INSTRUCTION
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Add registers.
|
def add_register(self, *regs):
"""Add registers."""
if not regs:
return
if any([isinstance(reg, int) for reg in regs]):
# QuantumCircuit defined without registers
if len(regs) == 1 and isinstance(regs[0], int):
# QuantumCircuit with anonymous quantum wires e.g. QuantumCircuit(2)
regs = (QuantumRegister(regs[0], 'q'),)
elif len(regs) == 2 and all([isinstance(reg, int) for reg in regs]):
# QuantumCircuit with anonymous wires e.g. QuantumCircuit(2, 3)
regs = (QuantumRegister(regs[0], 'q'), ClassicalRegister(regs[1], 'c'))
else:
raise QiskitError("QuantumCircuit parameters can be Registers or Integers."
" If Integers, up to 2 arguments. QuantumCircuit was called"
" with %s." % (regs,))
for register in regs:
if register in self.qregs or register in self.cregs:
raise QiskitError("register name \"%s\" already exists"
% register.name)
if isinstance(register, QuantumRegister):
self.qregs.append(register)
elif isinstance(register, ClassicalRegister):
self.cregs.append(register)
else:
raise QiskitError("expected a register")
|
Raise exception if list of qubits contains duplicates.
|
def _check_dups(self, qubits):
"""Raise exception if list of qubits contains duplicates."""
squbits = set(qubits)
if len(squbits) != len(qubits):
raise QiskitError("duplicate qubit arguments")
|
Raise exception if a qarg is not in this circuit or bad format.
|
def _check_qargs(self, qargs):
"""Raise exception if a qarg is not in this circuit or bad format."""
if not all(isinstance(i, tuple) and
isinstance(i[0], QuantumRegister) and
isinstance(i[1], int) for i in qargs):
raise QiskitError("qarg not (QuantumRegister, int) tuple")
if not all(self.has_register(i[0]) for i in qargs):
raise QiskitError("register not in this circuit")
for qubit in qargs:
qubit[0].check_range(qubit[1])
|
Raise exception if clbit is not in this circuit or bad format.
|
def _check_cargs(self, cargs):
"""Raise exception if clbit is not in this circuit or bad format."""
if not all(isinstance(i, tuple) and
isinstance(i[0], ClassicalRegister) and
isinstance(i[1], int) for i in cargs):
raise QiskitError("carg not (ClassicalRegister, int) tuple")
if not all(self.has_register(i[0]) for i in cargs):
raise QiskitError("register not in this circuit")
for clbit in cargs:
clbit[0].check_range(clbit[1])
|
Call a decomposition pass on this circuit to decompose one level ( shallow decompose ).
|
def decompose(self):
"""Call a decomposition pass on this circuit,
to decompose one level (shallow decompose).
Returns:
QuantumCircuit: a circuit one level decomposed
"""
from qiskit.transpiler.passes.decompose import Decompose
from qiskit.converters.circuit_to_dag import circuit_to_dag
from qiskit.converters.dag_to_circuit import dag_to_circuit
pass_ = Decompose()
decomposed_dag = pass_.run(circuit_to_dag(self))
return dag_to_circuit(decomposed_dag)
|
Raise exception if the circuits are defined on incompatible registers
|
def _check_compatible_regs(self, rhs):
"""Raise exception if the circuits are defined on incompatible registers"""
list1 = self.qregs + self.cregs
list2 = rhs.qregs + rhs.cregs
for element1 in list1:
for element2 in list2:
if element2.name == element1.name:
if element1 != element2:
raise QiskitError("circuits are not compatible")
|
Return OpenQASM string.
|
def qasm(self):
"""Return OpenQASM string."""
