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train | Stepper._init_scratch | Initializes a scratch pad equal in size to the wavefunction. | cirq/google/sim/xmon_stepper.py | def _init_scratch(self):
"""Initializes a scratch pad equal in size to the wavefunction."""
scratch = np.zeros((self._num_shards, self._shard_size),
dtype=np.complex64)
scratch_handle = mem_manager.SharedMemManager.create_array(
scratch.view(dtype=np.float3... | def _init_scratch(self):
"""Initializes a scratch pad equal in size to the wavefunction."""
scratch = np.zeros((self._num_shards, self._shard_size),
dtype=np.complex64)
scratch_handle = mem_manager.SharedMemManager.create_array(
scratch.view(dtype=np.float3... | [
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train | Stepper._init_state | Initializes a the shard wavefunction and sets the initial state. | cirq/google/sim/xmon_stepper.py | def _init_state(self, initial_state: Union[int, np.ndarray]):
"""Initializes a the shard wavefunction and sets the initial state."""
state = np.reshape(
sim.to_valid_state_vector(initial_state, self._num_qubits),
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state_handle = mem_ma... | def _init_state(self, initial_state: Union[int, np.ndarray]):
"""Initializes a the shard wavefunction and sets the initial state."""
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train | Stepper._shard_num_args | Helper that returns a list of dicts including a num_shard entry.
The dict for each entry also includes shared_mem_dict, the number of
shards, the number of shard qubits, and the supplied constant dict.
Args:
constant_dict: Dictionary that will be updated to every element of
... | cirq/google/sim/xmon_stepper.py | def _shard_num_args(self,
constant_dict: Dict[str, Any] = None
) -> List[Dict[str, Any]]:
"""Helper that returns a list of dicts including a num_shard entry.
The dict for each entry also includes shared_mem_dict, the number of
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train | Stepper.reset_state | Reset the state to the given initial state.
Args:
reset_state: If this is an int, then this is the state to reset
the stepper to, expressed as an integer of the computational
basis. Integer to bitwise indices is little endian. Otherwise
if this is a n... | cirq/google/sim/xmon_stepper.py | def reset_state(self, reset_state):
"""Reset the state to the given initial state.
Args:
reset_state: If this is an int, then this is the state to reset
the stepper to, expressed as an integer of the computational
basis. Integer to bitwise indices is little e... | def reset_state(self, reset_state):
"""Reset the state to the given initial state.
Args:
reset_state: If this is an int, then this is the state to reset
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train | Stepper.simulate_phases | Simulate a set of phase gates on the xmon architecture.
Args:
phase_map: A map from a tuple of indices to a value, one for each
phase gate being simulated. If the tuple key has one index, then
this is a Z phase gate on the index-th qubit with a rotation
... | cirq/google/sim/xmon_stepper.py | def simulate_phases(self, phase_map: Dict[Tuple[int, ...], float]):
"""Simulate a set of phase gates on the xmon architecture.
Args:
phase_map: A map from a tuple of indices to a value, one for each
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... | def simulate_phases(self, phase_map: Dict[Tuple[int, ...], float]):
"""Simulate a set of phase gates on the xmon architecture.
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phase_map: A map from a tuple of indices to a value, one for each
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train | Stepper.simulate_w | Simulate a single qubit rotation gate about a X + b Y.
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where W = cos(pi axis_half_turns) X + sin(pi axis_half_turns) Y
Args:
index: The qubit to act on.
half_turns: The amount of the overall rotation, see the formula
... | cirq/google/sim/xmon_stepper.py | def simulate_w(self,
index: int,
half_turns: float,
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"""Simulate a single qubit rotation gate about a X + b Y.
The gate simulated is U = exp(-i pi/2 W half_turns)
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train | Stepper.simulate_measurement | Simulates a single qubit measurement in the computational basis.
Args:
index: Which qubit is measured.
Returns:
True iff the measurement result corresponds to the |1> state. | cirq/google/sim/xmon_stepper.py | def simulate_measurement(self, index: int) -> bool:
"""Simulates a single qubit measurement in the computational basis.
Args:
index: Which qubit is measured.
Returns:
True iff the measurement result corresponds to the |1> state.
"""
args = self._shard_nu... | def simulate_measurement(self, index: int) -> bool:
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train | Stepper.sample_measurements | Samples from measurements in the computational basis.
Note that this does not collapse the wave function.
Args:
indices: Which qubits are measured.
Returns:
Measurement results with True corresponding to the |1> state.
The outer list is for repetitions, and... | cirq/google/sim/xmon_stepper.py | def sample_measurements(
self,
indices: List[int],
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"""Samples from measurements in the computational basis.
Note that this does not collapse the wave function.
Args:
indices: Which qubits are measured.
... | def sample_measurements(
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indices: List[int],
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Note that this does not collapse the wave function.
Args:
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train | fswap | Decompose the Fermionic SWAP gate into two single-qubit gates and
one iSWAP gate.
