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248,600 | jrief/django-angular | djng/views/crud.py | NgCRUDView.ng_delete | def ng_delete(self, request, *args, **kwargs):
"""
Delete object and return it's data in JSON encoding
The response is build before the object is actually deleted
so that we can still retrieve a serialization in the response
even with a m2m relationship
"""
if 'pk' not in request.GET:
raise NgMissingParameterError("Object id is required to delete.")
obj = self.get_object()
response = self.build_json_response(obj)
obj.delete()
return response | python | def ng_delete(self, request, *args, **kwargs):
if 'pk' not in request.GET:
raise NgMissingParameterError("Object id is required to delete.")
obj = self.get_object()
response = self.build_json_response(obj)
obj.delete()
return response | [
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248,601 | jrief/django-angular | djng/forms/angular_model.py | NgModelFormMixin._post_clean | def _post_clean(self):
"""
Rewrite the error dictionary, so that its keys correspond to the model fields.
"""
super(NgModelFormMixin, self)._post_clean()
if self._errors and self.prefix:
self._errors = ErrorDict((self.add_prefix(name), value) for name, value in self._errors.items()) | python | def _post_clean(self):
super(NgModelFormMixin, self)._post_clean()
if self._errors and self.prefix:
self._errors = ErrorDict((self.add_prefix(name), value) for name, value in self._errors.items()) | [
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248,602 | WoLpH/python-progressbar | progressbar/bar.py | ProgressBar.percentage | def percentage(self):
'''Return current percentage, returns None if no max_value is given
>>> progress = ProgressBar()
>>> progress.max_value = 10
>>> progress.min_value = 0
>>> progress.value = 0
>>> progress.percentage
0.0
>>>
>>> progress.value = 1
>>> progress.percentage
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>>> progress.value = 10
>>> progress.percentage
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>>> progress.min_value = -10
>>> progress.percentage
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>>> progress.value = 0
>>> progress.percentage
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>>> progress.value = 5
>>> progress.percentage
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>>> progress.value = -5
>>> progress.percentage
25.0
>>> progress.max_value = None
>>> progress.percentage
'''
if self.max_value is None or self.max_value is base.UnknownLength:
return None
elif self.max_value:
todo = self.value - self.min_value
total = self.max_value - self.min_value
percentage = todo / total
else:
percentage = 1
return percentage * 100 | python | def percentage(self):
'''Return current percentage, returns None if no max_value is given
>>> progress = ProgressBar()
>>> progress.max_value = 10
>>> progress.min_value = 0
>>> progress.value = 0
>>> progress.percentage
0.0
>>>
>>> progress.value = 1
>>> progress.percentage
10.0
>>> progress.value = 10
>>> progress.percentage
100.0
>>> progress.min_value = -10
>>> progress.percentage
100.0
>>> progress.value = 0
>>> progress.percentage
50.0
>>> progress.value = 5
>>> progress.percentage
75.0
>>> progress.value = -5
>>> progress.percentage
25.0
>>> progress.max_value = None
>>> progress.percentage
'''
if self.max_value is None or self.max_value is base.UnknownLength:
return None
elif self.max_value:
todo = self.value - self.min_value
total = self.max_value - self.min_value
percentage = todo / total
else:
percentage = 1
return percentage * 100 | [
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248,603 | WoLpH/python-progressbar | examples.py | example | def example(fn):
'''Wrap the examples so they generate readable output'''
@functools.wraps(fn)
def wrapped():
try:
sys.stdout.write('Running: %s\n' % fn.__name__)
fn()
sys.stdout.write('\n')
except KeyboardInterrupt:
sys.stdout.write('\nSkipping example.\n\n')
# Sleep a bit to make killing the script easier
time.sleep(0.2)
examples.append(wrapped)
return wrapped | python | def example(fn):
'''Wrap the examples so they generate readable output'''
@functools.wraps(fn)
def wrapped():
try:
sys.stdout.write('Running: %s\n' % fn.__name__)
fn()
sys.stdout.write('\n')
except KeyboardInterrupt:
sys.stdout.write('\nSkipping example.\n\n')
# Sleep a bit to make killing the script easier
time.sleep(0.2)
examples.append(wrapped)
return wrapped | [
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248,604 | rigetti/quantumflow | quantumflow/datasets/__init__.py | load_stdgraphs | def load_stdgraphs(size: int) -> List[nx.Graph]:
"""Load standard graph validation sets
For each size (from 6 to 32 graph nodes) the dataset consists of
100 graphs drawn from the Erdős-Rényi ensemble with edge
probability 50%.
"""
from pkg_resources import resource_stream
if size < 6 or size > 32:
raise ValueError('Size out of range.')
filename = 'datasets/data/graph{}er100.g6'.format(size)
fdata = resource_stream('quantumflow', filename)
return nx.read_graph6(fdata) | python | def load_stdgraphs(size: int) -> List[nx.Graph]:
from pkg_resources import resource_stream
if size < 6 or size > 32:
raise ValueError('Size out of range.')
filename = 'datasets/data/graph{}er100.g6'.format(size)
fdata = resource_stream('quantumflow', filename)
return nx.read_graph6(fdata) | [
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248,605 | rigetti/quantumflow | quantumflow/datasets/__init__.py | load_mnist | def load_mnist(size: int = None,
border: int = _MNIST_BORDER,
blank_corners: bool = False,
nums: List[int] = None) \
-> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Download and rescale the MNIST database of handwritten digits
MNIST is a dataset of 60,000 28x28 grayscale images handwritten digits,
along with a test set of 10,000 images. We use Keras to download and
access the dataset. The first invocation of this method may take a while
as the dataset has to be downloaded and cached.
If size is None, then we return the original MNIST data.
For rescaled MNIST, we chop off the border, downsample to the
desired size with Lanczos resampling, and then (optionally) zero out the
corner pixels.
Returns (x_train, y_train, x_test, y_test)
x_train ndarray of shape (60000, size, size)
y_train ndarray of shape (60000,)
x_test ndarray of shape (10000, size, size)
y_test ndarray of shape (10000,)
"""
# DOCME: Fix up formatting above,
# DOCME: Explain nums argument
# JIT import since keras startup is slow
from keras.datasets import mnist
def _filter_mnist(x: np.ndarray, y: np.ndarray, nums: List[int] = None) \
-> Tuple[np.ndarray, np.ndarray]:
xt = []
yt = []
items = len(y)
for n in range(items):
if nums is not None and y[n] in nums:
xt.append(x[n])
yt.append(y[n])
xt = np.stack(xt)
yt = np.stack(yt)
return xt, yt
def _rescale(imgarray: np.ndarray, size: int) -> np.ndarray:
N = imgarray.shape[0]
# Chop off border
imgarray = imgarray[:, border:-border, border:-border]
rescaled = np.zeros(shape=(N, size, size), dtype=np.float)
for n in range(0, N):
img = Image.fromarray(imgarray[n])
img = img.resize((size, size), Image.LANCZOS)
rsc = np.asarray(img).reshape((size, size))
rsc = 256.*rsc/rsc.max()
rescaled[n] = rsc
return rescaled.astype(dtype=np.uint8)
def _blank_corners(imgarray: np.ndarray) -> None:
# Zero out corners
sz = imgarray.shape[1]
corner = (sz//2)-1
for x in range(0, corner):
for y in range(0, corner-x):
imgarray[:, x, y] = 0
imgarray[:, -(1+x), y] = 0
imgarray[:, -(1+x), -(1+y)] = 0
imgarray[:, x, -(1+y)] = 0
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if nums:
x_train, y_train = _filter_mnist(x_train, y_train, nums)
x_test, y_test = _filter_mnist(x_test, y_test, nums)
if size:
x_train = _rescale(x_train, size)
x_test = _rescale(x_test, size)
if blank_corners:
_blank_corners(x_train)
_blank_corners(x_test)
return x_train, y_train, x_test, y_test | python | def load_mnist(size: int = None,
border: int = _MNIST_BORDER,
blank_corners: bool = False,
nums: List[int] = None) \
-> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
# DOCME: Fix up formatting above,
# DOCME: Explain nums argument
# JIT import since keras startup is slow
from keras.datasets import mnist
def _filter_mnist(x: np.ndarray, y: np.ndarray, nums: List[int] = None) \
-> Tuple[np.ndarray, np.ndarray]:
xt = []
yt = []
items = len(y)
for n in range(items):
if nums is not None and y[n] in nums:
xt.append(x[n])
yt.append(y[n])
xt = np.stack(xt)
yt = np.stack(yt)
return xt, yt
def _rescale(imgarray: np.ndarray, size: int) -> np.ndarray:
N = imgarray.shape[0]
# Chop off border
imgarray = imgarray[:, border:-border, border:-border]
rescaled = np.zeros(shape=(N, size, size), dtype=np.float)
for n in range(0, N):
img = Image.fromarray(imgarray[n])
img = img.resize((size, size), Image.LANCZOS)
rsc = np.asarray(img).reshape((size, size))
rsc = 256.*rsc/rsc.max()
rescaled[n] = rsc
return rescaled.astype(dtype=np.uint8)
def _blank_corners(imgarray: np.ndarray) -> None:
# Zero out corners
sz = imgarray.shape[1]
corner = (sz//2)-1
for x in range(0, corner):
for y in range(0, corner-x):
imgarray[:, x, y] = 0
imgarray[:, -(1+x), y] = 0
imgarray[:, -(1+x), -(1+y)] = 0
imgarray[:, x, -(1+y)] = 0
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if nums:
x_train, y_train = _filter_mnist(x_train, y_train, nums)
x_test, y_test = _filter_mnist(x_test, y_test, nums)
if size:
x_train = _rescale(x_train, size)
x_test = _rescale(x_test, size)
if blank_corners:
_blank_corners(x_train)
_blank_corners(x_test)
return x_train, y_train, x_test, y_test | [
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MNIST is a dataset of 60,000 28x28 grayscale images handwritten digits,
along with a test set of 10,000 images. We use Keras to download and
access the dataset. The first invocation of this method may take a while
as the dataset has to be downloaded and cached.
If size is None, then we return the original MNIST data.
For rescaled MNIST, we chop off the border, downsample to the
desired size with Lanczos resampling, and then (optionally) zero out the
corner pixels.
Returns (x_train, y_train, x_test, y_test)
x_train ndarray of shape (60000, size, size)
y_train ndarray of shape (60000,)
x_test ndarray of shape (10000, size, size)
y_test ndarray of shape (10000,) | [
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248,606 | rigetti/quantumflow | quantumflow/backend/tensorflow2bk.py | astensor | def astensor(array: TensorLike) -> BKTensor:
"""Covert numpy array to tensorflow tensor"""
tensor = tf.convert_to_tensor(value=array, dtype=CTYPE)
return tensor | python | def astensor(array: TensorLike) -> BKTensor:
tensor = tf.convert_to_tensor(value=array, dtype=CTYPE)
return tensor | [
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248,607 | rigetti/quantumflow | quantumflow/backend/tensorflow2bk.py | inner | def inner(tensor0: BKTensor, tensor1: BKTensor) -> BKTensor:
"""Return the inner product between two states"""
# Note: Relying on fact that vdot flattens arrays
N = rank(tensor0)
axes = list(range(N))
return tf.tensordot(tf.math.conj(tensor0), tensor1, axes=(axes, axes)) | python | def inner(tensor0: BKTensor, tensor1: BKTensor) -> BKTensor:
# Note: Relying on fact that vdot flattens arrays
N = rank(tensor0)
axes = list(range(N))
return tf.tensordot(tf.math.conj(tensor0), tensor1, axes=(axes, axes)) | [
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248,608 | rigetti/quantumflow | quantumflow/qaoa.py | graph_cuts | def graph_cuts(graph: nx.Graph) -> np.ndarray:
"""For the given graph, return the cut value for all binary assignments
of the graph.
