def test_split_node_names(backend):
a = tn.Node(np.zeros((2, 3, 4, 5, 6)), backend=backend)
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right, _ = tn.split_node(a,
left_edges,
right_edges,
left_name='left',
right_name='right',
edge_name='edge')
assert left.name == 'left'
assert right.name == 'right'
assert left.edges[-1].name == 'edge'
assert right.edges[0].name == 'edge'
def test_reachable_disconnected_1(backend):
nodes = [tn.Node(np.random.rand(2, 2, 2), backend=backend) for _ in range(4)]
nodes[0][1] ^ nodes[1][0]
nodes[2][1] ^ nodes[3][0]
assert set(tn.reachable([nodes[0], nodes[2]])) == set(nodes)
assert set(tn.reachable([nodes[0]])) == {nodes[0], nodes[1]}
assert set(tn.reachable([nodes[1]])) == {nodes[0], nodes[1]}
assert set(tn.reachable([nodes[0], nodes[1]])) == {nodes[0], nodes[1]}
assert set(tn.reachable([nodes[2]])) == {nodes[2], nodes[3]}
assert set(tn.reachable([nodes[3]])) == {nodes[2], nodes[3]}
assert set(tn.reachable([nodes[2], nodes[3]])) == {nodes[2], nodes[3]}
assert set(tn.reachable([nodes[0], nodes[1], nodes[2]])) == set(nodes)
assert set(tn.reachable([nodes[0], nodes[1], nodes[3]])) == set(nodes)
assert set(tn.reachable([nodes[0], nodes[2], nodes[3]])) == set(nodes)
assert set(tn.reachable([nodes[1], nodes[2], nodes[3]])) == set(nodes)
def test_split_node_full_svd_names(backend):
a = tn.Node(np.random.rand(10, 10), backend=backend)
e1 = a[0]
e2 = a[1]
left, s, right, _, = tn.split_node_full_svd(
a, [e1], [e2],
left_name='left',
middle_name='center',
right_name='right',
left_edge_name='left_edge',
right_edge_name='right_edge')
assert left.name == 'left'
assert s.name == 'center'
assert right.name == 'right'
assert left.edges[-1].name == 'left_edge'
assert s[0].name == 'left_edge'
assert s[1].name == 'right_edge'
assert right.edges[0].name == 'right_edge'
def test_split_node_qr_unitarity_complex(backend):
if backend == "pytorch":
pytest.skip("Complex numbers currently not supported in PyTorch")
if backend == "jax":
pytest.skip("Complex QR crashes jax")
a = tn.Node(np.random.rand(3, 3) + 1j * np.random.rand(3, 3),
backend=backend)
q, r = tn.split_node_qr(a, [a[0]], [a[1]])
q[1] | r[0]
qbar = tn.conj(q)
q[1] ^ qbar[1]
u1 = q @ qbar
qbar[0] ^ q[0]
u2 = qbar @ q
np.testing.assert_almost_equal(u1.tensor, np.eye(3))
np.testing.assert_almost_equal(u2.tensor, np.eye(3))
def test_split_node_rq(dtype, num_charges):
np.random.seed(10)
a = tn.Node(get_random((6, 7, 8, 9, 10), num_charges, dtype=dtype),
backend='symmetric')
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right = tn.split_node_rq(a, left_edges, right_edges)
tn.check_correct([left, right])
result = tn.contract(left[3])
np.testing.assert_allclose(result.tensor.data, a.tensor.data)
assert np.all([
charge_equal(result.tensor._charges[n], a.tensor._charges[n])
for n in range(len(a.tensor._charges))
])
def f(input_vec, blocks, rank, dim, bond_dim, label_len):
mps, edges = create_MPS_labeled(blocks, rank, dim, bond_dim)
data_tensor = []
for p in tf.unstack(input_vec):
data_tensor.append(tn.Node([1 - p, p]))
edges.append(data_tensor[0][0] ^ mps[0][0])
half_len = np.int(rank / 2)
[
