INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Calculate the purity of a quantum state. | def purity(state):
"""Calculate the purity of a quantum state.
Args:
state (ndarray): a quantum state
Returns:
float: purity.
"""
rho = np.array(state)
if rho.ndim == 1:
return 1.0
return np.real(np.trace(rho.dot(rho))) |
Return the corresponding OPENQASM string. | def qasm(self, prec=15):
"""Return the corresponding OPENQASM string."""
string = "gate " + self.name
if self.arguments is not None:
string += "(" + self.arguments.qasm(prec) + ")"
string += " " + self.bitlist.qasm(prec) + "\n"
string += "{\n" + self.body.qasm(prec) +... |
Run the pass on the DAG and write the discovered commutation relations into the property_set. | def run(self, dag):
"""
Run the pass on the DAG, and write the discovered commutation relations
into the property_set.
"""
# Initiate the commutation set
self.property_set['commutation_set'] = defaultdict(list)
# Build a dictionary to keep track of the gates on e... |
Creates a backend widget. | def backend_widget(backend):
"""Creates a backend widget.
"""
config = backend.configuration().to_dict()
props = backend.properties().to_dict()
name = widgets.HTML(value="<h4>{name}</h4>".format(name=backend.name()),
layout=widgets.Layout())
n_qubits = config['n_qubits'... |
Updates the monitor info Called from another thread. | def update_backend_info(self, interval=60):
"""Updates the monitor info
Called from another thread.
"""
my_thread = threading.currentThread()
current_interval = 0
started = False
all_dead = False
stati = [None]*len(self._backends)
while getattr(my_thread, "do_run", True) and not all_... |
Generates a jobs_pending progress bar widget. | def generate_jobs_pending_widget():
"""Generates a jobs_pending progress bar widget.
"""
pbar = widgets.IntProgress(
value=0,
min=0,
max=50,
description='',
orientation='horizontal', layout=widgets.Layout(max_width='180px'))
pbar.style.bar_color = '#71cddd'
p... |
Flips the cx nodes to match the directed coupling map. Args: dag ( DAGCircuit ): DAG to map. Returns: DAGCircuit: The rearranged dag for the coupling map | def run(self, dag):
"""
Flips the cx nodes to match the directed coupling map.
Args:
dag (DAGCircuit): DAG to map.
Returns:
DAGCircuit: The rearranged dag for the coupling map
Raises:
TranspilerError: If the circuit cannot be mapped just by fl... |
Run one pass of cx cancellation on the circuit | def run(self, dag):
"""
Run one pass of cx cancellation on the circuit
Args:
dag (DAGCircuit): the directed acyclic graph to run on.
Returns:
DAGCircuit: Transformed DAG.
"""
cx_runs = dag.collect_runs(["cx"])
for cx_run in cx_runs:
... |
Return a single backend matching the specified filtering. | def get_backend(self, name=None, **kwargs):
"""Return a single backend matching the specified filtering.
Args:
name (str): name of the backend.
**kwargs (dict): dict used for filtering.
Returns:
BaseBackend: a backend matching the filtering.
Raises:... |
Return the shape for bipartite matrix | def _bipartite_shape(self):
"""Return the shape for bipartite matrix"""
return (self._input_dim, self._output_dim, self._input_dim,
self._output_dim) |
Return the conjugate of the QuantumChannel. | def conjugate(self):
"""Return the conjugate of the QuantumChannel."""
return Choi(np.conj(self._data), self.input_dims(), self.output_dims()) |
Return the transpose of the QuantumChannel. | def transpose(self):
"""Return the transpose of the QuantumChannel."""
# Make bipartite matrix
d_in, d_out = self.dim
data = np.reshape(self._data, (d_in, d_out, d_in, d_out))
# Swap input and output indicies on bipartite matrix
data = np.transpose(data, (1, 0, 3, 2))
... |
Return the composition channel self∘other. | def compose(self, other, qargs=None, front=False):
"""Return the composition channel self∘other.
Args:
other (QuantumChannel): a quantum channel.
qargs (list): a list of subsystem positions to compose other on.
front (bool): If False compose in standard order other(s... |
The matrix power of the channel. | def power(self, n):
"""The matrix power of the channel.
Args:
n (int): compute the matrix power of the superoperator matrix.
Returns:
Choi: the matrix power of the SuperOp converted to a Choi channel.
