def test_all_gate_methods(self, Circ):
rots = ['rx', 'ry', 'rz']
g1s = ['x', 'y', 'z', 's', 't', 'h', 'iden']
g2s = ['cx', 'cy', 'cz', 'cnot', 'swap']
g_rand = np.random.permutation(rots + g1s + g2s + ['u3'])
psi0 = qtn.MPS_rand_state(2, 2)
circ = Circ(2, psi0)
for g in g_rand:
if g == 'u3':
angles = np.random.uniform(0, 2 * np.pi, size=3)
i = np.random.choice([0, 1])
args = (*angles, i)
elif g in rots:
theta = np.random.uniform(0, 2 * np.pi)
i = np.random.choice([0, 1])
args = (theta, i)
elif g in g1s:
i = np.random.choice([0, 1])
args = (i, )
elif g in g2s:
i, j = np.random.permutation([0, 1])
args = (i, j)
getattr(circ, g)(*args)
assert circ.psi.H @ circ.psi == pytest.approx(1.0)
assert abs((circ.psi.H & psi0) ^ all) < 0.99999999
def rand_tn1d_sect(n, bd, dtype=complex):
mps = qtn.MPS_rand_state(n + 2, bd, dtype=dtype)
mpo = qtn.MPO_rand_herm(n + 2, 5, dtype=dtype)
norm = qtn.TensorNetwork(qtn.align_TN_1D(mps.H, mpo, mps))
lix = qtn.bonds(norm[0], norm[1])
rix = qtn.bonds(norm[n], norm[n + 1])
to = norm[1:n + 1]
return qtn.TNLinearOperator1D(to, lix, rix, 1, n + 1)
def rand_tn1d_sect(n, bd, dtype=complex):
mps = qtn.MPS_rand_state(n + 2, bd, dtype=dtype)
mpo = qtn.MPO_rand_herm(n + 2, 5, dtype=dtype)
norm = qtn.TensorNetwork(qtn.align_TN_1D(mps.H, mpo, mps))
# greedy not good and contracting with large bsz
norm.structure_bsz = 2
lix = qtn.bonds(norm[0], norm[1])
rix = qtn.bonds(norm[n], norm[n + 1])
to = norm[1:n + 1]
return qtn.TNLinearOperator1D(to, lix, rix, 1, n + 1)
def test_non_trans_invar(self):
n = 10
tf = 1.0
p0 = qtn.MPS_rand_state(n, bond_dim=1)
H = qtn.NNI_ham_mbl(n, dh=1.7, cyclic=False, seed=42)
print(H)
assert H.special_sites == {(i, i + 1) for i in range(n)}
tebd = qtn.TEBD(p0, H)
tebd.update_to(tf, tol=1e-3)
p0d = p0.to_dense()
Hd = qu.ham_mbl(n, dh=1.7, cyclic=False, seed=42, sparse=True)
evo = qu.Evolution(p0d, Hd)
evo.update_to(tf)
assert qu.expec(tebd.pt.to_dense(), evo.pt) == pytest.approx(1.0)
def heis_pbc():
L = 10
chi = 8
dtype = 'float32'
psi0 = qtn.MPS_rand_state(L, chi, cyclic=True, seed=42).astype(dtype)
H = qtn.MPO_ham_heis(L, cyclic=True).astype(dtype)
def norm_fn(psi):
factor = (psi & psi).contract(all, optimize='random-greedy')
return psi / factor**0.5
def loss_fn(psi, H):
k, H, b = qtn.align_TN_1D(psi, H, psi)
energy = (k & H & b).contract(all, optimize='random-greedy')
return energy
en_ex = qu.groundenergy(qu.ham_heis(L, cyclic=True, sparse=True))
return psi0, H, norm_fn, loss_fn, en_ex
def ham_mbl_pbc_complex():
L = 10
chi = 8
dtype = 'complex64'
psi0 = qtn.MPS_rand_state(L, chi, cyclic=True, seed=42).astype(dtype)
ham_opts = {'cyclic': True, 'dh': 0.7, 'dh_dim': 3, 'seed': 42}
H = qtn.MPO_ham_mbl(L, **ham_opts).astype(dtype)
def norm_fn(psi):
factor = (psi.H & psi).contract(all, optimize='random-greedy')
return psi * factor**-0.5
def loss_fn(psi, H):
k, H, b = qtn.align_TN_1D(psi, H, psi.H)
energy = (k & H & b).contract(all, optimize='random-greedy')
return real(energy)
en_ex = qu.groundenergy(qu.ham_mbl(L, sparse=True, **ham_opts))
return psi0, H, norm_fn, loss_fn, en_ex
def rand_mps(num, rank, size=2):
"""
Generate random MPS.
"""
mps = qtn.MPS_rand_state(num, rank, phys_dim=size)
return load_quimb_tensors(mps)