edges.append(data_tensor[i][0] ^ mps[i][1])
for i in range(1, half_len)
]
[edges.append(data_tensor[i-label_len][0] ^ mps[i][1]) \
for i in range(half_len + label_len, rank + label_len)]
for k in reversed(range(len(edges))):
A = tn.contract(edges[k])
#result = tf.math.log(A.tensor)
result = A.tensor - tf.math.reduce_max(A.tensor)
return result
def _build_right_envs(self, rpos):
R = tn.Node(np.array([[[1]]]),
backend=self.backend,
name='right_env_{}'.format(self._len))
renvs = [R]
for i in reversed(range(rpos, self._len)):
self.psi.position(i - 1)
nodes = [
R, self.psi.nodes[i], self.H.nodes[i],
tn.conj(self.psi.nodes[i])
]
R = tn.ncon(nodes, [(1, 3, 5), (-1, 2, 1), (-2, 2, 4, 3),
(-3, 4, 5)],
backend=self.backend)
R.set_name('right_env_{}'.format(i))
renvs.append(R)
return renvs
def test_split_node_full_svd_names(num_charges):
np.random.seed(10)
a = tn.Node(get_random((10, 10), num_charges=num_charges),
backend='symmetric')
e1 = a[0]
e2 = a[1]
left, s, right, _, = tn.split_node_full_svd(a, [e1], [e2],
left_name='left',
middle_name='center',
right_name='right',
left_edge_name='left_edge',
right_edge_name='right_edge')
assert left.name == 'left'
assert s.name == 'center'
assert right.name == 'right'
assert left.edges[-1].name == 'left_edge'
assert s[0].name == 'left_edge'
assert s[1].name == 'right_edge'
assert right.edges[0].name == 'right_edge'
def renyiEntropy(n,
w,
h,
M,
randOption,
theta,
phi,
estimateFunc,
arguments,
filename,
d=2,
excludeIndices=[]):
start = datetime.now()
avg = 0
N = w * h
for m in range(M * 2**N * 10):
ops = [
getNonUnitaryRandomOps(d, randOption, theta, phi, vecsNum=n)
for i in range(N)
]
for ind in excludeIndices:
ops[ind] = [tn.Node(np.eye(d, dtype=complex)) for i in range(n)]
estimation = 1
for i in range(n):
expectation = wrapper(estimateFunc,
arguments + [[op[i] for op in ops]])
estimation *= expectation
if estimation > 40:
b = 1
avg += estimation
if m % M == M - 1:
with open(
filename + '_n_' + str(n) + '_w_' + str(w) + '_h_' +
str(h) + '_' + randOption + '_M_' + str(M) + '_m_' +
str(m), 'wb') as f:
pickle.dump(avg, f)
avg = 0
for op in ops:
bops.removeState(op)
end = datetime.now()
with open(filename + '_time_N_' + str(N) + '_M_' + str(M), 'wb') as f:
pickle.dump((end - start).total_seconds(), f)
def test_split_node_qr_names(num_charges):
np.random.seed(10)
a = tn.Node(get_random((2, 3, 4, 5, 6), num_charges=num_charges),
backend='symmetric')
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right = tn.split_node_qr(a,
left_edges,
right_edges,
left_name='left',
right_name='right',
edge_name='edge')
assert left.name == 'left'
assert right.name == 'right'
assert left.edges[-1].name == 'edge'
assert right.edges[0].name == 'edge'
def test_copy_method_with_trace_edges(backend):
a = tn.Node(np.ones([3, 3, 3, 3, 3]), name='mynode',
axis_names=['a', 'b', 'c', 'd', 'e'],
backend=backend)
a.add_edge(tn.Edge(a, 0, name='named_edge1'), 0)
a.add_edge(tn.Edge(a, 1, name='named_edge2'), 1)
a.add_edge(tn.Edge(a, 2, name='named_edge3'), 2)
a.add_edge(tn.Edge(a, 3, name='named_edge4'), 3)
a.add_edge(tn.Edge(a, 4, name='named_edge5'), 4)
a[0] ^ a[3]