Args:
p: the id of the first qubit
q: the id of the second qubit | examples/bcs_mean_field.py | def fswap(p, q):
"""Decompose the Fermionic SWAP gate into two single-qubit gates and
one iSWAP gate.
Args:
p: the id of the first qubit
q: the id of the second qubit
"""
yield cirq.ISWAP(q, p), cirq.Z(p) ** 1.5
yield cirq.Z(q) ** 1.5 | def fswap(p, q):
"""Decompose the Fermionic SWAP gate into two single-qubit gates and
one iSWAP gate.
Args:
p: the id of the first qubit
q: the id of the second qubit
"""
yield cirq.ISWAP(q, p), cirq.Z(p) ** 1.5
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train | fermi_fourier_trans_2 | The 2-mode fermionic Fourier transformation can be implemented
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"""The 2-mode fermionic Fourier transformation can be implemented
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train | fermi_fourier_trans_inverse_4 | The reverse fermionic Fourier transformation implemented on 4 qubits
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Using the fast Fourier transformation algorithm, the circuit can be
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SWAP gates, and single-qubit... | examples/bcs_mean_field.py | def fermi_fourier_trans_inverse_4(qubits):
"""The reverse fermionic Fourier transformation implemented on 4 qubits
on a line, which maps the momentum picture to the position picture.
Using the fast Fourier transformation algorithm, the circuit can be
decomposed into 2-mode fermionic Fourier transformati... | def fermi_fourier_trans_inverse_4(qubits):
"""The reverse fermionic Fourier transformation implemented on 4 qubits
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Using the fast Fourier transformation algorithm, the circuit can be
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train | fermi_fourier_trans_inverse_conjugate_4 | We will need to map the momentum states in the reversed order for
spin-down states to the position picture. This transformation can be
simply implemented the complex conjugate of the former one. We only
need to change the S gate to S* = S ** 3.
Args:
qubits: list of four qubits | examples/bcs_mean_field.py | def fermi_fourier_trans_inverse_conjugate_4(qubits):
"""We will need to map the momentum states in the reversed order for
spin-down states to the position picture. This transformation can be
simply implemented the complex conjugate of the former one. We only
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... | def fermi_fourier_trans_inverse_conjugate_4(qubits):
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train | bcs_parameters | Generate the parameters for the BCS ground state, i.e., the
superconducting gap and the rotational angles in the Bogoliubov
transformation.
Args:
n_site: the number of sites in the Hubbard model
n_fermi: the number of fermions
u: the interaction strength
t: the tunneling st... | examples/bcs_mean_field.py | def bcs_parameters(n_site, n_fermi, u, t) :
"""Generate the parameters for the BCS ground state, i.e., the
superconducting gap and the rotational angles in the Bogoliubov
transformation.
Args:
n_site: the number of sites in the Hubbard model
n_fermi: the number of fermions
u: t... | def bcs_parameters(n_site, n_fermi, u, t) :
"""Generate the parameters for the BCS ground state, i.e., the
superconducting gap and the rotational angles in the Bogoliubov
transformation.
Args:
n_site: the number of sites in the Hubbard model
n_fermi: the number of fermions
u: t... | [
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train | bloch_vector_from_state_vector | Returns the bloch vector of a qubit.
Calculates the bloch vector of the qubit at index
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Args:
state: A sequence representing a wave function in which
the ordering mapping to q... | cirq/sim/wave_function.py | def bloch_vector_from_state_vector(state: Sequence, index: int) -> np.ndarray:
"""Returns the bloch vector of a qubit.
Calculates the bloch vector of the qubit at index
in the wavefunction given by state, assuming state follows
the standard Kronecker convention of numpy.kron.
Args:
state: ... | def bloch_vector_from_state_vector(state: Sequence, index: int) -> np.ndarray:
"""Returns the bloch vector of a qubit.
Calculates the bloch vector of the qubit at index
in the wavefunction given by state, assuming state follows
the standard Kronecker convention of numpy.kron.
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train | density_matrix_from_state_vector | r"""Returns the density matrix of the wavefunction.
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indices, with the qubits not in indices that are present in state
traced out. If indices is None the full density matrix for state
is returned. We assume state follows the standard Kroneck... | cirq/sim/wave_function.py | def density_matrix_from_state_vector(
state: Sequence,
indices: Iterable[int] = None
) -> np.ndarray:
r"""Returns the density matrix of the wavefunction.
Calculate the density matrix for the system on the given qubit
indices, with the qubits not in indices that are present in state
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state: Sequence,
indices: Iterable[int] = None
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r"""Returns the density matrix of the wavefunction.
Calculate the density matrix for the system on the given qubit
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train | dirac_notation | Returns the wavefunction as a string in Dirac notation.
For example:
state = np.array([1/np.sqrt(2), 1/np.sqrt(2)], dtype=np.complex64)
print(dirac_notation(state)) -> 0.71|0⟩ + 0.71|1⟩
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state: A sequence representing a wave function in which the ordering
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"""Returns the wavefunction as a string in Dirac notation.