string_temp = self.header + "\n"
string_temp += self.extension_lib + "\n"
for register in self.qregs:
string_temp += register.qasm() + "\n"
for register in self.cregs:
string_temp += register.qasm() + "\n"
for instruction, qargs, cargs in self.data:
if instruction.name == 'measure':
qubit = qargs[0]
clbit = cargs[0]
string_temp += "%s %s[%d] -> %s[%d];\n" % (instruction.qasm(),
qubit[0].name, qubit[1],
clbit[0].name, clbit[1])
else:
string_temp += "%s %s;\n" % (instruction.qasm(),
",".join(["%s[%d]" % (j[0].name, j[1])
for j in qargs + cargs]))
return string_temp
|
Draw the quantum circuit
|
def draw(self, scale=0.7, filename=None, style=None, output='text',
interactive=False, line_length=None, plot_barriers=True,
reverse_bits=False, justify=None):
"""Draw the quantum circuit
Using the output parameter you can specify the format. The choices are:
0. text: ASCII art string
1. latex: high-quality images, but heavy external software dependencies
2. matplotlib: purely in Python with no external dependencies
Defaults to an overcomplete basis, in order to not alter gates.
Args:
scale (float): scale of image to draw (shrink if < 1)
filename (str): file path to save image to
style (dict or str): dictionary of style or file name of style
file. You can refer to the
:ref:`Style Dict Doc <style-dict-doc>` for more information
on the contents.
output (str): Select the output method to use for drawing the
circuit. Valid choices are `text`, `latex`, `latex_source`,
`mpl`.
interactive (bool): when set true show the circuit in a new window
(for `mpl` this depends on the matplotlib backend being used
supporting this). Note when used with either the `text` or the
`latex_source` output type this has no effect and will be
silently ignored.
line_length (int): sets the length of the lines generated by `text`
reverse_bits (bool): When set to True reverse the bit order inside
registers for the output visualization.
plot_barriers (bool): Enable/disable drawing barriers in the output
circuit. Defaults to True.
justify (string): Options are `left`, `right` or `none`, if anything
else is supplied it defaults to left justified. It refers to where
gates should be placed in the output circuit if there is an option.
`none` results in each gate being placed in its own column. Currently
only supported by text drawer.
Returns:
PIL.Image or matplotlib.figure or str or TextDrawing:
* PIL.Image: (output `latex`) an in-memory representation of the
image of the circuit diagram.
* matplotlib.figure: (output `mpl`) a matplotlib figure object
for the circuit diagram.
* str: (output `latex_source`). The LaTeX source code.
* TextDrawing: (output `text`). A drawing that can be printed as
ascii art
Raises:
VisualizationError: when an invalid output method is selected
"""
from qiskit.tools import visualization
return visualization.circuit_drawer(self, scale=scale,
filename=filename, style=style,
output=output,
interactive=interactive,
line_length=line_length,
plot_barriers=plot_barriers,
reverse_bits=reverse_bits,
justify=justify)
|
Returns total number of gate operations in circuit.
|
def size(self):
"""Returns total number of gate operations in circuit.
Returns:
int: Total number of gate operations.
"""
gate_ops = 0
for instr, _, _ in self.data:
if instr.name not in ['barrier', 'snapshot']:
gate_ops += 1
return gate_ops
|
Return circuit depth ( i. e. length of critical path ). This does not include compiler or simulator directives such as barrier or snapshot.
|
def depth(self):
"""Return circuit depth (i.e. length of critical path).
This does not include compiler or simulator directives
such as 'barrier' or 'snapshot'.
Returns:
int: Depth of circuit.
Notes:
The circuit depth and the DAG depth need not bt the
same.