"""
N = len(graph)
diag_hamiltonian = np.zeros(shape=([2]*N), dtype=np.double)
for q0, q1 in graph.edges():
for index, _ in np.ndenumerate(diag_hamiltonian):
if index[q0] != index[q1]:
weight = graph[q0][q1].get('weight', 1)
diag_hamiltonian[index] += weight
return diag_hamiltonian | python | def graph_cuts(graph: nx.Graph) -> np.ndarray:
N = len(graph)
diag_hamiltonian = np.zeros(shape=([2]*N), dtype=np.double)
for q0, q1 in graph.edges():
for index, _ in np.ndenumerate(diag_hamiltonian):
if index[q0] != index[q1]:
weight = graph[q0][q1].get('weight', 1)
diag_hamiltonian[index] += weight
return diag_hamiltonian | [
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248,609 | rigetti/quantumflow | quantumflow/dagcircuit.py | DAGCircuit.depth | def depth(self, local: bool = True) -> int:
"""Return the circuit depth.
Args:
local: If True include local one-qubit gates in depth
calculation. Else return the multi-qubit gate depth.
"""
G = self.graph
if not local:
def remove_local(dagc: DAGCircuit) \
-> Generator[Operation, None, None]:
for elem in dagc:
if dagc.graph.degree[elem] > 2:
yield elem
G = DAGCircuit(remove_local(self)).graph
return nx.dag_longest_path_length(G) - 1 | python | def depth(self, local: bool = True) -> int:
G = self.graph
if not local:
def remove_local(dagc: DAGCircuit) \
-> Generator[Operation, None, None]:
for elem in dagc:
if dagc.graph.degree[elem] > 2:
yield elem
G = DAGCircuit(remove_local(self)).graph
return nx.dag_longest_path_length(G) - 1 | [
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248,610 | rigetti/quantumflow | quantumflow/dagcircuit.py | DAGCircuit.components | def components(self) -> List['DAGCircuit']:
"""Split DAGCircuit into independent components"""
comps = nx.weakly_connected_component_subgraphs(self.graph)
return [DAGCircuit(comp) for comp in comps] | python | def components(self) -> List['DAGCircuit']:
comps = nx.weakly_connected_component_subgraphs(self.graph)
return [DAGCircuit(comp) for comp in comps] | [
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248,611 | rigetti/quantumflow | quantumflow/states.py | zero_state | def zero_state(qubits: Union[int, Qubits]) -> State:
"""Return the all-zero state on N qubits"""
N, qubits = qubits_count_tuple(qubits)
ket = np.zeros(shape=[2] * N)
ket[(0,) * N] = 1
return State(ket, qubits) | python | def zero_state(qubits: Union[int, Qubits]) -> State:
N, qubits = qubits_count_tuple(qubits)
ket = np.zeros(shape=[2] * N)
ket[(0,) * N] = 1
return State(ket, qubits) | [
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248,612 | rigetti/quantumflow | quantumflow/states.py | w_state | def w_state(qubits: Union[int, Qubits]) -> State:
"""Return a W state on N qubits"""
N, qubits = qubits_count_tuple(qubits)
ket = np.zeros(shape=[2] * N)
for n in range(N):
idx = np.zeros(shape=N, dtype=int)
idx[n] += 1
ket[tuple(idx)] = 1 / sqrt(N)
return State(ket, qubits) | python | def w_state(qubits: Union[int, Qubits]) -> State:
N, qubits = qubits_count_tuple(qubits)
ket = np.zeros(shape=[2] * N)
for n in range(N):
idx = np.zeros(shape=N, dtype=int)
idx[n] += 1
ket[tuple(idx)] = 1 / sqrt(N)
return State(ket, qubits) | [
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248,613 | rigetti/quantumflow | quantumflow/states.py | ghz_state | def ghz_state(qubits: Union[int, Qubits]) -> State:
"""Return a GHZ state on N qubits"""
N, qubits = qubits_count_tuple(qubits)
ket = np.zeros(shape=[2] * N)
ket[(0, ) * N] = 1 / sqrt(2)
ket[(1, ) * N] = 1 / sqrt(2)
return State(ket, qubits) | python | def ghz_state(qubits: Union[int, Qubits]) -> State:
N, qubits = qubits_count_tuple(qubits)
ket = np.zeros(shape=[2] * N)
ket[(0, ) * N] = 1 / sqrt(2)
ket[(1, ) * N] = 1 / sqrt(2)
return State(ket, qubits) | [
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248,614 | rigetti/quantumflow | quantumflow/states.py | random_state | def random_state(qubits: Union[int, Qubits]) -> State:
"""Return a random state from the space of N qubits"""
N, qubits = qubits_count_tuple(qubits)
ket = np.random.normal(size=([2] * N)) \
+ 1j * np.random.normal(size=([2] * N))
return State(ket, qubits).normalize() | python | def random_state(qubits: Union[int, Qubits]) -> State:
N, qubits = qubits_count_tuple(qubits)
ket = np.random.normal(size=([2] * N)) \
+ 1j * np.random.normal(size=([2] * N))
return State(ket, qubits).normalize() | [
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248,615 | rigetti/quantumflow | quantumflow/states.py | join_states | def join_states(*states: State) -> State:
"""Join two state vectors into a larger qubit state"""
vectors = [ket.vec for ket in states]
vec = reduce(outer_product, vectors)
return State(vec.tensor, vec.qubits) | python | def join_states(*states: State) -> State:
vectors = [ket.vec for ket in states]
vec = reduce(outer_product, vectors)
return State(vec.tensor, vec.qubits) | [
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248,616 | rigetti/quantumflow | quantumflow/states.py | print_state | def print_state(state: State, file: TextIO = None) -> None:
"""Print a state vector"""
state = state.vec.asarray()
for index, amplitude in np.ndenumerate(state):
ket = "".join([str(n) for n in index])
print(ket, ":", amplitude, file=file) | python | def print_state(state: State, file: TextIO = None) -> None:
state = state.vec.asarray()
for index, amplitude in np.ndenumerate(state):
ket = "".join([str(n) for n in index])
print(ket, ":", amplitude, file=file) | [
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248,617 | rigetti/quantumflow | quantumflow/states.py | print_probabilities | def print_probabilities(state: State, ndigits: int = 4,
file: TextIO = None) -> None:
"""
Pretty print state probabilities.
Args:
state:
ndigits: Number of digits of accuracy
file: Output stream (Defaults to stdout)
"""
prob = bk.evaluate(state.probabilities())
for index, prob in np.ndenumerate(prob):
prob = round(prob, ndigits)
if prob == 0.0:
continue
ket = "".join([str(n) for n in index])
print(ket, ":", prob, file=file) | python | def print_probabilities(state: State, ndigits: int = 4,
file: TextIO = None) -> None:
prob = bk.evaluate(state.probabilities())
for index, prob in np.ndenumerate(prob):
prob = round(prob, ndigits)
if prob == 0.0:
continue
ket = "".join([str(n) for n in index])
print(ket, ":", prob, file=file) | [
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248,618 | rigetti/quantumflow | quantumflow/states.py | mixed_density | def mixed_density(qubits: Union[int, Qubits]) -> Density:
"""Returns the completely mixed density matrix"""
N, qubits = qubits_count_tuple(qubits)
matrix = np.eye(2**N) / 2**N
return Density(matrix, qubits) | python | def mixed_density(qubits: Union[int, Qubits]) -> Density:
N, qubits = qubits_count_tuple(qubits)
matrix = np.eye(2**N) / 2**N
return Density(matrix, qubits) | [
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248,619 | rigetti/quantumflow | quantumflow/states.py | join_densities | def join_densities(*densities: Density) -> Density:
"""Join two mixed states into a larger qubit state"""
vectors = [rho.vec for rho in densities]
vec = reduce(outer_product, vectors)
memory = dict(ChainMap(*[rho.memory for rho in densities])) # TESTME
return Density(vec.tensor, vec.qubits, memory) | python | def join_densities(*densities: Density) -> Density:
vectors = [rho.vec for rho in densities]
vec = reduce(outer_product, vectors)
memory = dict(ChainMap(*[rho.memory for rho in densities])) # TESTME
return Density(vec.tensor, vec.qubits, memory) | [
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248,620 | rigetti/quantumflow | quantumflow/states.py | State.normalize | def normalize(self) -> 'State':
"""Normalize the state"""
tensor = self.tensor / bk.ccast(bk.sqrt(self.norm()))
return State(tensor, self.qubits, self._memory) | python | def normalize(self) -> 'State':
tensor = self.tensor / bk.ccast(bk.sqrt(self.norm()))
return State(tensor, self.qubits, self._memory) | [
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248,621 | rigetti/quantumflow | quantumflow/states.py | State.sample | def sample(self, trials: int) -> np.ndarray:
"""Measure the state in the computational basis the the given number
of trials, and return the counts of each output configuration.
"""
# TODO: Can we do this within backend?
probs = np.real(bk.evaluate(self.probabilities()))
res = np.random.multinomial(trials, probs.ravel())
res = res.reshape(probs.shape)
return res | python | def sample(self, trials: int) -> np.ndarray:
# TODO: Can we do this within backend?
probs = np.real(bk.evaluate(self.probabilities()))
res = np.random.multinomial(trials, probs.ravel())
res = res.reshape(probs.shape)
return res | [
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248,622 | rigetti/quantumflow | quantumflow/states.py | State.expectation | def expectation(self, diag_hermitian: bk.TensorLike,
trials: int = None) -> bk.BKTensor:
"""Return the expectation of a measurement. Since we can only measure
our computer in the computational basis, we only require the diagonal
of the Hermitian in that basis.
If the number of trials is specified, we sample the given number of
times. Else we return the exact expectation (as if we'd performed an
infinite number of trials. )
"""
if trials is None:
probs = self.probabilities()
else:
probs = bk.real(bk.astensorproduct(self.sample(trials) / trials))
diag_hermitian = bk.astensorproduct(diag_hermitian)
return bk.sum(bk.real(diag_hermitian) * probs) | python | def expectation(self, diag_hermitian: bk.TensorLike,
trials: int = None) -> bk.BKTensor:
if trials is None:
probs = self.probabilities()
else:
probs = bk.real(bk.astensorproduct(self.sample(trials) / trials))
diag_hermitian = bk.astensorproduct(diag_hermitian)
return bk.sum(bk.real(diag_hermitian) * probs) | [
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248,623 | rigetti/quantumflow | quantumflow/states.py | State.measure | def measure(self) -> np.ndarray:
"""Measure the state in the computational basis.