For example:
state = np.array([1/np.sqrt(2), 1/np.sqrt(2)], dtype=np.complex64)
print(dirac_notation(state)) -> 0.71|0⟩ + 0.71|1⟩
Args:
state: A sequence represe... | def dirac_notation(state: Sequence, decimals: int=2) -> str:
"""Returns the wavefunction as a string in Dirac notation.
For example:
state = np.array([1/np.sqrt(2), 1/np.sqrt(2)], dtype=np.complex64)
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train | to_valid_state_vector | Verifies the state_rep is valid and converts it to ndarray form.
This method is used to support passing in an integer representing a
computational basis state or a full wave function as a representation of
a state.
Args:
state_rep: If an int, the state returned is the state corresponding to
... | cirq/sim/wave_function.py | def to_valid_state_vector(state_rep: Union[int, np.ndarray],
num_qubits: int,
dtype: Type[np.number] = np.complex64) -> np.ndarray:
"""Verifies the state_rep is valid and converts it to ndarray form.
This method is used to support passing in an integer repres... | def to_valid_state_vector(state_rep: Union[int, np.ndarray],
num_qubits: int,
dtype: Type[np.number] = np.complex64) -> np.ndarray:
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train | validate_normalized_state | Validates that the given state is a valid wave function. | cirq/sim/wave_function.py | def validate_normalized_state(state: np.ndarray,
num_qubits: int,
dtype: Type[np.number] = np.complex64) -> None:
"""Validates that the given state is a valid wave function."""
if state.size != 1 << num_qubits:
raise ValueError(
'St... | def validate_normalized_state(state: np.ndarray,
num_qubits: int,
dtype: Type[np.number] = np.complex64) -> None:
"""Validates that the given state is a valid wave function."""
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train | sample_state_vector | Samples repeatedly from measurements in the computational basis.
Note that this does not modify the passed in state.
Args:
state: The multi-qubit wavefunction to be sampled. This is an array of
2 to the power of the number of qubit complex numbers, and so
state must be of size ... | cirq/sim/wave_function.py | def sample_state_vector(state: np.ndarray,
indices: List[int],
repetitions: int=1) -> np.ndarray:
"""Samples repeatedly from measurements in the computational basis.
Note that this does not modify the passed in state.
Args:
state: The multi-qubit wav... | def sample_state_vector(state: np.ndarray,
indices: List[int],
repetitions: int=1) -> np.ndarray:
"""Samples repeatedly from measurements in the computational basis.
Note that this does not modify the passed in state.
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train | _probs | Returns the probabilities for a measurement on the given indices. | cirq/sim/wave_function.py | def _probs(state: np.ndarray, indices: List[int],
num_qubits: int) -> List[float]:
"""Returns the probabilities for a measurement on the given indices."""
# Tensor of squared amplitudes, shaped a rank [2, 2, .., 2] tensor.
tensor = np.reshape(state, [2] * num_qubits)
# Calculate the probabil... | def _probs(state: np.ndarray, indices: List[int],
num_qubits: int) -> List[float]:
"""Returns the probabilities for a measurement on the given indices."""
# Tensor of squared amplitudes, shaped a rank [2, 2, .., 2] tensor.
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train | _validate_num_qubits | Validates that state's size is a power of 2, returning number of qubits. | cirq/sim/wave_function.py | def _validate_num_qubits(state: np.ndarray) -> int:
"""Validates that state's size is a power of 2, returning number of qubits.
"""
size = state.size
if size & (size - 1):
raise ValueError('state.size ({}) is not a power of two.'.format(size))
return size.bit_length() - 1 | def _validate_num_qubits(state: np.ndarray) -> int:
"""Validates that state's size is a power of 2, returning number of qubits.
"""
size = state.size
if size & (size - 1):
raise ValueError('state.size ({}) is not a power of two.'.format(size))
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train | _validate_indices | Validates that the indices have values within range of num_qubits. | cirq/sim/wave_function.py | def _validate_indices(num_qubits: int, indices: List[int]) -> None:
"""Validates that the indices have values within range of num_qubits."""
if any(index < 0 for index in indices):
raise IndexError('Negative index in indices: {}'.format(indices))
if any(index >= num_qubits for index in indices):
... | def _validate_indices(num_qubits: int, indices: List[int]) -> None:
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raise IndexError('Negative index in indices: {}'.format(indices))
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train | StateVectorMixin.density_matrix_of | r"""Returns the density matrix of the state.
Calculate the density matrix for the system on the list, qubits.
Any qubits not in the list that are present in self.state_vector() will
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r"""Returns the density matrix of the state.
Calculate the density matrix for the system on the list, qubits.
Any qubits not in the list that are present in self.state_vector() will
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r"""Returns the density matrix of the state.
Calculate the density matrix for the system on the list, qubits.
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train | StateVectorMixin.bloch_vector_of | Returns the bloch vector of a qubit in the state.
