation test (ctf)\n' + '-' * 50)
from cyclopeps.tools.peps_tools import PEPS
Nx = 3
Ny = 3
d = 2
D = 3
chi = 100
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
normalize=False,
backend='ctf')
norm0 = peps.calc_norm()
peps.rotate()
norm1 = peps.calc_norm()
peps.rotate(clockwise=False)
norm2 = peps.calc_norm()
mpiprint(0, 'Norms = {},{},{}'.format(norm0, norm1, norm2))
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-5)
self.assertTrue(abs((norm0 - norm2) / norm0) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_canonical_normalization_z2(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nCanonical Peps Normalization test (Z2 Symmetry) \n' + '-' * 50)
Nx = 3
Ny = 5
d = 2
D = 3
chi = 10
Zn = 2
backend = 'numpy'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False,
canonical=True)
norm = peps.calc_norm(chi=chi)
norm = peps.normalize()
mpiprint(0, 'Norm = {}'.format(norm))
self.assertTrue(abs(1.0 - norm) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_normalization_Z3(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps (5x5) Normalization test with Z3 Symmetry\n' + '-' * 50)
Nx = 5
Ny = 5
d = 2
D = 6
chi = 50
Zn = 3 # Zn symmetry (here, Z3)
dZn = 2
backend = 'ctf'
# Generate random PEPS
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
dZn=dZn,
backend=backend,
normalize=False)
# Compute the norm (2 ways for comparison)
peps_sparse = peps.make_sparse()
norm0 = peps.calc_norm(chi=chi)
norm1 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Symmetric Sparse Norm = {}'.format(norm1))
mpiprint(0, 'Symmetric Dense Norm = {}'.format(norm0))
self.assertTrue(abs((norm0 - norm1) / norm1) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_energy_contraction_ones_z2(self):
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps Energy (Ham=Identity, peps=ones, Z2 symmetry) calculation\n'
+ '-' * 50)
# Create a PEPS
from cyclopeps.tools.peps_tools import PEPS
Nx = 2
Ny = 2
d = 2
D = 3
Zn = 2
backend = 'numpy'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=1000,
Zn=2,
backend=backend,
normalize=False)
# Set all tensor values to 1
for xind in range(Nx):
for yind in range(Ny):
peps[xind][yind].fill_all(1.)
# Get the Hamiltonian
from cyclopeps.ops.identity import return_op
ham = return_op(Nx, Ny, sym='Z2', backend=backend)
# Calculate initial norm
norm0 = peps.calc_norm() * 4.
mpiprint(0, 'Norm (routine) = {}'.format(norm0))
# Perform the Exact energy calculation:
bra = einsum('LDWCM,lMXcu->LDluWXCc', peps[0][0],
peps[0][1]).remove_empty_ind(0).remove_empty_ind(
0).remove_empty_ind(0).remove_empty_ind(0)
bra = einsum('WXCc,CdYRm->dRWXYcm', bra,
peps[1][0]).remove_empty_ind(0).remove_empty_ind(0)
bra = einsum('WXYcm,cmZru->ruWXYZ', bra,
peps[1][1]).remove_empty_ind(0).remove_empty_ind(0)
norm1 = einsum('WXYZ,WXYZ->', bra, bra.conj())
norm1 = norm1 * 4.
mpiprint(0, 'Norm (explicit) = {}'.format(norm1))
tmp = einsum('WXYZ,wxYZ->WXwx', bra, bra.conj())
E1 = einsum('WXwx,WXwx->', tmp, ham[0][0][0])
tmp = einsum('WXYZ,wXyZ->WYwy', bra, bra.conj())
E1 += einsum('WYwy,WYwy->', tmp, ham[1][0][0])
tmp = einsum('WXYZ,WXyz->YZyz', bra, bra.conj())
E1 += einsum('YZyz,YZyz->', tmp, ham[0][1][0])
tmp = einsum('WXYZ,WxYz->XZxz', bra, bra.conj())
E1 += einsum('XZxz,XZxz->', tmp, ham[1][1][0])
mpiprint(0,
'Explicitly computed energy (not normalized) = {}'.format(E1))
# Contract Energy again
E2 = peps.calc_op(ham, normalize=False)
mpiprint(0, 'Energy via peps Method (not normalized) = {}'.format(E2))
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-10)
print('Check here {}, {}, {}, {}'.format(norm0, norm1, E1, E2))
self.assertTrue(abs((norm0 - E1) / norm0) < 1e-10)
self.assertTrue(abs((norm0 - E2) / norm0) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_normalization_ctf(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps (5x5) Normalization test without Symmetry (ctf)\n' +
'-' * 50)
Nx = 5
Ny = 5
d = 2
D = 6
chi = 10
Zn = None
backend = 'ctf'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=True)
norm = peps.calc_norm(chi=chi)
mpiprint(0, 'Norm = {}'.format(norm))
self.assertTrue(abs(1.0 - norm) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_energy_contraction_heis_z2(self):
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps Energy (Ham=Heisenberg, peps=random, Z2 symmetry) calculation\n'
+ '-' * 50)
# Create a PEPS
from cyclopeps.tools.peps_tools import PEPS
Nx = 2
Ny = 2
d = 2
D = 3
Zn = 2
backend = 'ctf'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=1000,
Zn=2,
backend=backend,
normalize=False)
# Get the Hamiltonian
from cyclopeps.ops.heis import return_op
