id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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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... | 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._... | 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... | 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... | [
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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('\nSkippi... | 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('\nSkippi... | [
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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 si... | 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... | 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 s... | [
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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_hamiltonia... | 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)
... | [
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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(da... | 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
... | [
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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.probab... | 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... | [
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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()))
r... | 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... | 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_h... | [
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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(... | 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... | 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.CN... | [
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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])
c... | 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.... | [
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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, c... | 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)
... | 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... | [
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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.iden... | 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/Expo... | [
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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(ele... | 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... | [
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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... | 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
each contravariant index, e.g. for 3-qubit states
:math... | [
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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,... | 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))
... | 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))
retu... | 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... | 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... | 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 []
... | 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)
el... | [
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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 isinst... | 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
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"... | Convert a protoquil pyQuil program to a QuantumFlow Circuit | [
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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])
... | 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._pr... | 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
... | 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, whi... | [
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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... | 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].cp... | [
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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 ... | 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... | 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:
... | 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
qubits... | 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 = ent... | [
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rho: A density matrix of the complete system
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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 ma... | 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, v... | [
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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... | 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('O... | [
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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.qubi... | 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(fubi... | [
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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.r... | 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)... | [
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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)
... | 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_qubi... | [
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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.r... | 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, ... | [
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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... | 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})
... | [
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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 :mat... | 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 :mat... | [
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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(se... | 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,... | [
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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 =... | 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))
re... | [
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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_c... | 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)... | 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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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):
... | 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)
... | [
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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.... | 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(out... | [
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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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] | 13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb | https://github.com/rigetti/quantumflow/blob/13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb/quantumflow/circuits.py#L53-L58 |
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... | 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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] | 13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb | https://github.com/rigetti/quantumflow/blob/13a66cabbe8aabf6e023cc675f4a4ebe6ccda8fb/quantumflow/circuits.py#L87-L99 |
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.al... | 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
... | [
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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)
new... | 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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