Calculates the bloch vector of the given qubit
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self.state_vector() follows the standard Kronecker convention of
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qubit: qubit who's bloch vector ... | cirq/sim/wave_function.py | def bloch_vector_of(self, qubit: ops.Qid) -> np.ndarray:
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Calculates the bloch vector of the given qubit
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train | get_acquaintance_size | The maximum number of qubits to be acquainted with each other. | cirq/contrib/acquaintance/devices.py | def get_acquaintance_size(obj: Union[circuits.Circuit, ops.Operation]) -> int:
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if isinstance(obj, circuits.Circuit):
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"""The maximum number of qubits to be acquainted with each other."""
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train | GreedySequenceSearch.get_or_search | Starts the search or gives previously calculated sequence.
Returns:
The linear qubit sequence found. | cirq/google/line/placement/greedy.py | def get_or_search(self) -> List[GridQubit]:
"""Starts the search or gives previously calculated sequence.
Returns:
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"""
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"""Starts the search or gives previously calculated sequence.
Returns:
The linear qubit sequence found.
"""
if not self._sequence:
self._sequence = self._find_sequence()
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train | GreedySequenceSearch._find_sequence | Looks for a sequence starting at a given qubit.
Search is issued twice from the starting qubit, so that longest possible
sequence is found. Starting qubit might not be the first qubit on the
returned sequence.
Returns:
The longest sequence found by this method. | cirq/google/line/placement/greedy.py | def _find_sequence(self) -> List[GridQubit]:
"""Looks for a sequence starting at a given qubit.
Search is issued twice from the starting qubit, so that longest possible
sequence is found. Starting qubit might not be the first qubit on the
returned sequence.
Returns:
... | def _find_sequence(self) -> List[GridQubit]:
"""Looks for a sequence starting at a given qubit.
Search is issued twice from the starting qubit, so that longest possible
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train | GreedySequenceSearch._sequence_search | Search for the continuous linear sequence from the given qubit.
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Args:
start: The first qubit, where search should be trigerred from.
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Args:
seq: Linear sequence of qubits.
Returns:
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Args:
seq: Linear sequence of qubits.
Returns:
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train | GreedySequenceSearch._find_path_between | Searches for continuous sequence between two qubits.
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one starting from qubit q. If at some point a qubit reachable from p is
found to be on the... | cirq/google/line/placement/greedy.py | def _find_path_between(self, p: GridQubit, q: GridQubit,
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This method runs two BFS algorithms in parallel (alternating variable s
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train | _PickLargestArea._collect_unused | Lists all the qubits that are reachable from given qubit.
Args:
start: The first qubit for which connectivity should be calculated.
Might be a member of used set.
used: Already used qubits, which cannot be used during the
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Args:
start: The first qubit for which connectivity should be calculated.
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train | GreedySequenceSearchStrategy.place_line | Runs line sequence search.
Args:
device: Chip description.
length: Required line length.
Returns:
Linear sequences found on the chip.
Raises:
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length: int) -> GridQubitLineTuple:
"""Runs line sequence search.
Args:
device: Chip description.
length: Required line length.
Returns:
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device: 'cirq.google.XmonDevice',
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Args:
device: Chip description.
length: Required line length.
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train | trace_distance_bound | Returns a maximum on the trace distance between this effect's input
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method to determine the maximum bound on the trace difference between
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Args:
val: The effect of which the bound should be c... | cirq/protocols/trace_distance_bound.py | def trace_distance_bound(val: Any) -> float:
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method to determine the maximum bound on the trace difference between
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Args:
... | def trace_distance_bound(val: Any) -> float:
"""Returns a maximum on the trace distance between this effect's input
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method to determine the maximum bound on the trace difference between
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train | Moment.operates_on | Determines if the moment has operations touching the given qubits.
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Returns:
Whether this moment has operations involving the qubits. | cirq/ops/moment.py | def operates_on(self, qubits: Iterable[raw_types.Qid]) -> bool:
"""Determines if the moment has operations touching the given qubits.
Args:
qubits: The qubits that may or may not be touched by operations.
Returns:
Whether this moment has operations involving the qubits.... | def operates_on(self, qubits: Iterable[raw_types.Qid]) -> bool:
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train | Moment.without_operations_touching | Returns an equal moment, but without ops on the given qubits.
Args:
qubits: Operations that touch these will be removed.
Returns:
The new moment. | cirq/ops/moment.py | def without_operations_touching(self, qubits: Iterable[raw_types.Qid]):
"""Returns an equal moment, but without ops on the given qubits.
Args:
qubits: Operations that touch these will be removed.
Returns:
The new moment.
"""
qubits = frozenset(qubits)
... | def without_operations_touching(self, qubits: Iterable[raw_types.Qid]):
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Args:
qubits: Operations that touch these will be removed.
Returns:
The new moment.