"""
# Labels the registers by ints
# and then the qubit position in
# a register is given by reg_int+qubit_num
reg_offset = 0
reg_map = {}
for reg in self.qregs+self.cregs:
reg_map[reg.name] = reg_offset
reg_offset += reg.size
# A list that holds the height of each qubit
# and classical bit.
op_stack = [0]*reg_offset
# Here we are playing a modified version of
# Tetris where we stack gates, but multi-qubit
# gates, or measurements have a block for each
# qubit or cbit that are connected by a virtual
# line so that they all stacked at the same depth.
# Conditional gates act on all cbits in the register
# they are conditioned on.
# We do not consider barriers or snapshots as
# They are transpiler and simulator directives.
# The max stack height is the circuit depth.
for instr, qargs, cargs in self.data:
if instr.name not in ['barrier', 'snapshot']:
levels = []
reg_ints = []
for ind, reg in enumerate(qargs+cargs):
# Add to the stacks of the qubits and
# cbits used in the gate.
reg_ints.append(reg_map[reg[0].name]+reg[1])
levels.append(op_stack[reg_ints[ind]] + 1)
if instr.control:
# Controls operate over all bits in the
# classical register they use.
cint = reg_map[instr.control[0].name]
for off in range(instr.control[0].size):
if cint+off not in reg_ints:
reg_ints.append(cint+off)
levels.append(op_stack[cint+off]+1)
max_level = max(levels)
for ind in reg_ints:
op_stack[ind] = max_level
return max(op_stack)
|
Return number of qubits plus clbits in circuit.
|
def width(self):
"""Return number of qubits plus clbits in circuit.
Returns:
int: Width of circuit.
"""
return sum(reg.size for reg in self.qregs+self.cregs)
|
Count each operation kind in the circuit.
|
def count_ops(self):
"""Count each operation kind in the circuit.
Returns:
dict: a breakdown of how many operations of each kind.
"""
count_ops = {}
for instr, _, _ in self.data:
if instr.name in count_ops.keys():
count_ops[instr.name] += 1
else:
count_ops[instr.name] = 1
return count_ops
|
How many non - entangled subcircuits can the circuit be factored to.
|
def num_connected_components(self, unitary_only=False):
"""How many non-entangled subcircuits can the circuit be factored to.
Args:
unitary_only (bool): Compute only unitary part of graph.
Returns:
int: Number of connected components in circuit.
"""
# Convert registers to ints (as done in depth).
reg_offset = 0
reg_map = {}
if unitary_only:
regs = self.qregs
else:
regs = self.qregs+self.cregs
for reg in regs:
reg_map[reg.name] = reg_offset
reg_offset += reg.size
# Start with each qubit or cbit being its own subgraph.
sub_graphs = [[bit] for bit in range(reg_offset)]
num_sub_graphs = len(sub_graphs)
# Here we are traversing the gates and looking to see
# which of the sub_graphs the gate joins together.
for instr, qargs, cargs in self.data:
if unitary_only:
args = qargs
num_qargs = len(args)
else:
args = qargs+cargs
num_qargs = len(args) + (1 if instr.control else 0)
if num_qargs >= 2 and instr.name not in ['barrier', 'snapshot']:
graphs_touched = []
num_touched = 0
# Controls necessarily join all the cbits in the
# register that they use.
if instr.control and not unitary_only:
creg = instr.control[0]
creg_int = reg_map[creg.name]
for coff in range(creg.size):
temp_int = creg_int+coff
for k in range(num_sub_graphs):
if temp_int in sub_graphs[k]:
graphs_touched.append(k)
num_touched += 1
break
for item in args:
reg_int = reg_map[item[0].name]+item[1]
for k in range(num_sub_graphs):
if reg_int in sub_graphs[k]:
if k not in graphs_touched:
graphs_touched.append(k)
num_touched += 1
break
# If the gate touches more than one subgraph
# join those graphs together and return
# reduced number of subgraphs
if num_touched > 1:
connections = []
for idx in graphs_touched:
connections.extend(sub_graphs[idx])
_sub_graphs = []
for idx in range(num_sub_graphs):
if idx not in graphs_touched:
_sub_graphs.append(sub_graphs[idx])
_sub_graphs.append(connections)
sub_graphs = _sub_graphs
num_sub_graphs -= (num_touched-1)
# Cannot go lower than one so break
if num_sub_graphs == 1:
break
return num_sub_graphs
|
Assign parameters to values yielding a new circuit.
|
def bind_parameters(self, value_dict):
"""Assign parameters to values yielding a new circuit.