Raises:
QiskitError: if the input and output dim... |
Evolve a quantum state by the QuantumChannel. | def _evolve(self, state, qargs=None):
"""Evolve a quantum state by the QuantumChannel.
Args:
state (QuantumState): The input statevector or density matrix.
qargs (list): a list of QuantumState subsystem positions to apply
the operator on.
Retu... |
Return the tensor product channel. | def _tensor_product(self, other, reverse=False):
"""Return the tensor product channel.
Args:
other (QuantumChannel): a quantum channel.
reverse (bool): If False return self ⊗ other, if True return
if True return (other ⊗ self) [Default: False
... |
Get the number and size of unique registers from bit_labels list. | def _get_register_specs(bit_labels):
"""Get the number and size of unique registers from bit_labels list.
Args:
bit_labels (list): this list is of the form::
[['reg1', 0], ['reg1', 1], ['reg2', 0]]
which indicates a register named "reg1" of size 2
and a register na... |
Truncate long floats | def _truncate_float(matchobj, format_str='0.2g'):
"""Truncate long floats
Args:
matchobj (re.Match): contains original float
format_str (str): format specifier
Returns:
str: returns truncated float
"""
if matchobj.group(0):
return format(float(matchobj.group(0)), form... |
Return LaTeX string representation of circuit. | def latex(self, aliases=None):
"""Return LaTeX string representation of circuit.
This method uses the LaTeX Qconfig package to create a graphical
representation of the circuit.
Returns:
string: for writing to a LaTeX file.
"""
self._initialize_latex_array(al... |
Get depth information for the circuit. | def _get_image_depth(self):
"""Get depth information for the circuit.
Returns:
int: number of columns in the circuit
int: total size of columns in the circuit
"""
max_column_widths = []
for layer in self.ops:
# store the max width for the l... |
Get height width & scale attributes for the beamer page. | def _get_beamer_page(self):
"""Get height, width & scale attributes for the beamer page.
Returns:
tuple: (height, width, scale) desirable page attributes
"""
# PIL python package limits image size to around a quarter gigabyte
# this means the beamer image should be l... |
Returns an array of strings containing \\ LaTeX for this circuit. | def _build_latex_array(self, aliases=None):
"""Returns an array of strings containing \\LaTeX for this circuit.
If aliases is not None, aliases contains a dict mapping
the current qubits in the circuit to new qubit names.
We will deduce the register names and sizes from aliases.
... |
Get the index number for a quantum bit Args: qubit ( tuple ): The tuple of the bit of the form ( register_name bit_number ) Returns: int: The index in the bit list Raises: VisualizationError: If the bit isn t found | def _get_qubit_index(self, qubit):
"""Get the index number for a quantum bit
Args:
qubit (tuple): The tuple of the bit of the form
(register_name, bit_number)
Returns:
int: The index in the bit list
Raises:
VisualizationError: If the bi... |
Loads the QObj schema for use in future validations. | def _load_schema(file_path, name=None):
"""Loads the QObj schema for use in future validations.
Caches schema in _SCHEMAS module attribute.
Args:
file_path(str): Path to schema.
name(str): Given name for schema. Defaults to file_path filename
without schema.
Return:
sc... |
Generate validator for JSON schema. | def _get_validator(name, schema=None, check_schema=True,
validator_class=None, **validator_kwargs):
"""Generate validator for JSON schema.
Args:
name (str): Name for validator. Will be validator key in
`_VALIDATORS` dict.
schema (dict): JSON schema `dict`. If not ... |
Load all default schemas into _SCHEMAS. | def _load_schemas_and_validators():
"""Load all default schemas into `_SCHEMAS`."""
schema_base_path = os.path.join(os.path.dirname(__file__), '../..')
for name, path in _DEFAULT_SCHEMA_PATHS.items():
_load_schema(os.path.join(schema_base_path, path), name)
_get_validator(name) |
Validates JSON dict against a schema. | def validate_json_against_schema(json_dict, schema,
err_msg=None):
"""Validates JSON dict against a schema.
Args:
json_dict (dict): JSON to be validated.
schema (dict or str): JSON schema dictionary or the name of one of the
standards schemas in Qisk... |
Return a cascading explanation of the validation error. | def _format_causes(err, level=0):
"""Return a cascading explanation of the validation error.
Returns a cascading explanation of the validation error in the form of::
<validator> failed @ <subfield_path> because of:
<validator> failed @ <subfield_path> because of:
...