a[1] ^ a[4]
b = a.copy()
assert a.name == b.name
assert a.shape == b.shape
assert a.axis_names == b.axis_names
for i in range(len(a.edges)):
assert a[i].name == b[i].name
assert b[0] is b[3]
assert b[1] is b[4]
np.testing.assert_allclose(a.tensor, b.tensor)
def test_constructor(backend):
psi_tensor = np.random.rand(2, 2)
psi_node = tn.Node(psi_tensor, backend=backend)
op = qu.quantum_constructor([psi_node[0]], [psi_node[1]])
assert not op.is_scalar()
assert not op.is_vector()
assert not op.is_adjoint_vector()
assert len(op.out_edges) == 1
assert len(op.in_edges) == 1
assert op.out_edges[0] is psi_node[0]
assert op.in_edges[0] is psi_node[1]
op = qu.quantum_constructor([psi_node[0], psi_node[1]], [])
assert not op.is_scalar()
assert op.is_vector()
assert not op.is_adjoint_vector()
assert len(op.out_edges) == 2
assert len(op.in_edges) == 0
assert op.out_edges[0] is psi_node[0]
assert op.out_edges[1] is psi_node[1]
op = qu.quantum_constructor([], [psi_node[0], psi_node[1]])
assert not op.is_scalar()
assert not op.is_vector()
assert op.is_adjoint_vector()
assert len(op.out_edges) == 0
assert len(op.in_edges) == 2
assert op.in_edges[0] is psi_node[0]
assert op.in_edges[1] is psi_node[1]
with pytest.raises(ValueError):
op = qu.quantum_constructor([], [], [psi_node])
_ = psi_node[0] ^ psi_node[1]
op = qu.quantum_constructor([], [], [psi_node])
assert op.is_scalar()
assert not op.is_vector()
assert not op.is_adjoint_vector()
assert len(op.out_edges) == 0
assert len(op.in_edges) == 0
def __gradient__(self, input_idx: int) -> np.ndarray:
"""
Calculates the gradient of the inner-product <MPS|input_tensor>
with respect to the bond tensor.
:param input_tensor: tensor formed from the input vector
:return: d<MPS|input_tensor>/d{bond}
"""
proj = self.__projection__(input_idx)
bond = self.mps.get_contracted_bond()
leftmost = self.__leftmost__()
if leftmost:
proj[0]['in'] ^ bond['in1']
proj[1]['in'] ^ bond['in2']
proj[2]['in'] ^ bond['r']
else:
proj[0]['in'] ^ bond['l']
proj[1]['in'] ^ bond['in1']
proj[2]['in'] ^ bond['in2']
proj[3]['in'] ^ bond['r']
mps_prod = tn.contractors.auto(proj + [bond]) - tn.Node(self.Ys[:, input_idx])
mps_prod.add_axis_names(['out'])
proj2 = tn.replicate_nodes(proj)
if leftmost:
in1_edge = proj2[0]['in']
in2_edge = proj2[1]['in']
r_edge = proj2[2]['in']
edge_order = [r_edge, in1_edge, in2_edge]
else:
l_edge = proj2[0]['in']
in1_edge = proj2[1]['in']
in2_edge = proj2[2]['in']
r_edge = proj2[3]['in']
edge_order = [l_edge, r_edge, in1_edge, in2_edge]
edge_order.append(mps_prod['out'])
return tn.contractors.auto([mps_prod] + proj2, output_edge_order=edge_order).tensor
def localVecsEstimate(psi: List[tn.Node],
vs: List[List[np.array]],
half='left'):
vs = np.round(vs, 10)
n = len(vs)
result = 1
for copy in range(n):
if half == 'left':
NA = len(vs[0])
curr = bops.multiContraction(psi[NA], psi[NA], '12', '12*')
sites = range(NA - 1, -1, -1)
elif half == 'right':
NA = len(psi) - len(vs[0])
curr = bops.multiContraction(psi[len(psi) - NA - 1],
psi[len(psi) - NA - 1], '01', '01*')
sites = range(NA, len(psi))
psiCopy = bops.copyState(psi)
for alpha in sites:
toEstimate = np.outer(vs[copy][alpha - NA],
np.conj(vs[np.mod(copy + 1, n)][alpha - NA]))
psiCopy[alpha] = bops.permute(bops.multiContraction(psiCopy[alpha], tn.Node(toEstimate), \
'1', '1'), [0, 2, 1])
if half == 'left':
curr = bops.multiContraction(bops.multiContraction(
psiCopy[alpha], curr, '2', '0', cleanOr2=True),
psi[alpha],
'12',
'12*',
cleanOr1=True)
elif half == 'right':
curr = bops.multiContraction(bops.multiContraction(
curr, psiCopy[alpha], '0', '0', cleanOr2=True),
psi[alpha],
'01',
'01*',
cleanOr1=True)
# psiCopy = bops.shiftWorkingSite(psiCopy, alpha, '<<')
result *= np.trace(curr.tensor)
tn.remove_node(curr)
bops.removeState(psiCopy)
return result
def wire_network(tensor_list, give_dense=False):
"""
Convert list of tensor cores into fully wired network of TN Nodes
If give_dense=True, the wired network is contracted together and a
single (large) tensor is returned
"""
num_cores = len(tensor_list)
assert valid_formatting(tensor_list)
# Wire together all internal edges connecting cores
node_list = [tn.Node(core) for core in tensor_list]
for i in range(num_cores):
for j in range(i + 1, num_cores):
node_list[i][j] ^ node_list[j][i]
if give_dense:
edge_order = [node[i] for i, node in enumerate(node_list)]
return contract_network(node_list, edge_order=edge_order)
else:
return node_list
def _add_node(self, A, wires, name="UnnamedNode"):
"""Adds a node to the underlying tensor network.
The node is also added to ``self._nodes`` for bookkeeping.
Args:
A (array): numerical data values for the operator (i.e., matrix form)
wires (list[int]): wires that this operator acts on
name (str): optional name for the node
Returns:
tn.Node: the newly created node
"""
name = "{}{}".format(name, tuple(w for w in wires))
if isinstance(A, tn.Node):
A.set_name(name)
node = A
else:
node = tn.Node(A, name=name)
self._nodes.append(node)
return node
def apply_consecutive_gates(self, i, gate):
"""
Applies a two-qubit gate to the i'th and the (i+1)'th qubit.
The gate must be either a 4x4 matrix or a 2x2x2x2 tensor.
Comment: In tn.split_node, it may make sense to use max_truncation_err
option instead of max_singular_values option, in case there
was a mistake in my note.
Args:
i(int): Index of the qubit.
gate(np.array): 4x4 matrix or 2x2x2x2 tensor.
"""
gate = normalize_gate(gate)
j = (i + 1) % self.n_qubits
# get dimension of inner bond between i and i+1
r = self.right_edges(i)[0].dimension
gate = tn.Node(gate)
# connect i and i+1 to the gate
self.out_edge(i) ^ gate[0]
self.out_edge(j) ^ gate[1]
# Get non-dangling edges for each side
left_edges = self.left_edges(i) + [gate[2]]
right_edges = self.right_edges(j) + [gate[3]]
# Contract edges
C = (self.nodes[i] @ self.nodes[j])
D = C @ gate
self.nodes[i], self.nodes[j], _ = tn.split_node(
D,
left_edges=left_edges,
right_edges=right_edges,
max_singular_values=4 * r,
left_name=str(i),
right_name=str(j))
def computational_basis_state(state: int, dim: int = 2) -> tn.Node:
"""Returns a computational basis state.
Args:
state: Integer which labels the state from the set {0, 1, ..., d - 1}.
dim: Dimension of the qudit. Default is two for qubits.