Args:
value_dict (dict): {parameter: value, ...}
Raises:
QiskitError: If value_dict contains parameters not present in the circuit
Returns:
QuantumCircuit: copy of self with assignment substitution.
"""
new_circuit = self.copy()
if value_dict.keys() > self.parameters:
raise QiskitError('Cannot bind parameters ({}) not present in the circuit.'.format(
[str(p) for p in value_dict.keys() - self.parameters]))
for parameter, value in value_dict.items():
new_circuit._bind_parameter(parameter, value)
# clear evaluated expressions
for parameter in value_dict:
del new_circuit._parameter_table[parameter]
return new_circuit
|
Assigns a parameter value to matching instructions in - place.
|
def _bind_parameter(self, parameter, value):
"""Assigns a parameter value to matching instructions in-place."""
for (instr, param_index) in self._parameter_table[parameter]:
instr.params[param_index] = value
|
Plot the interpolated envelope of pulse
|
def pulse_drawer(samples, duration, dt=None, interp_method='None',
filename=None, interactive=False,
dpi=150, nop=1000, size=(6, 5)):
"""Plot the interpolated envelope of pulse
Args:
samples (ndarray): Data points of complex pulse envelope.
duration (int): Pulse length (number of points).
dt (float): Time interval of samples.
interp_method (str): Method of interpolation
(set `None` for turn off the interpolation).
filename (str): Name required to save pulse image.
interactive (bool): When set true show the circuit in a new window
(this depends on the matplotlib backend being used supporting this).
dpi (int): Resolution of saved image.
nop (int): Data points for interpolation.
size (tuple): Size of figure.
Returns:
matplotlib.figure: A matplotlib figure object for the pulse envelope.
Raises:
ImportError: when the output methods requieres non-installed libraries.
QiskitError: when invalid interpolation method is specified.
"""
try:
from matplotlib import pyplot as plt
except ImportError:
raise ImportError('pulse_drawer need matplotlib. '
'Run "pip install matplotlib" before.')
if dt:
_dt = dt
else:
_dt = 1
re_y = np.real(samples)
im_y = np.imag(samples)
image = plt.figure(figsize=size)
ax0 = image.add_subplot(111)
if interp_method == 'CubicSpline':
# spline interpolation, use mid-point of dt
time = np.arange(0, duration + 1) * _dt + 0.5 * _dt
cs_ry = CubicSpline(time[:-1], re_y)
cs_iy = CubicSpline(time[:-1], im_y)
_time = np.linspace(0, duration * _dt, nop)
_re_y = cs_ry(_time)
_im_y = cs_iy(_time)
elif interp_method == 'None':
# pseudo-DAC output
time = np.arange(0, duration + 1) * _dt
_time = np.r_[time[0], np.repeat(time[1:-1], 2), time[-1]]
_re_y = np.repeat(re_y, 2)
_im_y = np.repeat(im_y, 2)
else:
raise QiskitError('Invalid interpolation method "%s"' % interp_method)
# plot
ax0.fill_between(x=_time, y1=_re_y, y2=np.zeros_like(_time),
facecolor='red', alpha=0.3,
edgecolor='red', linewidth=1.5,
label='real part')
ax0.fill_between(x=_time, y1=_im_y, y2=np.zeros_like(_time),
facecolor='blue', alpha=0.3,
edgecolor='blue', linewidth=1.5,
label='imaginary part')
ax0.set_xlim(0, duration * _dt)
ax0.grid(b=True, linestyle='-')
ax0.legend(bbox_to_anchor=(0.5, 1.00), loc='lower center',
ncol=2, frameon=False, fontsize=14)
if filename:
image.savefig(filename, dpi=dpi, bbox_inches='tight')
plt.close(image)
if image and interactive:
plt.show(image)
return image
|
Search for SWAPs which allow for application of largest number of gates.
|
def _search_forward_n_swaps(layout, gates, coupling_map,
depth=SEARCH_DEPTH, width=SEARCH_WIDTH):
"""Search for SWAPs which allow for application of largest number of gates.