... |
Return the corresponding OPENQASM string. | def qasm(self, prec=15):
"""Return the corresponding OPENQASM string."""
return ",".join([self.children[j].qasm(prec)
for j in range(self.size())]) |
Majority gate. | def majority(p, a, b, c):
"""Majority gate."""
p.cx(c, b)
p.cx(c, a)
p.ccx(a, b, c) |
Unmajority gate. | def unmajority(p, a, b, c):
"""Unmajority gate."""
p.ccx(a, b, c)
p.cx(c, a)
p.cx(a, b) |
Returns a dict of DAGNode: Barrier objects where the barrier needs to be inserted where the corresponding DAGNode appears in the main DAG | def _collect_potential_merges(dag, barriers):
"""
Returns a dict of DAGNode : Barrier objects, where the barrier needs to be
inserted where the corresponding DAGNode appears in the main DAG
"""
# if only got 1 or 0 barriers then can't merge
if len(barriers) < 2:
... |
Draw a quantum circuit to different formats ( set by output parameter ): 0. text: ASCII art TextDrawing that can be printed in the console. 1. latex: high - quality images but heavy external software dependencies 2. matplotlib: purely in Python with no external dependencies | def circuit_drawer(circuit,
scale=0.7,
filename=None,
style=None,
output='text',
interactive=False,
line_length=None,
plot_barriers=True,
reverse_bits=False,
... |
Draws a circuit using ascii art. Args: circuit ( QuantumCircuit ): Input circuit filename ( str ): optional filename to write the result line_length ( int ): Optional. Breaks the circuit drawing to this length. This useful when the drawing does not fit in the console. If None ( default ) it will try to guess the consol... | def _text_circuit_drawer(circuit, filename=None, line_length=None, reverse_bits=False,
plotbarriers=True, justify=None, vertically_compressed=True):
"""
Draws a circuit using ascii art.
Args:
circuit (QuantumCircuit): Input circuit
filename (str): optional filename t... |
Draw a quantum circuit based on latex ( Qcircuit package ) | def _latex_circuit_drawer(circuit,
scale=0.7,
filename=None,
style=None,
plot_barriers=True,
reverse_bits=False,
justify=None):
"""Draw a quantum circuit based ... |
Convert QuantumCircuit to LaTeX string. | def _generate_latex_source(circuit, filename=None,
scale=0.7, style=None, reverse_bits=False,
plot_barriers=True, justify=None):
"""Convert QuantumCircuit to LaTeX string.
Args:
circuit (QuantumCircuit): input circuit
scale (float): image sc... |
Draw a quantum circuit based on matplotlib. If %matplotlib inline is invoked in a Jupyter notebook it visualizes a circuit inline. We recommend %config InlineBackend. figure_format = svg for the inline visualization. | def _matplotlib_circuit_drawer(circuit,
scale=0.7,
filename=None,
style=None,
plot_barriers=True,
reverse_bits=False,
justify=None):
... |
Return a random quantum state from the uniform ( Haar ) measure on state space. | def random_state(dim, seed=None):
"""
Return a random quantum state from the uniform (Haar) measure on
state space.
Args:
dim (int): the dim of the state spaxe
seed (int): Optional. To set a random seed.
Returns:
ndarray: state(2**num) a random quantum state.
"""
i... |
Return a random dim x dim unitary Operator from the Haar measure. | def random_unitary(dim, seed=None):
"""
Return a random dim x dim unitary Operator from the Haar measure.
Args:
dim (int): the dim of the state space.
seed (int): Optional. To set a random seed.
Returns:
Operator: (dim, dim) unitary operator.
Raises:
QiskitError: i... |
Generate a random density matrix rho. | def random_density_matrix(length, rank=None, method='Hilbert-Schmidt', seed=None):
"""
Generate a random density matrix rho.
Args:
length (int): the length of the density matrix.
rank (int or None): the rank of the density matrix. The default
value is full-rank.
method (... |
Return a normally distributed complex random matrix. | def __ginibre_matrix(nrow, ncol=None, seed=None):
"""
Return a normally distributed complex random matrix.
Args:
nrow (int): number of rows in output matrix.
ncol (int): number of columns in output matrix.
seed (int): Optional. To set a random seed.