Returns:
A [2]*bits array of qubit states, either 0 or 1
"""
# TODO: Can we do this within backend?
probs = np.real(bk.evaluate(self.probabilities()))
indices = np.asarray(list(np.ndindex(*[2] * self.qubit_nb)))
res = np.random.choice(probs.size, p=probs.ravel())
res = indices[res]
return res | python | def measure(self) -> np.ndarray:
# TODO: Can we do this within backend?
probs = np.real(bk.evaluate(self.probabilities()))
indices = np.asarray(list(np.ndindex(*[2] * self.qubit_nb)))
res = np.random.choice(probs.size, p=probs.ravel())
res = indices[res]
return res | [
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248,624 | rigetti/quantumflow | quantumflow/states.py | State.asdensity | def asdensity(self) -> 'Density':
"""Convert a pure state to a density matrix"""
matrix = bk.outer(self.tensor, bk.conj(self.tensor))
return Density(matrix, self.qubits, self._memory) | python | def asdensity(self) -> 'Density':
matrix = bk.outer(self.tensor, bk.conj(self.tensor))
return Density(matrix, self.qubits, self._memory) | [
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248,625 | rigetti/quantumflow | tools/benchmark.py | benchmark | def benchmark(N, gates):
"""Create and run a circuit with N qubits and given number of gates"""
qubits = list(range(0, N))
ket = qf.zero_state(N)
for n in range(0, N):
ket = qf.H(n).run(ket)
for _ in range(0, (gates-N)//3):
qubit0, qubit1 = random.sample(qubits, 2)
ket = qf.X(qubit0).run(ket)
ket = qf.T(qubit1).run(ket)
ket = qf.CNOT(qubit0, qubit1).run(ket)
return ket.vec.tensor | python | def benchmark(N, gates):
qubits = list(range(0, N))
ket = qf.zero_state(N)
for n in range(0, N):
ket = qf.H(n).run(ket)
for _ in range(0, (gates-N)//3):
qubit0, qubit1 = random.sample(qubits, 2)
ket = qf.X(qubit0).run(ket)
ket = qf.T(qubit1).run(ket)
ket = qf.CNOT(qubit0, qubit1).run(ket)
return ket.vec.tensor | [
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248,626 | rigetti/quantumflow | examples/weyl.py | sandwich_decompositions | def sandwich_decompositions(coords0, coords1, samples=SAMPLES):
"""Create composite gates, decompose, and return a list
of canonical coordinates"""
decomps = []
for _ in range(samples):
circ = qf.Circuit()
circ += qf.CANONICAL(*coords0, 0, 1)
circ += qf.random_gate([0])
circ += qf.random_gate([1])
circ += qf.CANONICAL(*coords1, 0, 1)
gate = circ.asgate()
coords = qf.canonical_coords(gate)
decomps.append(coords)
return decomps | python | def sandwich_decompositions(coords0, coords1, samples=SAMPLES):
decomps = []
for _ in range(samples):
circ = qf.Circuit()
circ += qf.CANONICAL(*coords0, 0, 1)
circ += qf.random_gate([0])
circ += qf.random_gate([1])
circ += qf.CANONICAL(*coords1, 0, 1)
gate = circ.asgate()
coords = qf.canonical_coords(gate)
decomps.append(coords)
return decomps | [
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248,627 | rigetti/quantumflow | quantumflow/paulialgebra.py | sX | def sX(qubit: Qubit, coefficient: complex = 1.0) -> Pauli:
"""Return the Pauli sigma_X operator acting on the given qubit"""
return Pauli.sigma(qubit, 'X', coefficient) | python | def sX(qubit: Qubit, coefficient: complex = 1.0) -> Pauli:
return Pauli.sigma(qubit, 'X', coefficient) | [
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248,628 | rigetti/quantumflow | quantumflow/paulialgebra.py | sY | def sY(qubit: Qubit, coefficient: complex = 1.0) -> Pauli:
"""Return the Pauli sigma_Y operator acting on the given qubit"""
return Pauli.sigma(qubit, 'Y', coefficient) | python | def sY(qubit: Qubit, coefficient: complex = 1.0) -> Pauli:
return Pauli.sigma(qubit, 'Y', coefficient) | [
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248,629 | rigetti/quantumflow | quantumflow/paulialgebra.py | sZ | def sZ(qubit: Qubit, coefficient: complex = 1.0) -> Pauli:
"""Return the Pauli sigma_Z operator acting on the given qubit"""
return Pauli.sigma(qubit, 'Z', coefficient) | python | def sZ(qubit: Qubit, coefficient: complex = 1.0) -> Pauli:
return Pauli.sigma(qubit, 'Z', coefficient) | [
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248,630 | rigetti/quantumflow | quantumflow/paulialgebra.py | pauli_sum | def pauli_sum(*elements: Pauli) -> Pauli:
"""Return the sum of elements of the Pauli algebra"""
terms = []
key = itemgetter(0)
for term, grp in groupby(heapq.merge(*elements, key=key), key=key):
coeff = sum(g[1] for g in grp)
if not isclose(coeff, 0.0):
terms.append((term, coeff))
return Pauli(tuple(terms)) | python | def pauli_sum(*elements: Pauli) -> Pauli:
terms = []
key = itemgetter(0)
for term, grp in groupby(heapq.merge(*elements, key=key), key=key):
coeff = sum(g[1] for g in grp)
if not isclose(coeff, 0.0):
terms.append((term, coeff))
return Pauli(tuple(terms)) | [
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248,631 | rigetti/quantumflow | quantumflow/paulialgebra.py | pauli_product | def pauli_product(*elements: Pauli) -> Pauli:
"""Return the product of elements of the Pauli algebra"""
result_terms = []
for terms in product(*elements):
coeff = reduce(mul, [term[1] for term in terms])
ops = (term[0] for term in terms)
out = []
key = itemgetter(0)
for qubit, qops in groupby(heapq.merge(*ops, key=key), key=key):
res = next(qops)[1] # Operator: X Y Z
for op in qops:
pair = res + op[1]
res, rescoeff = PAULI_PROD[pair]
coeff *= rescoeff
if res != 'I':
out.append((qubit, res))
p = Pauli(((tuple(out), coeff),))
result_terms.append(p)
return pauli_sum(*result_terms) | python | def pauli_product(*elements: Pauli) -> Pauli:
result_terms = []
for terms in product(*elements):
coeff = reduce(mul, [term[1] for term in terms])
ops = (term[0] for term in terms)
out = []
key = itemgetter(0)
for qubit, qops in groupby(heapq.merge(*ops, key=key), key=key):
res = next(qops)[1] # Operator: X Y Z
for op in qops:
pair = res + op[1]
res, rescoeff = PAULI_PROD[pair]
coeff *= rescoeff
if res != 'I':
out.append((qubit, res))
p = Pauli(((tuple(out), coeff),))
result_terms.append(p)
return pauli_sum(*result_terms) | [
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248,632 | rigetti/quantumflow | quantumflow/paulialgebra.py | pauli_pow | def pauli_pow(pauli: Pauli, exponent: int) -> Pauli:
"""
Raise an element of the Pauli algebra to a non-negative integer power.
"""
if not isinstance(exponent, int) or exponent < 0:
raise ValueError("The exponent must be a non-negative integer.")
if exponent == 0:
return Pauli.identity()
if exponent == 1:
return pauli
# https://en.wikipedia.org/wiki/Exponentiation_by_squaring
y = Pauli.identity()
x = pauli
n = exponent
while n > 1:
if n % 2 == 0: # Even
x = x * x
n = n // 2
else: # Odd
y = x * y
x = x * x
n = (n - 1) // 2
return x * y | python | def pauli_pow(pauli: Pauli, exponent: int) -> Pauli:
if not isinstance(exponent, int) or exponent < 0:
raise ValueError("The exponent must be a non-negative integer.")
if exponent == 0:
return Pauli.identity()
if exponent == 1:
return pauli
# https://en.wikipedia.org/wiki/Exponentiation_by_squaring
y = Pauli.identity()
x = pauli
n = exponent
while n > 1:
if n % 2 == 0: # Even
x = x * x
n = n // 2
else: # Odd
y = x * y
x = x * x
n = (n - 1) // 2
return x * y | [
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248,633 | rigetti/quantumflow | quantumflow/paulialgebra.py | pauli_commuting_sets | def pauli_commuting_sets(element: Pauli) -> Tuple[Pauli, ...]:
"""Gather the terms of a Pauli polynomial into commuting sets.
Uses the algorithm defined in (Raeisi, Wiebe, Sanders,
arXiv:1108.4318, 2011) to find commuting sets. Except uses commutation
check from arXiv:1405.5749v2
"""
if len(element) < 2:
return (element,)
groups: List[Pauli] = [] # typing: List[Pauli]
for term in element:
pterm = Pauli((term,))
assigned = False
for i, grp in enumerate(groups):
if paulis_commute(grp, pterm):
groups[i] = grp + pterm
assigned = True
break
if not assigned:
groups.append(pterm)
return tuple(groups) | python | def pauli_commuting_sets(element: Pauli) -> Tuple[Pauli, ...]:
if len(element) < 2:
return (element,)
groups: List[Pauli] = [] # typing: List[Pauli]
for term in element:
pterm = Pauli((term,))
assigned = False
for i, grp in enumerate(groups):
if paulis_commute(grp, pterm):
groups[i] = grp + pterm
assigned = True
break
if not assigned:
groups.append(pterm)
return tuple(groups) | [
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248,634 | rigetti/quantumflow | quantumflow/backend/numpybk.py | astensor | def astensor(array: TensorLike) -> BKTensor:
"""Converts a numpy array to the backend's tensor object
"""
array = np.asarray(array, dtype=CTYPE)
return array | python | def astensor(array: TensorLike) -> BKTensor:
array = np.asarray(array, dtype=CTYPE)
return array | [
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248,635 | rigetti/quantumflow | quantumflow/backend/numpybk.py | productdiag | def productdiag(tensor: BKTensor) -> BKTensor:
"""Returns the matrix diagonal of the product tensor""" # DOCME: Explain
N = rank(tensor)
tensor = reshape(tensor, [2**(N//2), 2**(N//2)])
tensor = np.diag(tensor)
tensor = reshape(tensor, [2]*(N//2))
return tensor | python | def productdiag(tensor: BKTensor) -> BKTensor:
# DOCME: Explain
N = rank(tensor)
tensor = reshape(tensor, [2**(N//2), 2**(N//2)])
tensor = np.diag(tensor)
tensor = reshape(tensor, [2]*(N//2))
return tensor | [
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248,636 | rigetti/quantumflow | quantumflow/backend/numpybk.py | tensormul | def tensormul(tensor0: BKTensor, tensor1: BKTensor,
indices: typing.List[int]) -> BKTensor:
r"""
Generalization of matrix multiplication to product tensors.
A state vector in product tensor representation has N dimension, one for
each contravariant index, e.g. for 3-qubit states
:math:`B^{b_0,b_1,b_2}`. An operator has K dimensions, K/2 for
contravariant indices (e.g. ket components) and K/2 for covariant (bra)
indices, e.g. :math:`A^{a_0,a_1}_{a_2,a_3}` for a 2-qubit gate. The given
indices of A are contracted against B, replacing the given positions.