Arguments:
layout (Layout): Map from virtual qubit index to physical qubit index.
gates (list): Gates to be mapped.
coupling_map (CouplingMap): CouplingMap of the target backend.
depth (int): Number of SWAP layers to search before choosing a result.
width (int): Number of SWAPs to consider at each layer.
Returns:
dict: Describes solution step found.
layout (Layout): Virtual to physical qubit map after SWAPs.
gates_remaining (list): Gates that could not be mapped.
gates_mapped (list): Gates that were mapped, including added SWAPs.
"""
gates_mapped, gates_remaining = _map_free_gates(layout, gates, coupling_map)
base_step = {'layout': layout,
'swaps_added': 0,
'gates_mapped': gates_mapped,
'gates_remaining': gates_remaining}
if not gates_remaining or depth == 0:
return base_step
possible_swaps = coupling_map.get_edges()
def _score_swap(swap):
"""Calculate the relative score for a given SWAP."""
trial_layout = layout.copy()
trial_layout.swap(*swap)
return _calc_layout_distance(gates, coupling_map, trial_layout)
ranked_swaps = sorted(possible_swaps, key=_score_swap)
best_swap, best_step = None, None
for swap in ranked_swaps[:width]:
trial_layout = layout.copy()
trial_layout.swap(*swap)
next_step = _search_forward_n_swaps(trial_layout, gates_remaining,
coupling_map, depth - 1, width)
# ranked_swaps already sorted by distance, so distance is the tie-breaker.
if best_swap is None or _score_step(next_step) > _score_step(best_step):
best_swap, best_step = swap, next_step
best_swap_gate = _swap_ops_from_edge(best_swap, layout)
return {
'layout': best_step['layout'],
'swaps_added': 1 + best_step['swaps_added'],
'gates_remaining': best_step['gates_remaining'],
'gates_mapped': gates_mapped + best_swap_gate + best_step['gates_mapped'],
}
|
Map all gates that can be executed with the current layout.
|
def _map_free_gates(layout, gates, coupling_map):
"""Map all gates that can be executed with the current layout.
Args:
layout (Layout): Map from virtual qubit index to physical qubit index.
gates (list): Gates to be mapped.
coupling_map (CouplingMap): CouplingMap for target device topology.
Returns:
tuple:
mapped_gates (list): ops for gates that can be executed, mapped onto layout.
remaining_gates (list): gates that cannot be executed on the layout.
"""
blocked_qubits = set()
mapped_gates = []
remaining_gates = []
for gate in gates:
# Gates without a partition (barrier, snapshot, save, load, noise) may
# still have associated qubits. Look for them in the qargs.
if not gate['partition']:
qubits = [n for n in gate['graph'].nodes() if n.type == 'op'][0].qargs
if not qubits:
continue
if blocked_qubits.intersection(qubits):
blocked_qubits.update(qubits)
remaining_gates.append(gate)
else:
mapped_gate = _transform_gate_for_layout(gate, layout)
mapped_gates.append(mapped_gate)
continue
qubits = gate['partition'][0]
if blocked_qubits.intersection(qubits):
blocked_qubits.update(qubits)
remaining_gates.append(gate)
elif len(qubits) == 1:
mapped_gate = _transform_gate_for_layout(gate, layout)
mapped_gates.append(mapped_gate)
elif coupling_map.distance(*[layout[q] for q in qubits]) == 1:
mapped_gate = _transform_gate_for_layout(gate, layout)
mapped_gates.append(mapped_gate)
else:
blocked_qubits.update(qubits)
remaining_gates.append(gate)
return mapped_gates, remaining_gates
|
Return the sum of the distances of two - qubit pairs in each CNOT in gates according to the layout and the coupling.
|
def _calc_layout_distance(gates, coupling_map, layout, max_gates=None):
"""Return the sum of the distances of two-qubit pairs in each CNOT in gates
according to the layout and the coupling.