Returns:
ndarray: A c... |
Generate a random density matrix from the Hilbert - Schmidt metric. | def __random_density_hs(N, rank=None, seed=None):
"""
Generate a random density matrix from the Hilbert-Schmidt metric.
Args:
N (int): the length of the density matrix.
rank (int or None): the rank of the density matrix. The default
value is full-rank.
seed (int): Option... |
Generate a random density matrix from the Bures metric. | def __random_density_bures(N, rank=None, seed=None):
"""
Generate a random density matrix from the Bures metric.
Args:
N (int): the length of the density matrix.
rank (int or None): the rank of the density matrix. The default
value is full-rank.
seed (int): Optional. To ... |
Return the corresponding OPENQASM string. | def qasm(self, prec=15):
"""Return the corresponding OPENQASM string."""
string = ""
for children in self.children:
string += " " + children.qasm(prec) + "\n"
return string |
Return a list of custom gate names in this gate body. | def calls(self):
"""Return a list of custom gate names in this gate body."""
lst = []
for children in self.children:
if children.type == "custom_unitary":
lst.append(children.name)
return lst |
Return the corresponding OPENQASM string. | def qasm(self, prec=15):
"""Return the corresponding OPENQASM string."""
if self.value == pi:
return "pi"
return ccode(self.value, precision=prec) |
gate sdg a { u1 ( - pi/ 2 ) a ; } | def _define(self):
"""
gate sdg a { u1(-pi/2) a; }
"""
definition = []
q = QuantumRegister(1, "q")
rule = [
(U1Gate(-pi/2), [q[0]], [])
]
for inst in rule:
definition.append(inst)
self.definition = definition |
Return the composition channel self∘other. | def compose(self, other, qargs=None, front=False):
"""Return the composition channel self∘other.
Args:
other (QuantumChannel): a quantum channel.
qargs (list): a list of subsystem positions to compose other on.
front (bool): If False compose in standard order other(s... |
The matrix power of the channel. | def power(self, n):
"""The matrix power of the channel.
Args:
n (int): compute the matrix power of the superoperator matrix.
Returns:
Chi: the matrix power of the SuperOp converted to a Chi channel.
Raises:
QiskitError: if the input and output dimen... |
Return the QuantumChannel self + other. | def add(self, other):
"""Return the QuantumChannel self + other.
Args:
other (QuantumChannel): a quantum channel.
Returns:
Chi: the linear addition self + other as a Chi object.
Raises:
QiskitError: if other is not a QuantumChannel subclass, or
... |
Return the QuantumChannel self + other. | def multiply(self, other):
"""Return the QuantumChannel self + other.
Args:
other (complex): a complex number.
Returns:
Chi: the scalar multiplication other * self as a Chi object.
Raises:
QiskitError: if other is not a valid scalar.
"""
... |
Return the tensor product channel. | def _tensor_product(self, other, reverse=False):
"""Return the tensor product channel.
Args:
other (QuantumChannel): a quantum channel.
reverse (bool): If False return self ⊗ other, if True return
if True return (other ⊗ self) [Default: False
... |
Return the conjugate of the QuantumChannel. | def conjugate(self):
"""Return the conjugate of the QuantumChannel."""
return SuperOp(
np.conj(self._data), self.input_dims(), self.output_dims()) |
Return the transpose of the QuantumChannel. | def transpose(self):
"""Return the transpose of the QuantumChannel."""
return SuperOp(
np.transpose(self._data),
input_dims=self.output_dims(),
output_dims=self.input_dims()) |
Return the composition channel self∘other. | def compose(self, other, qargs=None, front=False):
"""Return the composition channel self∘other.
Args:
other (QuantumChannel): a quantum channel.
qargs (list): a list of subsystem positions to compose other on.
front (bool): If False compose in standard order other(s... |
Return the compose of a QuantumChannel with itself n times. | def power(self, n):
"""Return the compose of a QuantumChannel with itself n times.
Args:
n (int): compute the matrix power of the superoperator matrix.
Returns:
SuperOp: the n-times composition channel as a SuperOp object.
Raises:
QiskitError: if th... |
Return the QuantumChannel self + other. | def add(self, other):
"""Return the QuantumChannel self + other.
Args:
other (QuantumChannel): a quantum channel.
Returns:
SuperOp: the linear addition self + other as a SuperOp object.
Raises:
QiskitError: if other cannot be converted to a channel ... |
Return the QuantumChannel self + other. | def multiply(self, other):
"""Return the QuantumChannel self + other.