E.g. ``tensormul(A, B, [0,2])`` is equivalent to
.. math::
C^{a_0,b_1,a_1} =\sum_{i_0,i_1} A^{a_0,a_1}_{i_0,i_1} B^{i_0,b_1,i_1}
Args:
tensor0: A tensor product representation of a gate
tensor1: A tensor product representation of a gate or state
indices: List of indices of tensor1 on which to act.
Returns:
Resultant state or gate tensor
"""
# Note: This method is the critical computational core of QuantumFlow
# We currently have two implementations, one that uses einsum, the other
# using matrix multiplication
#
# numpy:
# einsum is much faster particularly for small numbers of qubits
# tensorflow:
# Little different is performance, but einsum would restrict the
# maximum number of qubits to 26 (Because tensorflow only allows 26
# einsum subscripts at present]
# torch:
# einsum is slower than matmul
N = rank(tensor1)
K = rank(tensor0) // 2
assert K == len(indices)
out = list(EINSUM_SUBSCRIPTS[0:N])
left_in = list(EINSUM_SUBSCRIPTS[N:N+K])
left_out = [out[idx] for idx in indices]
right = list(EINSUM_SUBSCRIPTS[0:N])
for idx, s in zip(indices, left_in):
right[idx] = s
subscripts = ''.join(left_out + left_in + [','] + right + ['->'] + out)
# print('>>>', K, N, subscripts)
tensor = einsum(subscripts, tensor0, tensor1)
return tensor | python | def tensormul(tensor0: BKTensor, tensor1: BKTensor,
indices: typing.List[int]) -> BKTensor:
r"""
Generalization of matrix multiplication to product tensors.
A state vector in product tensor representation has N dimension, one for
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indices of A are contracted against B, replacing the given positions.
E.g. ``tensormul(A, B, [0,2])`` is equivalent to
.. math::
C^{a_0,b_1,a_1} =\sum_{i_0,i_1} A^{a_0,a_1}_{i_0,i_1} B^{i_0,b_1,i_1}
Args:
tensor0: A tensor product representation of a gate
tensor1: A tensor product representation of a gate or state
indices: List of indices of tensor1 on which to act.
Returns:
Resultant state or gate tensor
"""
# Note: This method is the critical computational core of QuantumFlow
# We currently have two implementations, one that uses einsum, the other
# using matrix multiplication
#
# numpy:
# einsum is much faster particularly for small numbers of qubits
# tensorflow:
# Little different is performance, but einsum would restrict the
# maximum number of qubits to 26 (Because tensorflow only allows 26
# einsum subscripts at present]
# torch:
# einsum is slower than matmul
N = rank(tensor1)
K = rank(tensor0) // 2
assert K == len(indices)
out = list(EINSUM_SUBSCRIPTS[0:N])
left_in = list(EINSUM_SUBSCRIPTS[N:N+K])
left_out = [out[idx] for idx in indices]
right = list(EINSUM_SUBSCRIPTS[0:N])
for idx, s in zip(indices, left_in):
right[idx] = s
subscripts = ''.join(left_out + left_in + [','] + right + ['->'] + out)
# print('>>>', K, N, subscripts)
tensor = einsum(subscripts, tensor0, tensor1)
return tensor | [
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Generalization of matrix multiplication to product tensors.
A state vector in product tensor representation has N dimension, one for
each contravariant index, e.g. for 3-qubit states
:math:`B^{b_0,b_1,b_2}`. An operator has K dimensions, K/2 for
contravariant indices (e.g. ket components) and K/2 for covariant (bra)
indices, e.g. :math:`A^{a_0,a_1}_{a_2,a_3}` for a 2-qubit gate. The given
indices of A are contracted against B, replacing the given positions.
E.g. ``tensormul(A, B, [0,2])`` is equivalent to
.. math::
C^{a_0,b_1,a_1} =\sum_{i_0,i_1} A^{a_0,a_1}_{i_0,i_1} B^{i_0,b_1,i_1}
Args:
tensor0: A tensor product representation of a gate
tensor1: A tensor product representation of a gate or state
indices: List of indices of tensor1 on which to act.
Returns:
Resultant state or gate tensor | [
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248,637 | rigetti/quantumflow | quantumflow/utils.py | invert_map | def invert_map(mapping: dict, one_to_one: bool = True) -> dict:
"""Invert a dictionary. If not one_to_one then the inverted
map will contain lists of former keys as values.
"""
if one_to_one:
inv_map = {value: key for key, value in mapping.items()}
else:
inv_map = {}
for key, value in mapping.items():
inv_map.setdefault(value, set()).add(key)
return inv_map | python | def invert_map(mapping: dict, one_to_one: bool = True) -> dict:
if one_to_one:
inv_map = {value: key for key, value in mapping.items()}
else:
inv_map = {}
for key, value in mapping.items():
inv_map.setdefault(value, set()).add(key)
return inv_map | [
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248,638 | rigetti/quantumflow | quantumflow/utils.py | bitlist_to_int | def bitlist_to_int(bitlist: Sequence[int]) -> int:
"""Converts a sequence of bits to an integer.
>>> from quantumflow.utils import bitlist_to_int
>>> bitlist_to_int([1, 0, 0])
4
"""
return int(''.join([str(d) for d in bitlist]), 2) | python | def bitlist_to_int(bitlist: Sequence[int]) -> int:
return int(''.join([str(d) for d in bitlist]), 2) | [
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248,639 | rigetti/quantumflow | quantumflow/utils.py | int_to_bitlist | def int_to_bitlist(x: int, pad: int = None) -> Sequence[int]:
"""Converts an integer to a binary sequence of bits.
Pad prepends with sufficient zeros to ensures that the returned list
contains at least this number of bits.
>>> from quantumflow.utils import int_to_bitlist
>>> int_to_bitlist(4, 4))
[0, 1, 0, 0]
"""
if pad is None:
form = '{:0b}'
else:
form = '{:0' + str(pad) + 'b}'
return [int(b) for b in form.format(x)] | python | def int_to_bitlist(x: int, pad: int = None) -> Sequence[int]:
if pad is None:
form = '{:0b}'
else:
form = '{:0' + str(pad) + 'b}'
return [int(b) for b in form.format(x)] | [
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248,640 | rigetti/quantumflow | quantumflow/utils.py | spanning_tree_count | def spanning_tree_count(graph: nx.Graph) -> int:
"""Return the number of unique spanning trees of a graph, using
Kirchhoff's matrix tree theorem.
"""
laplacian = nx.laplacian_matrix(graph).toarray()
comatrix = laplacian[:-1, :-1]
det = np.linalg.det(comatrix)
count = int(round(det))
return count | python | def spanning_tree_count(graph: nx.Graph) -> int:
laplacian = nx.laplacian_matrix(graph).toarray()
comatrix = laplacian[:-1, :-1]
det = np.linalg.det(comatrix)
count = int(round(det))
return count | [
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248,641 | rigetti/quantumflow | quantumflow/utils.py | rationalize | def rationalize(flt: float, denominators: Set[int] = None) -> Fraction:
"""Convert a floating point number to a Fraction with a small
denominator.
Args:
flt: A floating point number
denominators: Collection of standard denominators. Default is
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 16, 32, 64, 128, 256, 512,
1024, 2048, 4096, 8192
Raises:
ValueError: If cannot rationalize float
"""
if denominators is None:
denominators = _DENOMINATORS
frac = Fraction.from_float(flt).limit_denominator()
if frac.denominator not in denominators:
raise ValueError('Cannot rationalize')
return frac | python | def rationalize(flt: float, denominators: Set[int] = None) -> Fraction:
if denominators is None:
denominators = _DENOMINATORS
frac = Fraction.from_float(flt).limit_denominator()
if frac.denominator not in denominators:
raise ValueError('Cannot rationalize')
return frac | [
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248,642 | rigetti/quantumflow | quantumflow/utils.py | symbolize | def symbolize(flt: float) -> sympy.Symbol:
"""Attempt to convert a real number into a simpler symbolic
representation.
Returns:
A sympy Symbol. (Convert to string with str(sym) or to latex with
sympy.latex(sym)
Raises:
ValueError: If cannot simplify float
"""
try:
ratio = rationalize(flt)
res = sympy.simplify(ratio)
except ValueError:
ratio = rationalize(flt/np.pi)
res = sympy.simplify(ratio) * sympy.pi
return res | python | def symbolize(flt: float) -> sympy.Symbol:
try:
ratio = rationalize(flt)
res = sympy.simplify(ratio)
except ValueError:
ratio = rationalize(flt/np.pi)
res = sympy.simplify(ratio) * sympy.pi
return res | [
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248,643 | rigetti/quantumflow | quantumflow/forest/__init__.py | pyquil_to_image | def pyquil_to_image(program: pyquil.Program) -> PIL.Image: # pragma: no cover
"""Returns an image of a pyquil circuit.
See circuit_to_latex() for more details.
"""
circ = pyquil_to_circuit(program)
latex = circuit_to_latex(circ)
img = render_latex(latex)
return img | python | def pyquil_to_image(program: pyquil.Program) -> PIL.Image: # pragma: no cover
circ = pyquil_to_circuit(program)
latex = circuit_to_latex(circ)
img = render_latex(latex)
return img | [
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248,644 | rigetti/quantumflow | quantumflow/forest/__init__.py | circuit_to_pyquil | def circuit_to_pyquil(circuit: Circuit) -> pyquil.Program:
"""Convert a QuantumFlow circuit to a pyQuil program"""
prog = pyquil.Program()
for elem in circuit.elements:
if isinstance(elem, Gate) and elem.name in QUIL_GATES:
params = list(elem.params.values()) if elem.params else []
prog.gate(elem.name, params, elem.qubits)
elif isinstance(elem, Measure):
prog.measure(elem.qubit, elem.cbit)
else:
# FIXME: more informative error message
raise ValueError('Cannot convert operation to pyquil')
return prog | python | def circuit_to_pyquil(circuit: Circuit) -> pyquil.Program:
prog = pyquil.Program()
for elem in circuit.elements:
if isinstance(elem, Gate) and elem.name in QUIL_GATES:
params = list(elem.params.values()) if elem.params else []
prog.gate(elem.name, params, elem.qubits)
elif isinstance(elem, Measure):
prog.measure(elem.qubit, elem.cbit)
else:
# FIXME: more informative error message
raise ValueError('Cannot convert operation to pyquil')
return prog | [
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248,645 | rigetti/quantumflow | quantumflow/forest/__init__.py | pyquil_to_circuit | def pyquil_to_circuit(program: pyquil.Program) -> Circuit:
"""Convert a protoquil pyQuil program to a QuantumFlow Circuit"""
circ = Circuit()
for inst in program.instructions:
# print(type(inst))
if isinstance(inst, pyquil.Declare): # Ignore
continue
if isinstance(inst, pyquil.Halt): # Ignore
continue
if isinstance(inst, pyquil.Pragma): # TODO Barrier?