ham = return_op(Nx, Ny, sym='Z2', backend=backend)
# Calculate initial norm
norm0 = peps.calc_norm()
mpiprint(0, 'Norm (routine) = {}'.format(norm0))
# Perform the Exact energy calculation:
bra = einsum('LDWCM,lMXcu->LDluWXCc', peps[0][0],
peps[0][1]).remove_empty_ind(0).remove_empty_ind(
0).remove_empty_ind(0).remove_empty_ind(0)
bra = einsum('WXCc,CdYRm->dRWXYcm', bra,
peps[1][0]).remove_empty_ind(0).remove_empty_ind(0)
bra = einsum('WXYcm,cmZru->ruWXYZ', bra,
peps[1][1]).remove_empty_ind(0).remove_empty_ind(0)
norm1 = einsum('WXYZ,WXYZ->', bra, bra.conj())
norm1 = norm1
mpiprint(0, 'Norm (explicit) = {}'.format(norm1))
#print(ham[0][0][0])
tmp = einsum('WXYZ,wxYZ->WXwx', bra, bra.conj())
E1 = einsum('WXwx,WXwx->', tmp, ham[0][0][0])
tmp = einsum('WXYZ,wXyZ->WYwy', bra, bra.conj())
E1 += einsum('WYwy,WYwy->', tmp, ham[1][0][0])
tmp = einsum('WXYZ,WXyz->YZyz', bra, bra.conj())
E1 += einsum('YZyz,YZyz->', tmp, ham[0][1][0])
tmp = einsum('WXYZ,WxYz->XZxz', bra, bra.conj())
E1 += einsum('XZxz,XZxz->', tmp, ham[1][1][0])
E1 = E1
mpiprint(0,
'Explicitly computed energy (not normalized) = {}'.format(E1))
# Contract Energy again
E2 = peps.calc_op(ham, normalize=False)
mpiprint(0, 'Energy via peps Method (not normalized) = {}'.format(E2))
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-10)
self.assertTrue(abs((E2 - E1) / E1) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_energy_itf_contraction_ones(self):
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps Energy (Ham=ITF, peps=ones, no symmetry) calculation\n' +
'-' * 50)
# Create a PEPS
from cyclopeps.tools.peps_tools import PEPS
Nx = 2
Ny = 2
d = 2
D = 3
backend = 'ctf'
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=1000,
normalize=False,
backend=backend)
# Set all tensor values to 1
for xind in range(Nx):
for yind in range(Ny):
peps[xind][yind].fill_all(1.)
# Get the Hamiltonian
from cyclopeps.ops.itf import return_op
ham = return_op(Nx, Ny, (1., 2.), backend=backend)
# Calculate initial norm
norm0 = peps.calc_norm()
mpiprint(0, 'Norm = {}'.format(norm0))
# Perform the Exact energy calculation:
bra = einsum('LDWCM,lMXcu->WXCc', peps[0][0], peps[0][1])
bra = einsum('WXCc,CdYRm->WXYcm', bra, peps[1][0])
bra = einsum('WXYcm,cmZru->WXYZ', bra, peps[1][1])
norm1 = einsum('WXYZ,WXYZ->', bra, bra.conj())
mpiprint(0, 'Explicitly computed norm = {}'.format(norm1))
tmp = einsum('WXYZ,wxYZ->WXwx', bra, bra.conj())
E1 = einsum('WXwx,WXwx->', tmp, ham[0][0][0])
tmp = einsum('WXYZ,wXyZ->WYwy', bra, bra.conj())
E1 += einsum('WYwy,WYwy->', tmp, ham[1][0][0])
tmp = einsum('WXYZ,WXyz->YZyz', bra, bra.conj())
E1 += einsum('YZyz,YZyz->', tmp, ham[0][1][0])
tmp = einsum('WXYZ,WxYz->XZxz', bra, bra.conj())
E1 += einsum('XZxz,XZxz->', tmp, ham[1][1][0])
mpiprint(0,
'Explicitly computed energy (not normalized) = {}'.format(E1))
# Contract Energy again
E2 = peps.calc_op(ham, normalize=False)
mpiprint(0, 'Energy (routine) = {}'.format(E2))
self.assertTrue(abs((E2 - E1) / E1) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_flip_Z2_ctf(self):
mpiprint(
0, '\n' + '=' * 50 + '\nPeps Z2 Flipping test (ctf)\n' + '-' * 50)
from cyclopeps.tools.peps_tools import PEPS
Nx = 5
Ny = 5
d = 2
D = 6
Zn = 2
chi = 10
backend = 'ctf'
# Generate random PEPS
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False)
# Compute the norm (2 ways for comparison)
norm0 = peps.calc_norm(chi=chi)
peps_sparse = peps.make_sparse()
norm1 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Symmetric Dense Norm = {}'.format(norm0))
mpiprint(0, 'Symmetric Sparse Norm = {}'.format(norm1))
# Rotate the PEPS
peps.flip()
norm2 = peps.calc_norm(chi=chi)
peps_sparse = peps.make_sparse()
norm3 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Flipped Symmetric Dense Norm = {}'.format(norm2))
mpiprint(0, 'Flipped Symmetric Sparse Norm = {}'.format(norm3))
self.assertTrue(abs((norm0 - norm1) / norm1) < 1e-3)
self.assertTrue(abs((norm0 - norm2) / norm2) < 1e-3)
self.assertTrue(abs((norm0 - norm3) / norm3) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_energy_contraction(self):
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps Energy (Ham=Identity, peps=random, no symmetry) calculation\n'
+ '-' * 50)
# Create a PEPS
from cyclopeps.tools.peps_tools import PEPS
Nx = 2
Ny = 2
d = 2
D = 3
backend = 'ctf'
peps = PEPS(Nx=Nx, Ny=Ny, d=d, D=D, chi=1000, backend=backend)
# Get the Hamiltonian
from cyclopeps.ops.identity import return_op
ham = return_op(Nx, Ny, backend=backend)
# Calculate initial norm
norm0 = peps.calc_norm() * 4.