Args:
other (complex): a complex number.
Returns:
SuperOp: the scalar multiplication other * self as a SuperOp object.
Raises:
QiskitError: if other is not a valid scalar.
... |
Evolve a quantum state by the QuantumChannel. | def _evolve(self, state, qargs=None):
"""Evolve a quantum state by the QuantumChannel.
Args:
state (QuantumState): The input statevector or density matrix.
qargs (list): a list of QuantumState subsystem positions to apply
the operator on.
Retu... |
Return the composition channel. | def _compose_subsystem(self, other, qargs, front=False):
"""Return the composition channel."""
# Compute tensor contraction indices from qargs
input_dims = list(self.input_dims())
output_dims = list(self.output_dims())
if front:
num_indices = len(self.input_dims())
... |
Evolve a quantum state by the operator. | def _evolve_subsystem(self, state, qargs):
"""Evolve a quantum state by the operator.
Args:
state (QuantumState): The input statevector or density matrix.
qargs (list): a list of QuantumState subsystem positions to apply
the operator on.
Retur... |
Convert a QuantumCircuit or Instruction to a SuperOp. | def _instruction_to_superop(cls, instruction):
"""Convert a QuantumCircuit or Instruction to a SuperOp."""
# Convert circuit to an instruction
if isinstance(instruction, QuantumCircuit):
instruction = instruction.to_instruction()
# Initialize an identity superoperator of the ... |
Update the current Operator by apply an instruction. | def _append_instruction(self, obj, qargs=None):
"""Update the current Operator by apply an instruction."""
if isinstance(obj, Instruction):
chan = None
if obj.name == 'reset':
# For superoperator evolution we can simulate a reset as
# a non-unitary... |
Return a circuit with a barrier before last measurements. | def run(self, dag):
"""Return a circuit with a barrier before last measurements."""
# Collect DAG nodes which are followed only by barriers or other measures.
final_op_types = ['measure', 'barrier']
final_ops = []
for candidate_node in dag.named_nodes(*final_op_types):
... |
Convert a list of circuits into a qobj. | def circuits_to_qobj(circuits, qobj_header=None,
qobj_id=None, backend_name=None,
config=None, shots=None, max_credits=None,
basis_gates=None,
coupling_map=None, seed=None, memory=None):
"""Convert a list of circuits into a qobj.
... |
Expand 3 + qubit gates using their decomposition rules. | def run(self, dag):
"""Expand 3+ qubit gates using their decomposition rules.
Args:
dag(DAGCircuit): input dag
Returns:
DAGCircuit: output dag with maximum node degrees of 2
Raises:
QiskitError: if a 3q+ gate is not decomposable
"""
fo... |
Expand a given gate into its decomposition. | def run(self, dag):
"""Expand a given gate into its decomposition.
Args:
dag(DAGCircuit): input dag
Returns:
DAGCircuit: output dag where gate was expanded.
"""
# Walk through the DAG and expand each non-basis node
for node in dag.op_nodes(self.ga... |
Apply u2 to q. | def unitary(self, obj, qubits, label=None):
"""Apply u2 to q."""
if isinstance(qubits, QuantumRegister):
qubits = qubits[:]
return self.append(UnitaryGate(obj, label=label), qubits, []) |
Calculate a subcircuit that implements this unitary. | def _define(self):
"""Calculate a subcircuit that implements this unitary."""
if self.num_qubits == 1:
q = QuantumRegister(1, "q")
angles = euler_angles_1q(self.to_matrix())
self.definition = [(U3Gate(*angles), [q[0]], [])]
if self.num_qubits == 2:
... |
Validate if the value is of the type of the schema s model. | def check_type(self, value, attr, data):
"""Validate if the value is of the type of the schema's model.
Assumes the nested schema is a ``BaseSchema``.
"""
if self.many and not is_collection(value):
raise self._not_expected_type(
value, Iterable, fields=[self]... |
Validate if it s a list of valid item - field values. | def check_type(self, value, attr, data):
"""Validate if it's a list of valid item-field values.
Check if each element in the list can be validated by the item-field
passed during construction.
"""
super().check_type(value, attr, data)
errors = []
for idx, v in e... |
Plot the directed acyclic graph ( dag ) to represent operation dependencies in a quantum circuit. | def dag_drawer(dag, scale=0.7, filename=None, style='color'):
"""Plot the directed acyclic graph (dag) to represent operation dependencies
in a quantum circuit.