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train | Moment._approx_eq_ | See `cirq.protocols.SupportsApproximateEquality`. | cirq/ops/moment.py | def _approx_eq_(self, other: Any, atol: Union[int, float]) -> bool:
"""See `cirq.protocols.SupportsApproximateEquality`."""
if not isinstance(other, type(self)):
return NotImplemented
return approx_eq(self.operations, other.operations, atol=atol) | def _approx_eq_(self, other: Any, atol: Union[int, float]) -> bool:
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if not isinstance(other, type(self)):
return NotImplemented
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train | _disjoint_qubits | Returns true only if the operations have qubits in common. | cirq/circuits/circuit_dag.py | def _disjoint_qubits(op1: ops.Operation, op2: ops.Operation) -> bool:
"""Returns true only if the operations have qubits in common."""
return not set(op1.qubits) & set(op2.qubits) | def _disjoint_qubits(op1: ops.Operation, op2: ops.Operation) -> bool:
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train | PointOptimizer.optimization_at | Describes how to change operations near the given location.
For example, this method could realize that the given operation is an
X gate and that in the very next moment there is a Z gate. It would
indicate that they should be combined into a Y gate by returning
PointOptimizationSummary... | cirq/circuits/optimization_pass.py | def optimization_at(self,
circuit: Circuit,
index: int,
op: ops.Operation
) -> Optional[PointOptimizationSummary]:
"""Describes how to change operations near the given location.
For example, this method coul... | def optimization_at(self,
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train | schedule_to_proto_dicts | Convert a schedule into an iterable of proto dictionaries.
Args:
schedule: The schedule to convert to a proto dict. Must contain only
gates that can be cast to xmon gates.
Yields:
A proto dictionary corresponding to an Operation proto. | cirq/google/programs.py | def schedule_to_proto_dicts(schedule: Schedule) -> Iterable[Dict]:
"""Convert a schedule into an iterable of proto dictionaries.
Args:
schedule: The schedule to convert to a proto dict. Must contain only
gates that can be cast to xmon gates.
Yields:
A proto dictionary correspon... | def schedule_to_proto_dicts(schedule: Schedule) -> Iterable[Dict]:
"""Convert a schedule into an iterable of proto dictionaries.
Args:
schedule: The schedule to convert to a proto dict. Must contain only
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train | schedule_from_proto_dicts | Convert proto dictionaries into a Schedule for the given device. | cirq/google/programs.py | def schedule_from_proto_dicts(
device: 'xmon_device.XmonDevice',
ops: Iterable[Dict],
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"""Convert proto dictionaries into a Schedule for the given device."""
scheduled_ops = []
last_time_picos = 0
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train | pack_results | Pack measurement results into a byte string.
Args:
measurements: A sequence of tuples, one for each measurement, consisting
of a string key and an array of boolean data. The data should be
a 2-D array indexed by (repetition, qubit_index). All data for all
measurements mu... | cirq/google/programs.py | def pack_results(measurements: Sequence[Tuple[str, np.ndarray]]) -> bytes:
"""Pack measurement results into a byte string.
Args:
measurements: A sequence of tuples, one for each measurement, consisting
of a string key and an array of boolean data. The data should be
a 2-D array ... | def pack_results(measurements: Sequence[Tuple[str, np.ndarray]]) -> bytes:
"""Pack measurement results into a byte string.
Args:
measurements: A sequence of tuples, one for each measurement, consisting
of a string key and an array of boolean data. The data should be
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train | unpack_results | Unpack data from a bitstring into individual measurement results.
Args:
data: Packed measurement results, in the form <rep0><rep1>...
where each repetition is <key0_0>..<key0_{size0-1}><key1_0>...
with bits packed in little-endian order in each byte.
repetitions: number of r... | cirq/google/programs.py | def unpack_results(
data: bytes,
repetitions: int,
key_sizes: Sequence[Tuple[str, int]]
) -> Dict[str, np.ndarray]:
"""Unpack data from a bitstring into individual measurement results.
Args:
data: Packed measurement results, in the form <rep0><rep1>...
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train | Engine.run | Runs the supplied Circuit or Schedule via Quantum Engine.
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program: The Circuit or Schedule to execute. If a circuit is
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job_config: Configures the names of programs and jobs.
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train | Engine.get_program | Returns the previously created quantum program.
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program_resource_name: A string of the form
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Returns:
A dictionary containing the metadata and the program. | cirq/google/engine/engine.py | def get_program(self, program_resource_name: str) -> Dict:
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program_resource_name: A string of the form
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Params:
job_resource_name: A string of the form
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train | Engine.cancel_job | Cancels the given job.
See also the cancel method on EngineJob.
Params:
job_resource_name: A string of the form
`projects/project_id/programs/program_id/jobs/job_id`. | cirq/google/engine/engine.py | def cancel_job(self, job_resource_name: str):
"""Cancels the given job.
See also the cancel method on EngineJob.
Params:
job_resource_name: A string of the form
`projects/project_id/programs/program_id/jobs/job_id`.
"""
self.service.projects().progra... | def cancel_job(self, job_resource_name: str):
"""Cancels the given job.
See also the cancel method on EngineJob.
Params:
job_resource_name: A string of the form
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train | EngineJob.results | Returns the job results, blocking until the job is complete. | cirq/google/engine/engine.py | def results(self) -> List[TrialResult]:
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train | flatten_op_tree | Performs an in-order iteration of the operations (leaves) in an OP_TREE.