Raises:
ValueError: If state < 0, dim < 0, or state >= dim.
"""
if state < 0:
raise ValueError(f"Argument state should be positive but is {state}.")
if dim < 0:
raise ValueError(f"Argument dim should be positive but is {dim}.")
if state >= dim:
raise ValueError(
f"Requires state < dim but state = {state} and dim = {dim}.")
vector = np.zeros((dim, ))
vector[state] = 1.
return tn.Node(vector, name=f"|{state}>")
def put_mps(tensors):
'''
returns
a set of tensor cores connected in MPS
a set of connected edges
'''
mps = []
for i in range(len(tensors)):
mps.append(
tn.Node(tensors[i].detach().clone().squeeze(), backend=backend))
if len(tensors) == 1:
return mps, []
connected_edges = []
conn = mps[0][-1] ^ mps[1][0]
if len(mps) > 2:
for k in range(1, len(tensors) - 1):
conn = mps[k][-1] ^ mps[k + 1][0]
connected_edges.append(conn)
return mps, connected_edges
def test_split_node(dtype, num_charges):
np.random.seed(111)
a = tn.Node(get_zeros((2, 3, 4, 5, 6), num_charges, dtype),
backend='symmetric')
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right, _ = tn.split_node(a, left_edges, right_edges)
tn.check_correct({left, right})
actual = left @ right
np.testing.assert_allclose(actual.tensor.shape, (2, 3, 4, 5, 6))
np.testing.assert_allclose(a.tensor.shape, (2, 3, 4, 5, 6))
np.testing.assert_allclose(left.tensor.data, 0)
np.testing.assert_allclose(right.tensor.data, 0)
assert np.all([
charge_equal(a.tensor._charges[n], actual.tensor._charges[n])
for n in range(len(a.tensor._charges))
])
def getPurity(l):
with open('results/toricBoundaries', 'rb') as f:
[upRow, downRow, leftRow, rightRow, openA, openB] = pickle.load(f)
upRow = tn.Node(upRow)
downRow = tn.Node(downRow)
leftRow = tn.Node(leftRow)
rightRow = tn.Node(rightRow)
openA = tn.Node(openA)
openB = tn.Node(openB)
[cUp, dUp, te] = bops.svdTruncation(upRow, [0, 1], [2, 3], '>>')
[cDown, dDown, te] = bops.svdTruncation(downRow, [0, 1], [2, 3], '>>')
norm = applyLocalOperators(cUp, dUp, cDown, dDown, leftRow, rightRow, A, B,
l, [tn.Node(np.eye(d)) for i in range(l * 4)])
leftRow = bops.multNode(leftRow, 1 / norm)
res = applyLocalOperators(cUp, dUp, cDown, dDown, leftRow, rightRow, A, B,
l,
[tn.Node(ru.proj0Tensor) for i in range(l * 4)])
# The density matrix is constructed of blocks of ones of size N and normalized by res.
# Squaring it adds a factor of N * res.
N = 2**l
purity = N * res
return purity
def apply_circuit(circ, qubits=None, init_state=None):
if 'tn' not in globals():
raise ImportError('Please install tensornetwork first.')
all_nodes, left_edges, right_edges, N = circuit_network(circ)
if qubits is not None:
assert len(qubits) == N
else:
qubits = list(range(N))
if init_state is None:
init_state_nodes = [
tn.Node(np.array([1, 0], dtype=complex)) for i in range(N)
]
init_state_edges = [init_state_nodes[i][0] for i in range(N)]
else:
init_state_nodes, init_state_edges = init_state
all_nodes.extend(init_state_nodes)
qubit_edges = [left_edges[q] for q in qubits]
for i, q in enumerate(qubits):
tn.connect(right_edges[i], init_state_edges[q])
result = tn.contractors.optimal(all_nodes, output_edge_order=qubit_edges)
return result.tensor.reshape(-1)
def rgate(seed: Optional[int] = None, angle_scale: float = 1.0):
"""Returns the random single qubit gate described in
https://arxiv.org/abs/2002.07730.