Note this function leverages
`pydot <https://github.com/erocarrera/pydot>`_ (via
`nxpd <https://github.com/chebee7i/nxpd`_) to generate th... |
Set the absolute tolerence parameter for float comparisons. | def _atol(self, atol):
"""Set the absolute tolerence parameter for float comparisons."""
# NOTE: that this overrides the class value so applies to all
# instances of the class.
max_tol = self.__class__.MAX_TOL
if atol < 0:
raise QiskitError("Invalid atol: must be non-... |
Set the relative tolerence parameter for float comparisons. | def _rtol(self, rtol):
"""Set the relative tolerence parameter for float comparisons."""
# NOTE: that this overrides the class value so applies to all
# instances of the class.
max_tol = self.__class__.MAX_TOL
if rtol < 0:
raise QiskitError("Invalid rtol: must be non-... |
Reshape input and output dimensions of operator. | def _reshape(self, input_dims=None, output_dims=None):
"""Reshape input and output dimensions of operator.
Arg:
input_dims (tuple): new subsystem input dimensions.
output_dims (tuple): new subsystem output dimensions.
Returns:
Operator: returns self with res... |
Return tuple of input dimension for specified subsystems. | def input_dims(self, qargs=None):
"""Return tuple of input dimension for specified subsystems."""
if qargs is None:
return self._input_dims
return tuple(self._input_dims[i] for i in qargs) |
Return tuple of output dimension for specified subsystems. | def output_dims(self, qargs=None):
"""Return tuple of output dimension for specified subsystems."""
if qargs is None:
return self._output_dims
return tuple(self._output_dims[i] for i in qargs) |
Make a copy of current operator. | def copy(self):
"""Make a copy of current operator."""
# pylint: disable=no-value-for-parameter
# The constructor of subclasses from raw data should be a copy
return self.__class__(self.data, self.input_dims(), self.output_dims()) |
Return the compose of a operator with itself n times. | def power(self, n):
"""Return the compose of a operator with itself n times.
Args:
n (int): the number of times to compose with self (n>0).
Returns:
BaseOperator: the n-times composed operator.
Raises:
QiskitError: if the input and output dimensions... |
Check if input dimension corresponds to qubit subsystems. | def _automatic_dims(cls, dims, size):
"""Check if input dimension corresponds to qubit subsystems."""
if dims is None:
dims = size
elif np.product(dims) != size:
raise QiskitError("dimensions do not match size.")
if isinstance(dims, (int, np.integer)):
... |
Perform a contraction using Numpy. einsum | def _einsum_matmul(cls, tensor, mat, indices, shift=0, right_mul=False):
"""Perform a contraction using Numpy.einsum
Args:
tensor (np.array): a vector or matrix reshaped to a rank-N tensor.
mat (np.array): a matrix reshaped to a rank-2M tensor.
indices (list): tensor... |
Override _deserialize for customizing the exception raised. | def _deserialize(self, value, attr, data):
"""Override ``_deserialize`` for customizing the exception raised."""
try:
return super()._deserialize(value, attr, data)
except ValidationError as ex:
if 'deserialization_schema_selector' in ex.messages[0]:
ex.me... |
Override _serialize for customizing the exception raised. | def _serialize(self, value, key, obj):
"""Override ``_serialize`` for customizing the exception raised."""
try:
return super()._serialize(value, key, obj)
except TypeError as ex:
if 'serialization_schema_selector' in str(ex):
raise ValidationError('Data fr... |
Check if at least one of the possible choices validates the value. | def check_type(self, value, attr, data):
"""Check if at least one of the possible choices validates the value.
Possible choices are assumed to be ``ModelTypeValidator`` fields.
"""
for field in self.choices:
if isinstance(field, ModelTypeValidator):
try:
... |
Return the state fidelity between two quantum states. | def state_fidelity(state1, state2):
"""Return the state fidelity between two quantum states.
Either input may be a state vector, or a density matrix. The state
fidelity (F) for two density matrices is defined as::
F(rho1, rho2) = Tr[sqrt(sqrt(rho1).rho2.sqrt(rho1))] ^ 2
For a pure state and m... |
Apply real scalar function to singular values of a matrix. | def _funm_svd(a, func):
"""Apply real scalar function to singular values of a matrix.