continue
elif isinstance(inst, pyquil.Measurement):
circ += Measure(inst.qubit.index)
# elif isinstance(inst, pyquil.ResetQubit): # TODO
# continue
elif isinstance(inst, pyquil.Gate):
defgate = STDGATES[inst.name]
gate = defgate(*inst.params)
qubits = [q.index for q in inst.qubits]
gate = gate.relabel(qubits)
circ += gate
else:
raise ValueError('PyQuil program is not protoquil')
return circ | python | def pyquil_to_circuit(program: pyquil.Program) -> Circuit:
circ = Circuit()
for inst in program.instructions:
# print(type(inst))
if isinstance(inst, pyquil.Declare): # Ignore
continue
if isinstance(inst, pyquil.Halt): # Ignore
continue
if isinstance(inst, pyquil.Pragma): # TODO Barrier?
continue
elif isinstance(inst, pyquil.Measurement):
circ += Measure(inst.qubit.index)
# elif isinstance(inst, pyquil.ResetQubit): # TODO
# continue
elif isinstance(inst, pyquil.Gate):
defgate = STDGATES[inst.name]
gate = defgate(*inst.params)
qubits = [q.index for q in inst.qubits]
gate = gate.relabel(qubits)
circ += gate
else:
raise ValueError('PyQuil program is not protoquil')
return circ | [
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] | 13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb | https://github.com/rigetti/quantumflow/blob/13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb/quantumflow/forest/__init__.py#L188-L213 |
248,646 | rigetti/quantumflow | quantumflow/forest/__init__.py | quil_to_program | def quil_to_program(quil: str) -> Program:
"""Parse a quil program and return a Program object"""
pyquil_instructions = pyquil.parser.parse(quil)
return pyquil_to_program(pyquil_instructions) | python | def quil_to_program(quil: str) -> Program:
pyquil_instructions = pyquil.parser.parse(quil)
return pyquil_to_program(pyquil_instructions) | [
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248,647 | rigetti/quantumflow | quantumflow/forest/__init__.py | state_to_wavefunction | def state_to_wavefunction(state: State) -> pyquil.Wavefunction:
"""Convert a QuantumFlow state to a pyQuil Wavefunction"""
# TODO: qubits?
amplitudes = state.vec.asarray()
# pyQuil labels states backwards.
amplitudes = amplitudes.transpose()
amplitudes = amplitudes.reshape([amplitudes.size])
return pyquil.Wavefunction(amplitudes) | python | def state_to_wavefunction(state: State) -> pyquil.Wavefunction:
# TODO: qubits?
amplitudes = state.vec.asarray()
# pyQuil labels states backwards.
amplitudes = amplitudes.transpose()
amplitudes = amplitudes.reshape([amplitudes.size])
return pyquil.Wavefunction(amplitudes) | [
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248,648 | rigetti/quantumflow | quantumflow/forest/__init__.py | QuantumFlowQVM.load | def load(self, binary: pyquil.Program) -> 'QuantumFlowQVM':
"""
Load a pyQuil program, and initialize QVM into a fresh state.
Args:
binary: A pyQuil program
"""
assert self.status in ['connected', 'done']
prog = quil_to_program(str(binary))
self._prog = prog
self.program = binary
self.status = 'loaded'
return self | python | def load(self, binary: pyquil.Program) -> 'QuantumFlowQVM':
assert self.status in ['connected', 'done']
prog = quil_to_program(str(binary))
self._prog = prog
self.program = binary
self.status = 'loaded'
return self | [
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248,649 | rigetti/quantumflow | quantumflow/forest/__init__.py | QuantumFlowQVM.run | def run(self) -> 'QuantumFlowQVM':
"""Run a previously loaded program"""
assert self.status in ['loaded']
self.status = 'running'
self._ket = self._prog.run()
# Should set state to 'done' after run complete.
# Makes no sense to keep status at running. But pyQuil's
# QuantumComputer calls wait() after run, which expects state to be
# 'running', and whose only effect to is to set state to 'done'
return self | python | def run(self) -> 'QuantumFlowQVM':
assert self.status in ['loaded']
self.status = 'running'
self._ket = self._prog.run()
# Should set state to 'done' after run complete.
# Makes no sense to keep status at running. But pyQuil's
# QuantumComputer calls wait() after run, which expects state to be
# 'running', and whose only effect to is to set state to 'done'
return self | [
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248,650 | rigetti/quantumflow | quantumflow/forest/__init__.py | QuantumFlowQVM.wavefunction | def wavefunction(self) -> pyquil.Wavefunction:
"""
Return the wavefunction of a completed program.
"""
assert self.status == 'done'
assert self._ket is not None
wavefn = state_to_wavefunction(self._ket)
return wavefn | python | def wavefunction(self) -> pyquil.Wavefunction:
assert self.status == 'done'
assert self._ket is not None
wavefn = state_to_wavefunction(self._ket)
return wavefn | [
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248,651 | rigetti/quantumflow | quantumflow/backend/torchbk.py | evaluate | def evaluate(tensor: BKTensor) -> TensorLike:
"""Return the value of a tensor"""
if isinstance(tensor, _DTYPE):
if torch.numel(tensor) == 1:
return tensor.item()
if tensor.numel() == 2:
return tensor[0].cpu().numpy() + 1.0j * tensor[1].cpu().numpy()
return tensor[0].cpu().numpy() + 1.0j * tensor[1].cpu().numpy()
return tensor | python | def evaluate(tensor: BKTensor) -> TensorLike:
if isinstance(tensor, _DTYPE):
if torch.numel(tensor) == 1:
return tensor.item()
if tensor.numel() == 2:
return tensor[0].cpu().numpy() + 1.0j * tensor[1].cpu().numpy()
return tensor[0].cpu().numpy() + 1.0j * tensor[1].cpu().numpy()
return tensor | [
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248,652 | rigetti/quantumflow | quantumflow/backend/torchbk.py | rank | def rank(tensor: BKTensor) -> int:
"""Return the number of dimensions of a tensor"""
if isinstance(tensor, np.ndarray):
return len(tensor.shape)
return len(tensor[0].size()) | python | def rank(tensor: BKTensor) -> int:
if isinstance(tensor, np.ndarray):
return len(tensor.shape)
return len(tensor[0].size()) | [
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248,653 | rigetti/quantumflow | quantumflow/measures.py | state_fidelity | def state_fidelity(state0: State, state1: State) -> bk.BKTensor:
"""Return the quantum fidelity between pure states."""
assert state0.qubits == state1.qubits # FIXME
tensor = bk.absolute(bk.inner(state0.tensor, state1.tensor))**bk.fcast(2)
return tensor | python | def state_fidelity(state0: State, state1: State) -> bk.BKTensor:
assert state0.qubits == state1.qubits # FIXME
tensor = bk.absolute(bk.inner(state0.tensor, state1.tensor))**bk.fcast(2)
return tensor | [
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248,654 | rigetti/quantumflow | quantumflow/measures.py | state_angle | def state_angle(ket0: State, ket1: State) -> bk.BKTensor:
"""The Fubini-Study angle between states.
Equal to the Burrs angle for pure states.
"""
return fubini_study_angle(ket0.vec, ket1.vec) | python | def state_angle(ket0: State, ket1: State) -> bk.BKTensor:
return fubini_study_angle(ket0.vec, ket1.vec) | [
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248,655 | rigetti/quantumflow | quantumflow/measures.py | states_close | def states_close(state0: State, state1: State,
tolerance: float = TOLERANCE) -> bool:
"""Returns True if states are almost identical.
Closeness is measured with the metric Fubini-Study angle.
"""
return vectors_close(state0.vec, state1.vec, tolerance) | python | def states_close(state0: State, state1: State,
tolerance: float = TOLERANCE) -> bool:
return vectors_close(state0.vec, state1.vec, tolerance) | [
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248,656 | rigetti/quantumflow | quantumflow/measures.py | purity | def purity(rho: Density) -> bk.BKTensor:
"""
Calculate the purity of a mixed quantum state.
Purity, defined as tr(rho^2), has an upper bound of 1 for a pure state,
and a lower bound of 1/D (where D is the Hilbert space dimension) for a
competently mixed state.
Two closely related measures are the linear entropy, 1- purity, and the
participation ratio, 1/purity.
"""
tensor = rho.tensor
N = rho.qubit_nb
matrix = bk.reshape(tensor, [2**N, 2**N])
return bk.trace(bk.matmul(matrix, matrix)) | python | def purity(rho: Density) -> bk.BKTensor:
tensor = rho.tensor
N = rho.qubit_nb
matrix = bk.reshape(tensor, [2**N, 2**N])
return bk.trace(bk.matmul(matrix, matrix)) | [
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248,657 | rigetti/quantumflow | quantumflow/measures.py | bures_distance | def bures_distance(rho0: Density, rho1: Density) -> float:
"""Return the Bures distance between mixed quantum states
Note: Bures distance cannot be calculated within the tensor backend.
"""
fid = fidelity(rho0, rho1)
op0 = asarray(rho0.asoperator())
op1 = asarray(rho1.asoperator())
tr0 = np.trace(op0)
tr1 = np.trace(op1)
return np.sqrt(tr0 + tr1 - 2.*np.sqrt(fid)) | python | def bures_distance(rho0: Density, rho1: Density) -> float:
fid = fidelity(rho0, rho1)
op0 = asarray(rho0.asoperator())
op1 = asarray(rho1.asoperator())
tr0 = np.trace(op0)
tr1 = np.trace(op1)
return np.sqrt(tr0 + tr1 - 2.*np.sqrt(fid)) | [
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248,658 | rigetti/quantumflow | quantumflow/measures.py | bures_angle | def bures_angle(rho0: Density, rho1: Density) -> float:
"""Return the Bures angle between mixed quantum states
Note: Bures angle cannot be calculated within the tensor backend.
"""
return np.arccos(np.sqrt(fidelity(rho0, rho1))) | python | def bures_angle(rho0: Density, rho1: Density) -> float:
return np.arccos(np.sqrt(fidelity(rho0, rho1))) | [
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248,659 | rigetti/quantumflow | quantumflow/measures.py | density_angle | def density_angle(rho0: Density, rho1: Density) -> bk.BKTensor:
"""The Fubini-Study angle between density matrices"""
return fubini_study_angle(rho0.vec, rho1.vec) | python | def density_angle(rho0: Density, rho1: Density) -> bk.BKTensor:
return fubini_study_angle(rho0.vec, rho1.vec) | [
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248,660 | rigetti/quantumflow | quantumflow/measures.py | densities_close | def densities_close(rho0: Density, rho1: Density,
tolerance: float = TOLERANCE) -> bool:
"""Returns True if densities are almost identical.
Closeness is measured with the metric Fubini-Study angle.
"""
return vectors_close(rho0.vec, rho1.vec, tolerance) | python | def densities_close(rho0: Density, rho1: Density,
tolerance: float = TOLERANCE) -> bool:
return vectors_close(rho0.vec, rho1.vec, tolerance) | [
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248,661 | rigetti/quantumflow | quantumflow/measures.py | entropy | def entropy(rho: Density, base: float = None) -> float:
"""
Returns the von-Neumann entropy of a mixed quantum state.