Args:
seed: Seed for random number generator.
angle_scale: Floating point value to scale angles by. Default 1.
"""
if seed:
np.random.seed(seed)
# Get the random parameters
theta, alpha, phi = np.random.rand(3) * 2 * np.pi
mx = np.sin(alpha) * np.cos(phi)
my = np.sin(alpha) * np.sin(phi)
mz = np.cos(alpha)
theta *= angle_scale
# Get the unitary
unitary = expm(-1j * theta *
(mx * _xmatrix + my * _ymatrix * mz * _zmatrix))
return tn.Node(unitary)
def computational_basis_projector(state: int, dim: int = 2) -> tn.Node:
"""Returns a projector onto a computational basis state which acts on a
single qudit of dimension dim.
Args:
state: Basis state to project onto.
dim: Dimension of the qudit. Default is two for qubits.
Raises:
ValueError: If state < 0, dim < 0, or state >= dim.
"""
if state < 0:
raise ValueError(f"Argument state should be positive but is {state}.")
if dim < 0:
raise ValueError(f"Argument dim should be positive but is {dim}.")
if state >= dim:
raise ValueError(
f"Requires state < dim but state = {state} and dim = {dim}.")
projector = np.zeros((dim, dim))
projector[state, state] = 1.
return tn.Node(projector, name=f"|{state}><{state}|")
def translate(self, alpha: float, max_singular_values: int, num_threads: int) -> bool:
B = self.Xs.shape[1]
num_per_thd = int(B / num_threads)
bond = self.mps.get_contracted_bond()
bond_shape = bond.shape
leftmost = self.__leftmost__()
if leftmost:
grads = np.zeros((num_threads, bond_shape[0], bond_shape[1], bond_shape[2], bond_shape[3]))
else:
grads = np.zeros((num_threads, bond_shape[0], bond_shape[1], bond_shape[2], bond_shape[3], bond_shape[4]))
iter_barrier = threading.Barrier(num_threads + 1)
def thread_main(ithread):
grad = np.zeros(bond_shape)
for i in np.arange(ithread * num_per_thd, (ithread + 1) * num_per_thd):
grad = grad + self.__gradient__(i)
grads[ithread] = grad
iter_barrier.wait()
worker_threads = [threading.Thread(target=thread_main, args=(it,)) for it in range(num_threads)]
for t in worker_threads:
t.start()
iter_barrier.wait()
for t in worker_threads:
t.join()
# Here, all gradients have been calculated
total_grad = np.sum(grads, axis=0)
new_bond = tn.Node(bond.tensor - alpha * total_grad)
self.__add_newbond_names__(new_bond)
self.mps.update_bond(new_bond, max_singular_values)
for i in range(B):
self.__precompute_projections__(i)
return self.mps.output_idx < len(self.mps.tensors) - 2
def haar_random_unitary(nqudits: int = 2,
qudit_dimension: int = 2,
name: str = "Haar",
seed: Optional[int] = None) -> tn.Node:
"""Returns a Haar random unitary matrix of dimension 2**nqubits using
the algorithm in https://arxiv.org/abs/math-ph/0609050.
Args:
nqudits: Number of qudits the unitary acts on.
qudit_dimension: Dimension of each qudit.
name: Name for the gate.
seed: Seed for random number generator.
Notes:
See also
http://qutip.org/docs/4.3/apidoc/functions.html#qutip.random_objects
which was used as a template for this function.