# Perform the Exact energy calculation:
bra = einsum('LDWCM,lMXcu->WXCc', peps[0][0], peps[0][1])
bra = einsum('WXCc,CdYRm->WXYcm', bra, peps[1][0])
bra = einsum('WXYcm,cmZru->WXYZ', bra, peps[1][1])
norm1 = einsum('WXYZ,WXYZ->', bra, bra.conj()) * 4.
tmp = einsum('WXYZ,wxYZ->WXwx', bra, bra.conj())
E1 = einsum('WXwx,WXwx->', tmp, ham[0][0][0])
tmp = einsum('WXYZ,wXyZ->WYwy', bra, bra.conj())
E1 += einsum('WYwy,WYwy->', tmp, ham[1][0][0])
tmp = einsum('WXYZ,WXyz->YZyz', bra, bra.conj())
E1 += einsum('YZyz,YZyz->', tmp, ham[0][1][0])
tmp = einsum('WXYZ,WxYz->XZxz', bra, bra.conj())
E1 += einsum('XZxz,XZxz->', tmp, ham[1][1][0])
# Contract Energy again
E2 = peps.calc_op(ham, normalize=False)
self.assertTrue(abs((norm0 - norm1) / norm0) < 1e-10)
mpiprint(0, 'Passed Norm1')
self.assertTrue(abs((norm0 - E1) / norm0) < 1e-10)
mpiprint(0, 'Passed E1')
mpiprint(0, 'Norm from calc_norm = {}'.format(norm0))
mpiprint(0, 'Norm from exact contraction {}'.format(norm1))
mpiprint(0, 'Norm from Energy calc op = {}'.format(E2))
mpiprint(0, 'Norm from Energy exact contraction {}'.format(E1))
#mpiprint(0,norm1,E1,norm0,E2,abs((norm0-E2)/norm0))
self.assertTrue(abs((norm0 - E2) / norm0) < 1e-10)
mpiprint(0, 'Passed\n' + '=' * 50)
def test_normalization_Z2_ctf(self):
from cyclopeps.tools.peps_tools import PEPS
mpiprint(
0, '\n' + '=' * 50 +
'\nPeps (5x5) Normalization test with Z2 Symmetry (ctf)\n' +
'-' * 50)
Nx = 5
Ny = 5
d = 2
D = 6
chi = 10
Zn = 2 # Zn symmetry (here, Z2)
backend = 'ctf'
# Generate random PEPS
peps = PEPS(Nx=Nx,
Ny=Ny,
d=d,
D=D,
chi=chi,
Zn=Zn,
backend=backend,
normalize=False)
# Compute the norm (2 ways for comparison)
norm0 = peps.calc_norm(chi=chi)
peps_sparse = peps.make_sparse()
norm1 = peps_sparse.calc_norm(chi=chi)
mpiprint(0, 'Symmetric Dense Norm = {}'.format(norm0))
mpiprint(0, 'Symmetric Sparse Norm = {}'.format(norm1))
# Normalize the PEPS
norm2 = peps.normalize()
peps_sparse = peps.make_sparse()
norm3 = peps_sparse.calc_norm(chi=chi)
mpiprint(0,
'Symmetric Dense Norm (After normalized) = {}'.format(norm2))
mpiprint(0,
'Symmetric Sparse Norm (After normalized) = {}'.format(norm3))
# Do some assertions to check if passed
self.assertTrue(abs((norm0 - norm1) / norm1) < 1e-3)
self.assertTrue(abs(1.0 - norm2) < 1e-3)
self.assertTrue(abs(1.0 - norm3) < 1e-3)
mpiprint(0, 'Passed\n' + '=' * 50)