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preserve_moments: Whether to yield Moments intact instead of
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op_transformation: How to transform the operations (i.e. leaves).
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train | circuit_to_quirk_url | Returns a Quirk URL for the given circuit.
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prefer_unknown_gate_to_failure: If not set, gates that fail to convert
will cause this function to raise an error. If set, a URL
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Args:
circuit: The circuit to open in Quirk.
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circuit: The circuit to open in Quirk.
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train | GridQubit.is_adjacent | Determines if two qubits are adjacent qubits. | cirq/devices/grid_qubit.py | def is_adjacent(self, other: ops.Qid) -> bool:
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return (isinstance(other, GridQubit) and
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train | GridQubit.from_proto_dict | Proto dict must have 'row' and 'col' keys. | cirq/devices/grid_qubit.py | def from_proto_dict(proto_dict: Dict) -> 'GridQubit':
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train | ExecutionStrategy.get_operations | Gets the logical operations to apply to qubits. | cirq/contrib/acquaintance/executor.py | def get_operations(self,
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train | GreedyExecutionStrategy.canonicalize_gates | Canonicalizes a set of gates by the qubits they act on.
Takes a set of gates specified by ordered sequences of logical
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order. | cirq/contrib/acquaintance/executor.py | def canonicalize_gates(gates: LogicalGates
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Takes a set of gates specified by ordered sequences of logical
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... | def canonicalize_gates(gates: LogicalGates
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Takes a set of gates specified by ordered sequences of logical
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train | merge_single_qubit_gates_into_phased_x_z | Canonicalizes runs of single-qubit rotations in a circuit.
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Args:
circuit: The circuit to rewrite. This value is mutated in-place.
atol: Absolute tolera... | cirq/optimizers/merge_single_qubit_gates.py | def merge_single_qubit_gates_into_phased_x_z(
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"""Canonicalizes runs of single-qubit rotations in a circuit.
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train | highlight | Wraps the given string with terminal color codes.
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text: The content to highlight.
color_code: The color to highlight with, e.g. 'shelltools.RED'.
bold: Whether to bold the content in addition to coloring.
Returns:
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text: The content to highlight.
color_code: The color to highlight with, e.g. 'shelltools.RED'.
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... | def highlight(text: str, color_code: int, bold: bool=False) -> str:
"""Wraps the given string with terminal color codes.
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text: The content to highlight.
color_code: The color to highlight with, e.g. 'shelltools.RED'.
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out: Where to put the chunks.
Returns:
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async_chunks: An asynchronous source of bytes or str.
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train | _async_wait_for_process | Awaits the creation and completion of an asynchronous process.
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err: Where to write stuff emitted by the process' stderr.
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train | run_cmd | Invokes a subprocess and waits for it to finish.
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out: Where to write the process' stdout. Defaults to sys.stdout. Can be
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train | run_shell | Invokes a shell command and waits for it to finish.
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train | chosen_angle_to_half_turns | Returns a half_turns value based on the given arguments.
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train | chosen_angle_to_canonical_half_turns | Returns a canonicalized half_turns based on the given arguments.
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train | canonicalize_half_turns | Wraps the input into the range (-1, +1]. | cirq/value/angle.py | def canonicalize_half_turns(
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train | single_qubit_matrix_to_pauli_rotations | Implements a single-qubit operation with few rotations.
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train | single_qubit_matrix_to_gates | Implements a single-qubit operation with few gates.
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tolerance: A limit on the amount of error introduced by the
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mat: The 2x2 unitary matrix of the operation to implement.
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train | single_qubit_op_to_framed_phase_form | Decomposes a 2x2 unitary M into U^-1 * diag(1, r) * U * diag(g, g).
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mat: The 2x2 unitary matrix to break down.
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mat: The 2x2 unitary matrix to break down.
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train | single_qubit_matrix_to_phased_x_z | Implements a single-qubit operation with a PhasedX and Z gate.
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mat: The 2x2 unitary matrix of the operation to implement.
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"""Implements a single-qubit operation with a PhasedX and Z gate.
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train | unitary | Returns a unitary matrix describing the given value.
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val: The value to describe with a unitary matrix.
default: Determines the fallback behavior when `val` doesn't have
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default: TDefault = RaiseTypeErrorIfNotProvided
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"""Returns a unitary matrix describing the given value.
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val: The value to describe with a unitary matrix.
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train | has_unitary | Returns whether the value has a unitary matrix representation.