Args:
rho: A density matrix
base: Optional logarithm base. Default is base e, and entropy is
measures in nats. For bits set base to 2.
Returns:
The von-Neumann entropy of rho
"""
op = asarray(rho.asoperator())
probs = np.linalg.eigvalsh(op)
probs = np.maximum(probs, 0.0) # Compensate for floating point errors
return scipy.stats.entropy(probs, base=base) | python | def entropy(rho: Density, base: float = None) -> float:
op = asarray(rho.asoperator())
probs = np.linalg.eigvalsh(op)
probs = np.maximum(probs, 0.0) # Compensate for floating point errors
return scipy.stats.entropy(probs, base=base) | [
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248,662 | rigetti/quantumflow | quantumflow/measures.py | mutual_info | def mutual_info(rho: Density,
qubits0: Qubits,
qubits1: Qubits = None,
base: float = None) -> float:
"""Compute the bipartite von-Neumann mutual information of a mixed
quantum state.
Args:
rho: A density matrix of the complete system
qubits0: Qubits of system 0
qubits1: Qubits of system 1. If none, taken to be all remaining qubits
base: Optional logarithm base. Default is base e
Returns:
The bipartite von-Neumann mutual information.
"""
if qubits1 is None:
qubits1 = tuple(set(rho.qubits) - set(qubits0))
rho0 = rho.partial_trace(qubits1)
rho1 = rho.partial_trace(qubits0)
ent = entropy(rho, base)
ent0 = entropy(rho0, base)
ent1 = entropy(rho1, base)
return ent0 + ent1 - ent | python | def mutual_info(rho: Density,
qubits0: Qubits,
qubits1: Qubits = None,
base: float = None) -> float:
if qubits1 is None:
qubits1 = tuple(set(rho.qubits) - set(qubits0))
rho0 = rho.partial_trace(qubits1)
rho1 = rho.partial_trace(qubits0)
ent = entropy(rho, base)
ent0 = entropy(rho0, base)
ent1 = entropy(rho1, base)
return ent0 + ent1 - ent | [
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248,663 | rigetti/quantumflow | quantumflow/measures.py | gate_angle | def gate_angle(gate0: Gate, gate1: Gate) -> bk.BKTensor:
"""The Fubini-Study angle between gates"""
return fubini_study_angle(gate0.vec, gate1.vec) | python | def gate_angle(gate0: Gate, gate1: Gate) -> bk.BKTensor:
return fubini_study_angle(gate0.vec, gate1.vec) | [
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248,664 | rigetti/quantumflow | quantumflow/measures.py | channel_angle | def channel_angle(chan0: Channel, chan1: Channel) -> bk.BKTensor:
"""The Fubini-Study angle between channels"""
return fubini_study_angle(chan0.vec, chan1.vec) | python | def channel_angle(chan0: Channel, chan1: Channel) -> bk.BKTensor:
return fubini_study_angle(chan0.vec, chan1.vec) | [
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248,665 | rigetti/quantumflow | quantumflow/qubits.py | inner_product | def inner_product(vec0: QubitVector, vec1: QubitVector) -> bk.BKTensor:
""" Hilbert-Schmidt inner product between qubit vectors
The tensor rank and qubits must match.
"""
if vec0.rank != vec1.rank or vec0.qubit_nb != vec1.qubit_nb:
raise ValueError('Incompatibly vectors. Qubits and rank must match')
vec1 = vec1.permute(vec0.qubits) # Make sure qubits in same order
return bk.inner(vec0.tensor, vec1.tensor) | python | def inner_product(vec0: QubitVector, vec1: QubitVector) -> bk.BKTensor:
if vec0.rank != vec1.rank or vec0.qubit_nb != vec1.qubit_nb:
raise ValueError('Incompatibly vectors. Qubits and rank must match')
vec1 = vec1.permute(vec0.qubits) # Make sure qubits in same order
return bk.inner(vec0.tensor, vec1.tensor) | [
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248,666 | rigetti/quantumflow | quantumflow/qubits.py | outer_product | def outer_product(vec0: QubitVector, vec1: QubitVector) -> QubitVector:
"""Direct product of qubit vectors
The tensor ranks must match and qubits must be disjoint.
"""
R = vec0.rank
R1 = vec1.rank
N0 = vec0.qubit_nb
N1 = vec1.qubit_nb
if R != R1:
raise ValueError('Incompatibly vectors. Rank must match')
if not set(vec0.qubits).isdisjoint(vec1.qubits):
raise ValueError('Overlapping qubits')
qubits: Qubits = tuple(vec0.qubits) + tuple(vec1.qubits)
tensor = bk.outer(vec0.tensor, vec1.tensor)
# Interleave (super)-operator axes
# R = 1 perm = (0, 1)
# R = 2 perm = (0, 2, 1, 3)
# R = 4 perm = (0, 4, 1, 5, 2, 6, 3, 7)
tensor = bk.reshape(tensor, ([2**N0] * R) + ([2**N1] * R))
perm = [idx for ij in zip(range(0, R), range(R, 2*R)) for idx in ij]
tensor = bk.transpose(tensor, perm)
return QubitVector(tensor, qubits) | python | def outer_product(vec0: QubitVector, vec1: QubitVector) -> QubitVector:
R = vec0.rank
R1 = vec1.rank
N0 = vec0.qubit_nb
N1 = vec1.qubit_nb
if R != R1:
raise ValueError('Incompatibly vectors. Rank must match')
if not set(vec0.qubits).isdisjoint(vec1.qubits):
raise ValueError('Overlapping qubits')
qubits: Qubits = tuple(vec0.qubits) + tuple(vec1.qubits)
tensor = bk.outer(vec0.tensor, vec1.tensor)
# Interleave (super)-operator axes
# R = 1 perm = (0, 1)
# R = 2 perm = (0, 2, 1, 3)
# R = 4 perm = (0, 4, 1, 5, 2, 6, 3, 7)
tensor = bk.reshape(tensor, ([2**N0] * R) + ([2**N1] * R))
perm = [idx for ij in zip(range(0, R), range(R, 2*R)) for idx in ij]
tensor = bk.transpose(tensor, perm)
return QubitVector(tensor, qubits) | [
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248,667 | rigetti/quantumflow | quantumflow/qubits.py | vectors_close | def vectors_close(vec0: QubitVector, vec1: QubitVector,
tolerance: float = TOLERANCE) -> bool:
"""Return True if vectors in close in the projective Hilbert space.
Similarity is measured with the Fubini–Study metric.
"""
if vec0.rank != vec1.rank:
return False
if vec0.qubit_nb != vec1.qubit_nb:
return False
if set(vec0.qubits) ^ set(vec1.qubits):
return False
return bk.evaluate(fubini_study_angle(vec0, vec1)) <= tolerance | python | def vectors_close(vec0: QubitVector, vec1: QubitVector,
tolerance: float = TOLERANCE) -> bool:
if vec0.rank != vec1.rank:
return False
if vec0.qubit_nb != vec1.qubit_nb:
return False
if set(vec0.qubits) ^ set(vec1.qubits):
return False
return bk.evaluate(fubini_study_angle(vec0, vec1)) <= tolerance | [
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248,668 | rigetti/quantumflow | quantumflow/qubits.py | QubitVector.flatten | def flatten(self) -> bk.BKTensor:
"""Return tensor with with qubit indices flattened"""
N = self.qubit_nb
R = self.rank
return bk.reshape(self.tensor, [2**N]*R) | python | def flatten(self) -> bk.BKTensor:
N = self.qubit_nb
R = self.rank
return bk.reshape(self.tensor, [2**N]*R) | [
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248,669 | rigetti/quantumflow | quantumflow/qubits.py | QubitVector.relabel | def relabel(self, qubits: Qubits) -> 'QubitVector':
"""Return a copy of this vector with new qubits"""
qubits = tuple(qubits)
assert len(qubits) == self.qubit_nb
vec = copy(self)
vec.qubits = qubits
return vec | python | def relabel(self, qubits: Qubits) -> 'QubitVector':
qubits = tuple(qubits)
assert len(qubits) == self.qubit_nb
vec = copy(self)
vec.qubits = qubits
return vec | [
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248,670 | rigetti/quantumflow | quantumflow/qubits.py | QubitVector.H | def H(self) -> 'QubitVector':
"""Return the conjugate transpose of this tensor."""
N = self.qubit_nb
R = self.rank
# (super) operator transpose
tensor = self.tensor
tensor = bk.reshape(tensor, [2**(N*R//2)] * 2)
tensor = bk.transpose(tensor)
tensor = bk.reshape(tensor, [2] * R * N)
tensor = bk.conj(tensor)
return QubitVector(tensor, self.qubits) | python | def H(self) -> 'QubitVector':
N = self.qubit_nb
R = self.rank
# (super) operator transpose
tensor = self.tensor
tensor = bk.reshape(tensor, [2**(N*R//2)] * 2)
tensor = bk.transpose(tensor)
tensor = bk.reshape(tensor, [2] * R * N)
tensor = bk.conj(tensor)
return QubitVector(tensor, self.qubits) | [
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248,671 | rigetti/quantumflow | quantumflow/qubits.py | QubitVector.norm | def norm(self) -> bk.BKTensor:
"""Return the norm of this vector"""
return bk.absolute(bk.inner(self.tensor, self.tensor)) | python | def norm(self) -> bk.BKTensor:
return bk.absolute(bk.inner(self.tensor, self.tensor)) | [
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248,672 | rigetti/quantumflow | quantumflow/qubits.py | QubitVector.partial_trace | def partial_trace(self, qubits: Qubits) -> 'QubitVector':
"""
Return the partial trace over some subset of qubits"""
N = self.qubit_nb
R = self.rank
if R == 1:
raise ValueError('Cannot take trace of vector')
new_qubits: List[Qubit] = list(self.qubits)
for q in qubits:
new_qubits.remove(q)
if not new_qubits:
raise ValueError('Cannot remove all qubits with partial_trace.')
indices = [self.qubits.index(qubit) for qubit in qubits]
subscripts = list(EINSUM_SUBSCRIPTS)[0:N*R]
for idx in indices:
for r in range(1, R):
subscripts[r * N + idx] = subscripts[idx]
subscript_str = ''.join(subscripts)
# Only numpy's einsum works with repeated subscripts
tensor = self.asarray()
tensor = np.einsum(subscript_str, tensor)
return QubitVector(tensor, new_qubits) | python | def partial_trace(self, qubits: Qubits) -> 'QubitVector':
N = self.qubit_nb
R = self.rank
if R == 1:
raise ValueError('Cannot take trace of vector')
new_qubits: List[Qubit] = list(self.qubits)
for q in qubits:
new_qubits.remove(q)
if not new_qubits:
raise ValueError('Cannot remove all qubits with partial_trace.')
indices = [self.qubits.index(qubit) for qubit in qubits]
subscripts = list(EINSUM_SUBSCRIPTS)[0:N*R]
for idx in indices:
for r in range(1, R):
subscripts[r * N + idx] = subscripts[idx]
subscript_str = ''.join(subscripts)
# Only numpy's einsum works with repeated subscripts
tensor = self.asarray()
tensor = np.einsum(subscript_str, tensor)
return QubitVector(tensor, new_qubits) | [
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248,673 | rigetti/quantumflow | examples/tensorflow2_fit_gate.py | fit_zyz | def fit_zyz(target_gate):
"""
Tensorflow 2.0 example. Given an arbitrary one-qubit gate, use
gradient descent to find corresponding parameters of a universal ZYZ
gate.