"""
if max_gates is None:
max_gates = 50 + 10 * len(coupling_map.physical_qubits)
return sum(coupling_map.distance(*[layout[q] for q in gate['partition'][0]])
for gate in gates[:max_gates]
if gate['partition'] and len(gate['partition'][0]) == 2)
|
Count the mapped two - qubit gates less the number of added SWAPs.
|
def _score_step(step):
"""Count the mapped two-qubit gates, less the number of added SWAPs."""
# Each added swap will add 3 ops to gates_mapped, so subtract 3.
return len([g for g in step['gates_mapped']
if len(g.qargs) == 2]) - 3 * step['swaps_added']
|
Return a copy of source_dag with metadata but empty. Generate only a single qreg in the output DAG matching the size of the coupling_map.
|
def _copy_circuit_metadata(source_dag, coupling_map):
"""Return a copy of source_dag with metadata but empty.
Generate only a single qreg in the output DAG, matching the size of the
coupling_map."""
target_dag = DAGCircuit()
target_dag.name = source_dag.name
for creg in source_dag.cregs.values():
target_dag.add_creg(creg)
device_qreg = QuantumRegister(len(coupling_map.physical_qubits), 'q')
target_dag.add_qreg(device_qreg)
return target_dag
|
Return op implementing a virtual gate on given layout.
|
def _transform_gate_for_layout(gate, layout):
"""Return op implementing a virtual gate on given layout."""
mapped_op_node = deepcopy([n for n in gate['graph'].nodes() if n.type == 'op'][0])
# Workaround until #1816, apply mapped to qargs to both DAGNode and op
device_qreg = QuantumRegister(len(layout.get_physical_bits()), 'q')
mapped_qargs = [(device_qreg, layout[a]) for a in mapped_op_node.qargs]
mapped_op_node.qargs = mapped_op_node.op.qargs = mapped_qargs
mapped_op_node.pop('name')
return mapped_op_node
|
Generate list of ops to implement a SWAP gate along a coupling edge.
|
def _swap_ops_from_edge(edge, layout):
"""Generate list of ops to implement a SWAP gate along a coupling edge."""
device_qreg = QuantumRegister(len(layout.get_physical_bits()), 'q')
qreg_edge = [(device_qreg, i) for i in edge]
# TODO shouldn't be making other nodes not by the DAG!!
return [
DAGNode({'op': SwapGate(), 'qargs': qreg_edge, 'cargs': [], 'type': 'op'})
]
|
Run one pass of the lookahead mapper on the provided DAG.
|
def run(self, dag):
"""Run one pass of the lookahead mapper on the provided DAG.
Args:
dag (DAGCircuit): the directed acyclic graph to be mapped
Returns:
DAGCircuit: A dag mapped to be compatible with the coupling_map in
the property_set.
Raises:
TranspilerError: if the coupling map or the layout are not
compatible with the DAG
"""
coupling_map = self._coupling_map
ordered_virtual_gates = list(dag.serial_layers())
if self.initial_layout is None:
if self.property_set["layout"]:
self.initial_layout = self.property_set["layout"]
else:
self.initial_layout = Layout.generate_trivial_layout(*dag.qregs.values())
if len(dag.qubits()) != len(self.initial_layout):
raise TranspilerError('The layout does not match the amount of qubits in the DAG')
if len(self._coupling_map.physical_qubits) != len(self.initial_layout):
raise TranspilerError(
"Mappers require to have the layout to be the same size as the coupling map")
mapped_gates = []
layout = self.initial_layout.copy()
gates_remaining = ordered_virtual_gates.copy()
while gates_remaining:
best_step = _search_forward_n_swaps(layout, gates_remaining,
coupling_map)
layout = best_step['layout']
gates_mapped = best_step['gates_mapped']
gates_remaining = best_step['gates_remaining']
mapped_gates.extend(gates_mapped)
# Preserve input DAG's name, regs, wire_map, etc. but replace the graph.
mapped_dag = _copy_circuit_metadata(dag, coupling_map)
for node in mapped_gates:
mapped_dag.apply_operation_back(op=node.op, qargs=node.qargs, cargs=node.cargs)
return mapped_dag
|
Add a physical qubit to the coupling graph as a node.
|
def add_physical_qubit(self, physical_qubit):
"""Add a physical qubit to the coupling graph as a node.
physical_qubit (int): An integer representing a physical qubit.