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If `val` has a _has_unitary_ method and its result is not
NotImplemented, that result is returned. Otherwise, if `val` is a
cirq.Gate or cirq.Operation, a decomposition is attempted and the
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If `val` can be decomposed into unitaries, calculate the resulting
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If `val` can be decomposed into unitaries, calculate the resulting
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train | check_collaborator_has_write | References:
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References:
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train | check_auto_merge_labeler | References:
https://developer.github.com/v3/issues/events/#list-events-for-an-issue | dev_tools/auto_merge.py | def check_auto_merge_labeler(repo: GithubRepository, pull_id: int
) -> Optional[CannotAutomergeError]:
"""
References:
https://developer.github.com/v3/issues/events/#list-events-for-an-issue
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/events"
... | def check_auto_merge_labeler(repo: GithubRepository, pull_id: int
) -> Optional[CannotAutomergeError]:
"""
References:
https://developer.github.com/v3/issues/events/#list-events-for-an-issue
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/events"
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train | add_comment | References:
https://developer.github.com/v3/issues/comments/#create-a-comment | dev_tools/auto_merge.py | def add_comment(repo: GithubRepository, pull_id: int, text: str) -> None:
"""
References:
https://developer.github.com/v3/issues/comments/#create-a-comment
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/comments"
"?access_token={}".format(repo.organization,
... | def add_comment(repo: GithubRepository, pull_id: int, text: str) -> None:
"""
References:
https://developer.github.com/v3/issues/comments/#create-a-comment
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/comments"
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train | edit_comment | References:
https://developer.github.com/v3/issues/comments/#edit-a-comment | dev_tools/auto_merge.py | def edit_comment(repo: GithubRepository, text: str, comment_id: int) -> None:
"""
References:
https://developer.github.com/v3/issues/comments/#edit-a-comment
"""
url = ("https://api.github.com/repos/{}/{}/issues/comments/{}"
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... | def edit_comment(repo: GithubRepository, text: str, comment_id: int) -> None:
"""
References:
https://developer.github.com/v3/issues/comments/#edit-a-comment
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url = ("https://api.github.com/repos/{}/{}/issues/comments/{}"
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train | get_branch_details | References:
https://developer.github.com/v3/repos/branches/#get-branch | dev_tools/auto_merge.py | def get_branch_details(repo: GithubRepository, branch: str) -> Any:
"""
References:
https://developer.github.com/v3/repos/branches/#get-branch
"""
url = ("https://api.github.com/repos/{}/{}/branches/{}"
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... | def get_branch_details(repo: GithubRepository, branch: str) -> Any:
"""
References:
https://developer.github.com/v3/repos/branches/#get-branch
"""
url = ("https://api.github.com/repos/{}/{}/branches/{}"
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train | classify_pr_synced_state | References:
https://developer.github.com/v3/pulls/#get-a-single-pull-request
https://developer.github.com/v4/enum/mergestatestatus/ | dev_tools/auto_merge.py | def classify_pr_synced_state(pr: PullRequestDetails) -> Optional[bool]:
"""
References:
https://developer.github.com/v3/pulls/#get-a-single-pull-request
https://developer.github.com/v4/enum/mergestatestatus/
"""
state = pr.payload['mergeable_state'].lower()
classification = {
... | def classify_pr_synced_state(pr: PullRequestDetails) -> Optional[bool]:
"""
References:
https://developer.github.com/v3/pulls/#get-a-single-pull-request
https://developer.github.com/v4/enum/mergestatestatus/
"""
state = pr.payload['mergeable_state'].lower()
classification = {
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train | get_pr_review_status | References:
https://developer.github.com/v3/pulls/reviews/#list-reviews-on-a-pull-request | dev_tools/auto_merge.py | def get_pr_review_status(pr: PullRequestDetails) -> Any:
"""
References:
https://developer.github.com/v3/pulls/reviews/#list-reviews-on-a-pull-request
"""
url = ("https://api.github.com/repos/{}/{}/pulls/{}/reviews"
"?access_token={}".format(pr.repo.organization,
... | def get_pr_review_status(pr: PullRequestDetails) -> Any:
"""
References:
https://developer.github.com/v3/pulls/reviews/#list-reviews-on-a-pull-request
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url = ("https://api.github.com/repos/{}/{}/pulls/{}/reviews"
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train | get_pr_checks | References:
https://developer.github.com/v3/checks/runs/#list-check-runs-for-a-specific-ref | dev_tools/auto_merge.py | def get_pr_checks(pr: PullRequestDetails) -> Dict[str, Any]:
"""
References:
https://developer.github.com/v3/checks/runs/#list-check-runs-for-a-specific-ref
"""
url = ("https://api.github.com/repos/{}/{}/commits/{}/check-runs"
"?access_token={}".format(pr.repo.organization,
... | def get_pr_checks(pr: PullRequestDetails) -> Dict[str, Any]:
"""
References:
https://developer.github.com/v3/checks/runs/#list-check-runs-for-a-specific-ref
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url = ("https://api.github.com/repos/{}/{}/commits/{}/check-runs"