"""
steps = 1000
dev = '/gpu:0' if bk.DEVICE == 'gpu' else '/cpu:0'
with tf.device(dev):
t = tf.Variable(tf.random.normal([3]))
def loss_fn():
"""Loss"""
gate = qf.ZYZ(t[0], t[1], t[2])
ang = qf.fubini_study_angle(target_gate.vec, gate.vec)
return ang
opt = tf.optimizers.Adam(learning_rate=0.001)
opt.minimize(loss_fn, var_list=[t])
for step in range(steps):
opt.minimize(loss_fn, var_list=[t])
loss = loss_fn()
print(step, loss.numpy())
if loss < 0.01:
break
else:
print("Failed to coverge")
return bk.evaluate(t) | python | def fit_zyz(target_gate):
steps = 1000
dev = '/gpu:0' if bk.DEVICE == 'gpu' else '/cpu:0'
with tf.device(dev):
t = tf.Variable(tf.random.normal([3]))
def loss_fn():
"""Loss"""
gate = qf.ZYZ(t[0], t[1], t[2])
ang = qf.fubini_study_angle(target_gate.vec, gate.vec)
return ang
opt = tf.optimizers.Adam(learning_rate=0.001)
opt.minimize(loss_fn, var_list=[t])
for step in range(steps):
opt.minimize(loss_fn, var_list=[t])
loss = loss_fn()
print(step, loss.numpy())
if loss < 0.01:
break
else:
print("Failed to coverge")
return bk.evaluate(t) | [
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248,674 | rigetti/quantumflow | quantumflow/programs.py | Program.run | def run(self, ket: State = None) -> State:
"""Compiles and runs a program. The optional program argument
supplies the initial state and memory. Else qubits and classical
bits start from zero states.
"""
if ket is None:
qubits = self.qubits
ket = zero_state(qubits)
ket = self._initilize(ket)
pc = 0
while pc >= 0 and pc < len(self):
instr = self.instructions[pc]
ket = ket.update({PC: pc + 1})
ket = instr.run(ket)
pc = ket.memory[PC]
return ket | python | def run(self, ket: State = None) -> State:
if ket is None:
qubits = self.qubits
ket = zero_state(qubits)
ket = self._initilize(ket)
pc = 0
while pc >= 0 and pc < len(self):
instr = self.instructions[pc]
ket = ket.update({PC: pc + 1})
ket = instr.run(ket)
pc = ket.memory[PC]
return ket | [
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248,675 | rigetti/quantumflow | quantumflow/ops.py | Gate.relabel | def relabel(self, qubits: Qubits) -> 'Gate':
"""Return a copy of this Gate with new qubits"""
gate = copy(self)
gate.vec = gate.vec.relabel(qubits)
return gate | python | def relabel(self, qubits: Qubits) -> 'Gate':
gate = copy(self)
gate.vec = gate.vec.relabel(qubits)
return gate | [
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248,676 | rigetti/quantumflow | quantumflow/ops.py | Gate.run | def run(self, ket: State) -> State:
"""Apply the action of this gate upon a state"""
qubits = self.qubits
indices = [ket.qubits.index(q) for q in qubits]
tensor = bk.tensormul(self.tensor, ket.tensor, indices)
return State(tensor, ket.qubits, ket.memory) | python | def run(self, ket: State) -> State:
qubits = self.qubits
indices = [ket.qubits.index(q) for q in qubits]
tensor = bk.tensormul(self.tensor, ket.tensor, indices)
return State(tensor, ket.qubits, ket.memory) | [
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248,677 | rigetti/quantumflow | quantumflow/ops.py | Gate.evolve | def evolve(self, rho: Density) -> Density:
"""Apply the action of this gate upon a density"""
# TODO: implement without explicit channel creation?
chan = self.aschannel()
return chan.evolve(rho) | python | def evolve(self, rho: Density) -> Density:
# TODO: implement without explicit channel creation?
chan = self.aschannel()
return chan.evolve(rho) | [
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248,678 | rigetti/quantumflow | quantumflow/ops.py | Gate.aschannel | def aschannel(self) -> 'Channel':
"""Converts a Gate into a Channel"""
N = self.qubit_nb
R = 4
tensor = bk.outer(self.tensor, self.H.tensor)
tensor = bk.reshape(tensor, [2**N]*R)
tensor = bk.transpose(tensor, [0, 3, 1, 2])
return Channel(tensor, self.qubits) | python | def aschannel(self) -> 'Channel':
N = self.qubit_nb
R = 4
tensor = bk.outer(self.tensor, self.H.tensor)
tensor = bk.reshape(tensor, [2**N]*R)
tensor = bk.transpose(tensor, [0, 3, 1, 2])
return Channel(tensor, self.qubits) | [
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248,679 | rigetti/quantumflow | quantumflow/ops.py | Gate.su | def su(self) -> 'Gate':
"""Convert gate tensor to the special unitary group."""
rank = 2**self.qubit_nb
U = asarray(self.asoperator())
U /= np.linalg.det(U) ** (1/rank)
return Gate(tensor=U, qubits=self.qubits) | python | def su(self) -> 'Gate':
rank = 2**self.qubit_nb
U = asarray(self.asoperator())
U /= np.linalg.det(U) ** (1/rank)
return Gate(tensor=U, qubits=self.qubits) | [
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248,680 | rigetti/quantumflow | quantumflow/ops.py | Channel.relabel | def relabel(self, qubits: Qubits) -> 'Channel':
"""Return a copy of this channel with new qubits"""
chan = copy(self)
chan.vec = chan.vec.relabel(qubits)
return chan | python | def relabel(self, qubits: Qubits) -> 'Channel':
chan = copy(self)
chan.vec = chan.vec.relabel(qubits)
return chan | [
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248,681 | rigetti/quantumflow | quantumflow/ops.py | Channel.permute | def permute(self, qubits: Qubits) -> 'Channel':
"""Return a copy of this channel with qubits in new order"""
vec = self.vec.permute(qubits)
return Channel(vec.tensor, qubits=vec.qubits) | python | def permute(self, qubits: Qubits) -> 'Channel':
vec = self.vec.permute(qubits)
return Channel(vec.tensor, qubits=vec.qubits) | [
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248,682 | rigetti/quantumflow | quantumflow/ops.py | Channel.sharp | def sharp(self) -> 'Channel':
r"""Return the 'sharp' transpose of the superoperator.
The transpose :math:`S^\#` switches the two covariant (bra)
indices of the superoperator. (Which in our representation
are the 2nd and 3rd super-indices)
If :math:`S^\#` is Hermitian, then :math:`S` is a Hermitian-map
(i.e. transforms Hermitian operators to hJrmitian operators)
Flattening the :math:`S^\#` superoperator to a matrix gives
the Choi matrix representation. (See channel.choi())
"""
N = self.qubit_nb
tensor = self.tensor
tensor = bk.reshape(tensor, [2**N] * 4)
tensor = bk.transpose(tensor, (0, 2, 1, 3))
tensor = bk.reshape(tensor, [2] * 4 * N)
return Channel(tensor, self.qubits) | python | def sharp(self) -> 'Channel':
r"""Return the 'sharp' transpose of the superoperator.
The transpose :math:`S^\#` switches the two covariant (bra)
indices of the superoperator. (Which in our representation
are the 2nd and 3rd super-indices)
If :math:`S^\#` is Hermitian, then :math:`S` is a Hermitian-map
(i.e. transforms Hermitian operators to hJrmitian operators)
Flattening the :math:`S^\#` superoperator to a matrix gives
the Choi matrix representation. (See channel.choi())
"""
N = self.qubit_nb
tensor = self.tensor
tensor = bk.reshape(tensor, [2**N] * 4)
tensor = bk.transpose(tensor, (0, 2, 1, 3))
tensor = bk.reshape(tensor, [2] * 4 * N)
return Channel(tensor, self.qubits) | [
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248,683 | rigetti/quantumflow | quantumflow/ops.py | Channel.choi | def choi(self) -> bk.BKTensor:
"""Return the Choi matrix representation of this super
operator"""
# Put superop axes in [ok, ib, ob, ik] and reshape to matrix
N = self.qubit_nb
return bk.reshape(self.sharp.tensor, [2**(N*2)] * 2) | python | def choi(self) -> bk.BKTensor:
# Put superop axes in [ok, ib, ob, ik] and reshape to matrix
N = self.qubit_nb
return bk.reshape(self.sharp.tensor, [2**(N*2)] * 2) | [
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248,684 | rigetti/quantumflow | quantumflow/ops.py | Channel.evolve | def evolve(self, rho: Density) -> Density:
"""Apply the action of this channel upon a density"""
N = rho.qubit_nb
qubits = rho.qubits
indices = list([qubits.index(q) for q in self.qubits]) + \
list([qubits.index(q) + N for q in self.qubits])
tensor = bk.tensormul(self.tensor, rho.tensor, indices)
return Density(tensor, qubits, rho.memory) | python | def evolve(self, rho: Density) -> Density:
N = rho.qubit_nb
qubits = rho.qubits
indices = list([qubits.index(q) for q in self.qubits]) + \
list([qubits.index(q) + N for q in self.qubits])
tensor = bk.tensormul(self.tensor, rho.tensor, indices)
return Density(tensor, qubits, rho.memory) | [
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248,685 | rigetti/quantumflow | quantumflow/backend/eagerbk.py | fcast | def fcast(value: float) -> TensorLike:
"""Cast to float tensor"""
newvalue = tf.cast(value, FTYPE)
if DEVICE == 'gpu':
newvalue = newvalue.gpu() # Why is this needed? # pragma: no cover
return newvalue | python | def fcast(value: float) -> TensorLike:
newvalue = tf.cast(value, FTYPE)
if DEVICE == 'gpu':
newvalue = newvalue.gpu() # Why is this needed? # pragma: no cover
return newvalue | [
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248,686 | rigetti/quantumflow | quantumflow/backend/eagerbk.py | astensor | def astensor(array: TensorLike) -> BKTensor:
"""Convert to product tensor"""
tensor = tf.convert_to_tensor(array, dtype=CTYPE)
if DEVICE == 'gpu':
tensor = tensor.gpu() # pragma: no cover
# size = np.prod(np.array(tensor.get_shape().as_list()))
N = int(math.log2(size(tensor)))
tensor = tf.reshape(tensor, ([2]*N))
return tensor | python | def astensor(array: TensorLike) -> BKTensor:
tensor = tf.convert_to_tensor(array, dtype=CTYPE)
if DEVICE == 'gpu':
tensor = tensor.gpu() # pragma: no cover
# size = np.prod(np.array(tensor.get_shape().as_list()))
N = int(math.log2(size(tensor)))
tensor = tf.reshape(tensor, ([2]*N))
return tensor | [
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248,687 | rigetti/quantumflow | quantumflow/circuits.py | count_operations | def count_operations(elements: Iterable[Operation]) \
-> Dict[Type[Operation], int]:
"""Return a count of different operation types given a colelction of
operations, such as a Circuit or DAGCircuit
"""
op_count: Dict[Type[Operation], int] = defaultdict(int)
for elem in elements:
op_count[type(elem)] += 1
return dict(op_count) | python | def count_operations(elements: Iterable[Operation]) \
-> Dict[Type[Operation], int]:
op_count: Dict[Type[Operation], int] = defaultdict(int)
for elem in elements:
op_count[type(elem)] += 1
return dict(op_count) | [
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248,688 | rigetti/quantumflow | quantumflow/circuits.py | map_gate | def map_gate(gate: Gate, args: Sequence[Qubits]) -> Circuit:
"""Applies the same gate all input qubits in the argument list.