"""
# Seed for random number generator
rng = np.random.RandomState(seed)
# Generate an N x N matrix of complex standard normal random variables
units = np.array([1, 1j])
shape = (qudit_dimension**nqudits, qudit_dimension**nqudits)
mat = np.sum(rng.randn(*(shape + (2, ))) * units, axis=-1) / np.sqrt(2)
# Do the QR decomposition
qmat, rmat = np.linalg.qr(mat)
# Create a diagonal matrix by rescaling the diagonal elements of rmat
diag = np.diag(rmat).copy()
diag /= np.abs(diag)
# Reshape the tensor
tensor = qmat * diag
tensor = np.reshape(tensor, newshape=[qudit_dimension] * 2 * nqudits)
return tn.Node(tensor, name=name)
def randomMeasurement(psi, startInd, endInd):
d = psi[0].tensor.shape[1]
res = [-1] * (endInd - startInd)
psiCopy = bops.copyState(psi)
for k in [len(psiCopy) - 1 - i for i in range(len(psiCopy) - startInd - 1)]:
psiCopy = bops.shiftWorkingSite(psiCopy, k, '<<')
for i in range(startInd, endInd):
rho = bops.multiContraction(psiCopy[i], psiCopy[i], '02', '02*')
measurement = np.random.uniform(low=0, high=1)
covered = 0
for s in range(len(rho.tensor)):
if covered < measurement < covered + rho.tensor[s, s]:
res[i - startInd] = s
break
covered += rho.tensor[s, s]
projectorTensor = np.zeros((d, d), dtype=complex)
projectorTensor[res[i - startInd], res[i - startInd]] = 1
projector = tn.Node(projectorTensor, backend=None)
bops.applySingleSiteOp(psiCopy, projector, i)
psiCopy = bops.shiftWorkingSite(psiCopy, i, '>>')
psiCopy[i + 1].tensor /= np.sqrt(bops.getOverlap(psiCopy, psiCopy))
tn.remove_node(rho)
bops.removeState(psiCopy)
return res
def test_real_physics(dtype, num_charges):
# Calcuate the expected value in numpy
t1 = get_random_symmetric((20, 20, 20, 20), [False, False, False, False],
num_charges,
dtype=dtype)
t2 = get_random_symmetric((20, 20, 20), [True, False, True],
num_charges,
dtype=dtype)
t3 = get_random_symmetric((20, 20, 20), [True, True, False],
num_charges,
dtype=dtype)
t1_dense = t1.todense()
t2_dense = t2.todense()
t3_dense = t3.todense()
adense = tn.Node(t1_dense, name="T", backend='numpy')
bdense = tn.Node(t2_dense, name="A", backend='numpy')
cdense = tn.Node(t3_dense, name="B", backend='numpy')
e1 = tn.connect(adense[2], bdense[0], "edge")
e2 = tn.connect(cdense[0], adense[3], "edge2")
e3 = tn.connect(bdense[1], cdense[1], "edge3")
node_result = tn.contract(e1)
node_result = tn.contract(e2)
final_result = tn.contract(e3)
# Build the network
a = tn.Node(t1, name="T", backend='symmetric')
b = tn.Node(t2, name="A", backend='symmetric')
c = tn.Node(t3, name="B", backend='symmetric')
e1 = tn.connect(a[2], b[0], "edge")
e2 = tn.connect(c[0], a[3], "edge2")
e3 = tn.connect(b[1], c[1], "edge3")
tn.check_correct(tn.reachable(a))
node_result = tn.contract(e1)
tn.check_correct(tn.reachable(node_result))
node_result = tn.contract(e2)
tn.check_correct(tn.reachable(node_result))
val = tn.contract(e3)
tn.check_correct(tn.reachable(val))
np.testing.assert_allclose(val.tensor.todense(), final_result.tensor)
def test_split_node_qr_orig_shape(backend):
n1 = tn.Node(np.random.rand(3, 4, 5), backend=backend)
tn.split_node_qr(n1, [n1[0], n1[2]], [n1[1]])
np.testing.assert_allclose(n1.shape, (3, 4, 5))
def test_switch_backend_raises_error(backend):
a = tn.Node(np.random.rand(3, 3, 3))
a.backend = BaseBackend()
with pytest.raises(NotImplementedError):
tn.switch_backend({a}, backend)