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train | get_repo_ref | References:
https://developer.github.com/v3/git/refs/#get-a-reference | dev_tools/auto_merge.py | def get_repo_ref(repo: GithubRepository, ref: str) -> Dict[str, Any]:
"""
References:
https://developer.github.com/v3/git/refs/#get-a-reference
"""
url = ("https://api.github.com/repos/{}/{}/git/refs/{}"
"?access_token={}".format(repo.organization,
... | def get_repo_ref(repo: GithubRepository, ref: str) -> Dict[str, Any]:
"""
References:
https://developer.github.com/v3/git/refs/#get-a-reference
"""
url = ("https://api.github.com/repos/{}/{}/git/refs/{}"
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train | list_pr_comments | References:
https://developer.github.com/v3/issues/comments/#list-comments-on-an-issue | dev_tools/auto_merge.py | def list_pr_comments(repo: GithubRepository, pull_id: int
) -> List[Dict[str, Any]]:
"""
References:
https://developer.github.com/v3/issues/comments/#list-comments-on-an-issue
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/comments"
"?access_token={}".fo... | def list_pr_comments(repo: GithubRepository, pull_id: int
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"""
References:
https://developer.github.com/v3/issues/comments/#list-comments-on-an-issue
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url = ("https://api.github.com/repos/{}/{}/issues/{}/comments"
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train | delete_comment | References:
https://developer.github.com/v3/issues/comments/#delete-a-comment | dev_tools/auto_merge.py | def delete_comment(repo: GithubRepository, comment_id: int) -> None:
"""
References:
https://developer.github.com/v3/issues/comments/#delete-a-comment
"""
url = ("https://api.github.com/repos/{}/{}/issues/comments/{}"
"?access_token={}".format(repo.organization,
... | def delete_comment(repo: GithubRepository, comment_id: int) -> None:
"""
References:
https://developer.github.com/v3/issues/comments/#delete-a-comment
"""
url = ("https://api.github.com/repos/{}/{}/issues/comments/{}"
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train | attempt_sync_with_master | References:
https://developer.github.com/v3/repos/merging/#perform-a-merge | dev_tools/auto_merge.py | def attempt_sync_with_master(pr: PullRequestDetails
) -> Union[bool, CannotAutomergeError]:
"""
References:
https://developer.github.com/v3/repos/merging/#perform-a-merge
"""
master_sha = get_master_sha(pr.repo)
remote = pr.remote_repo
url = ("https://api.git... | def attempt_sync_with_master(pr: PullRequestDetails
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"""
References:
https://developer.github.com/v3/repos/merging/#perform-a-merge
"""
master_sha = get_master_sha(pr.repo)
remote = pr.remote_repo
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train | attempt_squash_merge | References:
https://developer.github.com/v3/pulls/#merge-a-pull-request-merge-button | dev_tools/auto_merge.py | def attempt_squash_merge(pr: PullRequestDetails
) -> Union[bool, CannotAutomergeError]:
"""
References:
https://developer.github.com/v3/pulls/#merge-a-pull-request-merge-button
"""
url = ("https://api.github.com/repos/{}/{}/pulls/{}/merge"
"?access_token={}".f... | def attempt_squash_merge(pr: PullRequestDetails
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"""
References:
https://developer.github.com/v3/pulls/#merge-a-pull-request-merge-button
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url = ("https://api.github.com/repos/{}/{}/pulls/{}/merge"
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train | auto_delete_pr_branch | References:
https://developer.github.com/v3/git/refs/#delete-a-reference | dev_tools/auto_merge.py | def auto_delete_pr_branch(pr: PullRequestDetails) -> bool:
"""
References:
https://developer.github.com/v3/git/refs/#delete-a-reference
"""
open_pulls = list_open_pull_requests(pr.repo, base_branch=pr.branch_name)
if any(open_pulls):
log('Not deleting branch {!r}. It is used elsewhe... | def auto_delete_pr_branch(pr: PullRequestDetails) -> bool:
"""
References:
https://developer.github.com/v3/git/refs/#delete-a-reference
"""
open_pulls = list_open_pull_requests(pr.repo, base_branch=pr.branch_name)
if any(open_pulls):
log('Not deleting branch {!r}. It is used elsewhe... | [
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train | add_labels_to_pr | References:
https://developer.github.com/v3/issues/labels/#add-labels-to-an-issue | dev_tools/auto_merge.py | def add_labels_to_pr(repo: GithubRepository,
pull_id: int,
*labels: str,
override_token: str = None) -> None:
"""
References:
https://developer.github.com/v3/issues/labels/#add-labels-to-an-issue
"""
url = ("https://api.github.com/re... | def add_labels_to_pr(repo: GithubRepository,
pull_id: int,
*labels: str,
override_token: str = None) -> None:
"""
References:
https://developer.github.com/v3/issues/labels/#add-labels-to-an-issue
"""
url = ("https://api.github.com/re... | [
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train | remove_label_from_pr | References:
https://developer.github.com/v3/issues/labels/#remove-a-label-from-an-issue | dev_tools/auto_merge.py | def remove_label_from_pr(repo: GithubRepository,
pull_id: int,
label: str) -> bool:
"""
References:
https://developer.github.com/v3/issues/labels/#remove-a-label-from-an-issue
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/labels/{}"
... | def remove_label_from_pr(repo: GithubRepository,
pull_id: int,
label: str) -> bool:
"""
References:
https://developer.github.com/v3/issues/labels/#remove-a-label-from-an-issue
"""
url = ("https://api.github.com/repos/{}/{}/issues/{}/labels/{}"
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