Args:
a (array_like): (N, N) Matrix at which to evaluate the function.
func (callable): Callable object that evaluates a scalar function f.
Returns:
ndarray: funm (N, N) Value of the matrix functi... |
If dag is mapped to coupling_map the property is_swap_mapped is set to True ( or to False otherwise ). | def run(self, dag):
"""
If `dag` is mapped to `coupling_map`, the property
`is_swap_mapped` is set to True (or to False otherwise).
Args:
dag (DAGCircuit): DAG to map.
"""
if self.layout is None:
if self.property_set["layout"]:
sel... |
Take a statevector snapshot of the internal simulator representation. Works on all qubits and prevents reordering ( like barrier ). | def snapshot(self,
label,
snapshot_type='statevector',
qubits=None,
params=None):
"""Take a statevector snapshot of the internal simulator representation.
Works on all qubits, and prevents reordering (like barrier).
For other types of snapshots use the Sn... |
Assemble a QasmQobjInstruction | def assemble(self):
"""Assemble a QasmQobjInstruction"""
instruction = super().assemble()
instruction.label = self._label
instruction.snapshot_type = self._snapshot_type
return instruction |
Special case. Return self. | def inverse(self):
"""Special case. Return self."""
return Snapshot(self.num_qubits, self.num_clbits, self.params[0],
self.params[1]) |
Set snapshot label to name | def label(self, name):
"""Set snapshot label to name
Args:
name (str or None): label to assign unitary
Raises:
TypeError: name is not string or None.
"""
if isinstance(name, str):
self._label = name
else:
raise TypeError('... |
Return True if completely - positive trace - preserving ( CPTP ). | def is_cptp(self, atol=None, rtol=None):
"""Return True if completely-positive trace-preserving (CPTP)."""
choi = _to_choi(self.rep, self._data, *self.dim)
return self._is_cp_helper(choi, atol, rtol) and self._is_tp_helper(
choi, atol, rtol) |
Test if a channel is completely - positive ( CP ) | def is_tp(self, atol=None, rtol=None):
"""Test if a channel is completely-positive (CP)"""
choi = _to_choi(self.rep, self._data, *self.dim)
return self._is_tp_helper(choi, atol, rtol) |
Test if Choi - matrix is completely - positive ( CP ) | def is_cp(self, atol=None, rtol=None):
"""Test if Choi-matrix is completely-positive (CP)"""
choi = _to_choi(self.rep, self._data, *self.dim)
return self._is_cp_helper(choi, atol, rtol) |
Return True if QuantumChannel is a unitary channel. | def is_unitary(self, atol=None, rtol=None):
"""Return True if QuantumChannel is a unitary channel."""
try:
op = self.to_operator()
return op.is_unitary(atol=atol, rtol=rtol)
except QiskitError:
return False |
Try to convert channel to a unitary representation Operator. | def to_operator(self):
"""Try to convert channel to a unitary representation Operator."""
mat = _to_operator(self.rep, self._data, *self.dim)
return Operator(mat, self.input_dims(), self.output_dims()) |
Convert to a Kraus or UnitaryGate circuit instruction. | def to_instruction(self):
"""Convert to a Kraus or UnitaryGate circuit instruction.
If the channel is unitary it will be added as a unitary gate,
otherwise it will be added as a kraus simulator instruction.
Returns:
Instruction: A kraus instruction for the channel.
... |
Test if a channel is completely - positive ( CP ) | def _is_cp_helper(self, choi, atol, rtol):
"""Test if a channel is completely-positive (CP)"""
if atol is None:
atol = self._atol
if rtol is None:
rtol = self._rtol
return is_positive_semidefinite_matrix(choi, rtol=rtol, atol=atol) |
Test if Choi - matrix is trace - preserving ( TP ) | def _is_tp_helper(self, choi, atol, rtol):
"""Test if Choi-matrix is trace-preserving (TP)"""
if atol is None:
atol = self._atol
if rtol is None:
rtol = self._rtol
# Check if the partial trace is the identity matrix
d_in, d_out = self.dim
mat = np.... |
Format input state so it is statevector or density matrix | def _format_state(self, state, density_matrix=False):
"""Format input state so it is statevector or density matrix"""
state = np.array(state)
shape = state.shape
ndim = state.ndim
if ndim > 2:
raise QiskitError('Input state is not a vector or matrix.')
# Flatt... |
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