>>> circ = qf.map_gate(qf.H(), [[0], [1], [2]])
>>> print(circ)
H(0)
H(1)
H(2)
"""
circ = Circuit()
for qubits in args:
circ += gate.relabel(qubits)
return circ | python | def map_gate(gate: Gate, args: Sequence[Qubits]) -> Circuit:
circ = Circuit()
for qubits in args:
circ += gate.relabel(qubits)
return circ | [
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>>> circ = qf.map_gate(qf.H(), [[0], [1], [2]])
>>> print(circ)
H(0)
H(1)
H(2) | [
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248,689 | rigetti/quantumflow | quantumflow/circuits.py | qft_circuit | def qft_circuit(qubits: Qubits) -> Circuit:
"""Returns the Quantum Fourier Transform circuit"""
# Kudos: Adapted from Rigetti Grove, grove/qft/fourier.py
N = len(qubits)
circ = Circuit()
for n0 in range(N):
q0 = qubits[n0]
circ += H(q0)
for n1 in range(n0+1, N):
q1 = qubits[n1]
angle = pi / 2 ** (n1-n0)
circ += CPHASE(angle, q1, q0)
circ.extend(reversal_circuit(qubits))
return circ | python | def qft_circuit(qubits: Qubits) -> Circuit:
# Kudos: Adapted from Rigetti Grove, grove/qft/fourier.py
N = len(qubits)
circ = Circuit()
for n0 in range(N):
q0 = qubits[n0]
circ += H(q0)
for n1 in range(n0+1, N):
q1 = qubits[n1]
angle = pi / 2 ** (n1-n0)
circ += CPHASE(angle, q1, q0)
circ.extend(reversal_circuit(qubits))
return circ | [
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248,690 | rigetti/quantumflow | quantumflow/circuits.py | reversal_circuit | def reversal_circuit(qubits: Qubits) -> Circuit:
"""Returns a circuit to reverse qubits"""
N = len(qubits)
circ = Circuit()
for n in range(N // 2):
circ += SWAP(qubits[n], qubits[N-1-n])
return circ | python | def reversal_circuit(qubits: Qubits) -> Circuit:
N = len(qubits)
circ = Circuit()
for n in range(N // 2):
circ += SWAP(qubits[n], qubits[N-1-n])
return circ | [
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248,691 | rigetti/quantumflow | quantumflow/circuits.py | zyz_circuit | def zyz_circuit(t0: float, t1: float, t2: float, q0: Qubit) -> Circuit:
"""Circuit equivalent of 1-qubit ZYZ gate"""
circ = Circuit()
circ += TZ(t0, q0)
circ += TY(t1, q0)
circ += TZ(t2, q0)
return circ | python | def zyz_circuit(t0: float, t1: float, t2: float, q0: Qubit) -> Circuit:
circ = Circuit()
circ += TZ(t0, q0)
circ += TY(t1, q0)
circ += TZ(t2, q0)
return circ | [
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248,692 | rigetti/quantumflow | quantumflow/circuits.py | phase_estimation_circuit | def phase_estimation_circuit(gate: Gate, outputs: Qubits) -> Circuit:
"""Returns a circuit for quantum phase estimation.
The gate has an eigenvector with eigenvalue e^(i 2 pi phase). To
run the circuit, the eigenvector should be set on the gate qubits,
and the output qubits should be in the zero state. After evolution and
measurement, the output qubits will be (approximately) a binary fraction
representation of the phase.
The output registers can be converted with the aid of the
quantumflow.utils.bitlist_to_int() method.
>>> import numpy as np
>>> import quantumflow as qf
>>> N = 8
>>> phase = 1/4
>>> gate = qf.RZ(-4*np.pi*phase, N)
>>> circ = qf.phase_estimation_circuit(gate, range(N))
>>> res = circ.run().measure()[0:N]
>>> est_phase = int(''.join([str(d) for d in res]), 2) / 2**N # To float
>>> print(phase, est_phase)
0.25 0.25
"""
circ = Circuit()
circ += map_gate(H(), list(zip(outputs))) # Hadamard on all output qubits
for cq in reversed(outputs):
cgate = control_gate(cq, gate)
circ += cgate
gate = gate @ gate
circ += qft_circuit(outputs).H
return circ | python | def phase_estimation_circuit(gate: Gate, outputs: Qubits) -> Circuit:
circ = Circuit()
circ += map_gate(H(), list(zip(outputs))) # Hadamard on all output qubits
for cq in reversed(outputs):
cgate = control_gate(cq, gate)
circ += cgate
gate = gate @ gate
circ += qft_circuit(outputs).H
return circ | [
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The gate has an eigenvector with eigenvalue e^(i 2 pi phase). To
run the circuit, the eigenvector should be set on the gate qubits,
and the output qubits should be in the zero state. After evolution and
measurement, the output qubits will be (approximately) a binary fraction
representation of the phase.
The output registers can be converted with the aid of the
quantumflow.utils.bitlist_to_int() method.
>>> import numpy as np
>>> import quantumflow as qf
>>> N = 8
>>> phase = 1/4
>>> gate = qf.RZ(-4*np.pi*phase, N)
>>> circ = qf.phase_estimation_circuit(gate, range(N))
>>> res = circ.run().measure()[0:N]
>>> est_phase = int(''.join([str(d) for d in res]), 2) / 2**N # To float
>>> print(phase, est_phase)
0.25 0.25 | [
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] | 13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb | https://github.com/rigetti/quantumflow/blob/13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb/quantumflow/circuits.py#L269-L303 |
248,693 | rigetti/quantumflow | quantumflow/circuits.py | ghz_circuit | def ghz_circuit(qubits: Qubits) -> Circuit:
"""Returns a circuit that prepares a multi-qubit Bell state from the zero
state.
"""
circ = Circuit()
circ += H(qubits[0])
for q0 in range(0, len(qubits)-1):
circ += CNOT(qubits[q0], qubits[q0+1])
return circ | python | def ghz_circuit(qubits: Qubits) -> Circuit:
circ = Circuit()
circ += H(qubits[0])
for q0 in range(0, len(qubits)-1):
circ += CNOT(qubits[q0], qubits[q0+1])
return circ | [
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248,694 | rigetti/quantumflow | quantumflow/circuits.py | Circuit.extend | def extend(self, other: Operation) -> None:
"""Append gates from circuit to the end of this circuit"""
if isinstance(other, Circuit):
self.elements.extend(other.elements)
else:
self.elements.extend([other]) | python | def extend(self, other: Operation) -> None:
if isinstance(other, Circuit):
self.elements.extend(other.elements)
else:
self.elements.extend([other]) | [
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248,695 | rigetti/quantumflow | quantumflow/circuits.py | Circuit.run | def run(self, ket: State = None) -> State:
"""
Apply the action of this circuit upon a state.
If no initial state provided an initial zero state will be created.
"""
if ket is None:
qubits = self.qubits
ket = zero_state(qubits=qubits)
for elem in self.elements:
ket = elem.run(ket)
return ket | python | def run(self, ket: State = None) -> State:
if ket is None:
qubits = self.qubits
ket = zero_state(qubits=qubits)
for elem in self.elements:
ket = elem.run(ket)
return ket | [
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248,696 | rigetti/quantumflow | quantumflow/circuits.py | Circuit.asgate | def asgate(self) -> Gate:
"""
Return the action of this circuit as a gate
"""
gate = identity_gate(self.qubits)
for elem in self.elements:
gate = elem.asgate() @ gate
return gate | python | def asgate(self) -> Gate:
gate = identity_gate(self.qubits)
for elem in self.elements:
gate = elem.asgate() @ gate
return gate | [
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248,697 | rigetti/quantumflow | quantumflow/channels.py | join_channels | def join_channels(*channels: Channel) -> Channel:
"""Join two channels acting on different qubits into a single channel
acting on all qubits"""
vectors = [chan.vec for chan in channels]
vec = reduce(outer_product, vectors)
return Channel(vec.tensor, vec.qubits) | python | def join_channels(*channels: Channel) -> Channel:
vectors = [chan.vec for chan in channels]
vec = reduce(outer_product, vectors)
return Channel(vec.tensor, vec.qubits) | [
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248,698 | rigetti/quantumflow | quantumflow/channels.py | channel_to_kraus | def channel_to_kraus(chan: Channel) -> 'Kraus':
"""Convert a channel superoperator into a Kraus operator representation
of the same channel."""
qubits = chan.qubits
N = chan.qubit_nb
choi = asarray(chan.choi())
evals, evecs = np.linalg.eig(choi)
evecs = np.transpose(evecs)
assert np.allclose(evals.imag, 0.0) # FIXME exception
assert np.all(evals.real >= 0.0) # FIXME exception
values = np.sqrt(evals.real)
ops = []
for i in range(2**(2*N)):
if not np.isclose(values[i], 0.0):
mat = np.reshape(evecs[i], (2**N, 2**N))*values[i]
g = Gate(mat, qubits)
ops.append(g)
return Kraus(ops) | python | def channel_to_kraus(chan: Channel) -> 'Kraus':
qubits = chan.qubits
N = chan.qubit_nb
choi = asarray(chan.choi())
evals, evecs = np.linalg.eig(choi)
evecs = np.transpose(evecs)
assert np.allclose(evals.imag, 0.0) # FIXME exception
assert np.all(evals.real >= 0.0) # FIXME exception
values = np.sqrt(evals.real)
ops = []
for i in range(2**(2*N)):
if not np.isclose(values[i], 0.0):
mat = np.reshape(evecs[i], (2**N, 2**N))*values[i]
g = Gate(mat, qubits)
ops.append(g)
return Kraus(ops) | [
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248,699 | rigetti/quantumflow | quantumflow/channels.py | Kraus.run | def run(self, ket: State) -> State:
"""Apply the action of this Kraus quantum operation upon a state"""
res = [op.run(ket) for op in self.operators]
probs = [asarray(ket.norm()) * w for ket, w in zip(res, self.weights)]
probs = np.asarray(probs)
probs /= np.sum(probs)
newket = np.random.choice(res, p=probs)
return newket.normalize() | python | def run(self, ket: State) -> State:
res = [op.run(ket) for op in self.operators]
probs = [asarray(ket.norm()) * w for ket, w in zip(res, self.weights)]
probs = np.asarray(probs)
probs /= np.sum(probs)
newket = np.random.choice(res, p=probs)
return newket.normalize() | [
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Subsets and Splits