Raises:
CouplingError: if trying to add duplicate qubit
"""
if not isinstance(physical_qubit, int):
raise CouplingError("Physical qubits should be integers.")
if physical_qubit in self.physical_qubits:
raise CouplingError(
"The physical qubit %s is already in the coupling graph" % physical_qubit)
self.graph.add_node(physical_qubit)
self._dist_matrix = None # invalidate
self._qubit_list = None
|
Add directed edge to coupling graph.
|
def add_edge(self, src, dst):
"""
Add directed edge to coupling graph.
src (int): source physical qubit
dst (int): destination physical qubit
"""
if src not in self.physical_qubits:
self.add_physical_qubit(src)
if dst not in self.physical_qubits:
self.add_physical_qubit(dst)
self.graph.add_edge(src, dst)
self._dist_matrix = None
|
Return a CouplingMap object for a subgraph of self.
|
def subgraph(self, nodelist):
"""Return a CouplingMap object for a subgraph of self.
nodelist (list): list of integer node labels
"""
subcoupling = CouplingMap()
subcoupling.graph = self.graph.subgraph(nodelist)
for node in nodelist:
if node not in subcoupling.physical_qubits:
subcoupling.add_physical_qubit(node)
return subcoupling
|
Returns a sorted list of physical_qubits
|
def physical_qubits(self):
"""Returns a sorted list of physical_qubits"""
if self._qubit_list is None:
self._qubit_list = sorted([pqubit for pqubit in self.graph.nodes])
return self._qubit_list
|
Test if the graph is connected.
|
def is_connected(self):
"""
Test if the graph is connected.
Return True if connected, False otherwise
"""
try:
return nx.is_weakly_connected(self.graph)
except nx.exception.NetworkXException:
return False
|
Compute the full distance matrix on pairs of nodes.
|
def _compute_distance_matrix(self):
"""Compute the full distance matrix on pairs of nodes.
The distance map self._dist_matrix is computed from the graph using
all_pairs_shortest_path_length.
"""
if not self.is_connected():
raise CouplingError("coupling graph not connected")
lengths = nx.all_pairs_shortest_path_length(self.graph.to_undirected(as_view=True))
lengths = dict(lengths)
size = len(lengths)
cmap = np.zeros((size, size))
for idx in range(size):
cmap[idx, np.fromiter(lengths[idx].keys(), dtype=int)] = np.fromiter(
lengths[idx].values(), dtype=int)
self._dist_matrix = cmap
|
Returns the undirected distance between physical_qubit1 and physical_qubit2.
|
def distance(self, physical_qubit1, physical_qubit2):
"""Returns the undirected distance between physical_qubit1 and physical_qubit2.
Args:
physical_qubit1 (int): A physical qubit
physical_qubit2 (int): Another physical qubit
Returns:
int: The undirected distance
Raises:
CouplingError: if the qubits do not exist in the CouplingMap
"""
if physical_qubit1 not in self.physical_qubits:
raise CouplingError("%s not in coupling graph" % (physical_qubit1,))
if physical_qubit2 not in self.physical_qubits:
raise CouplingError("%s not in coupling graph" % (physical_qubit2,))
if self._dist_matrix is None:
self._compute_distance_matrix()
return self._dist_matrix[physical_qubit1, physical_qubit2]
|
transpile one or more circuits.
|
def transpile(circuits, backend=None, basis_gates=None, coupling_map=None,
initial_layout=None, seed_mapper=None, pass_manager=None):
"""transpile one or more circuits.
Args:
circuits (QuantumCircuit or list[QuantumCircuit]): circuits to compile
backend (BaseBackend): a backend to compile for
basis_gates (list[str]): list of basis gate names supported by the
target. Default: ['u1','u2','u3','cx','id']
coupling_map (list): coupling map (perhaps custom) to target in mapping
initial_layout (Layout or dict or list):
Initial position of virtual qubits on physical qubits. The final
layout is not guaranteed to be the same, as the transpiler may permute
qubits through swaps or other means.
seed_mapper (int): random seed for the swap_mapper
pass_manager (PassManager): a pass_manager for the transpiler stages
Returns:
QuantumCircuit or list[QuantumCircuit]: transpiled circuit(s).
Raises:
TranspilerError: in case of bad inputs to transpiler or errors in passes
"""
warnings.warn("qiskit.transpiler.transpile() has been deprecated and will be "
"removed in the 0.9 release. Use qiskit.compiler.transpile() instead.",
DeprecationWarning)
return compiler.transpile(circuits=circuits, backend=backend,
basis_gates=basis_gates, coupling_map=coupling_map,
initial_layout=initial_layout, seed_transpiler=seed_mapper,
pass_manager=pass_manager)
|
Deprecated - Use qiskit. compiler. transpile for transpiling from circuits to circuits. Transform a dag circuit into another dag circuit ( transpile ) through consecutive passes on the dag.
|
def transpile_dag(dag, basis_gates=None, coupling_map=None,
initial_layout=None, seed_mapper=None, pass_manager=None):
"""Deprecated - Use qiskit.compiler.transpile for transpiling from
circuits to circuits.
Transform a dag circuit into another dag circuit
(transpile), through consecutive passes on the dag.
Args:
dag (DAGCircuit): dag circuit to transform via transpilation
basis_gates (list[str]): list of basis gate names supported by the
target. Default: ['u1','u2','u3','cx','id']
coupling_map (list): A graph of coupling::
[
[control0(int), target0(int)],
[control1(int), target1(int)],
]
eg. [[0, 2], [1, 2], [1, 3], [3, 4]}
initial_layout (Layout or None): A layout object
seed_mapper (int): random seed_mapper for the swap mapper
pass_manager (PassManager): pass manager instance for the transpilation process
If None, a default set of passes are run.
Otherwise, the passes defined in it will run.
If contains no passes in it, no dag transformations occur.
Returns:
DAGCircuit: transformed dag
"""
warnings.warn("transpile_dag has been deprecated and will be removed in the "
"0.9 release. Circuits can be transpiled directly to other "
"circuits with the transpile function.", DeprecationWarning)
if basis_gates is None:
basis_gates = ['u1', 'u2', 'u3', 'cx', 'id']
if pass_manager is None:
# default set of passes
# if a coupling map is given compile to the map
if coupling_map:
pass_manager = default_pass_manager(basis_gates,
CouplingMap(coupling_map),
initial_layout,
seed_transpiler=seed_mapper)
else:
pass_manager = default_pass_manager_simulator(basis_gates)
# run the passes specified by the pass manager
# TODO return the property set too. See #1086
name = dag.name
circuit = dag_to_circuit(dag)
circuit = pass_manager.run(circuit)
dag = circuit_to_dag(circuit)
dag.name = name
return dag
|
Apply cu1 from ctl to tgt with angle theta.
|
def cu1(self, theta, ctl, tgt):
"""Apply cu1 from ctl to tgt with angle theta."""
return self.append(Cu1Gate(theta), [ctl, tgt], [])
|
Add an instruction and its context ( where it s attached ).
|
def add(self, gate, qargs, cargs):
"""Add an instruction and its context (where it's attached)."""
if not isinstance(gate, Instruction):
raise QiskitError("attempt to add non-Instruction" +
" to InstructionSet")
self.instructions.append(gate)
self.qargs.append(qargs)
self.cargs.append(cargs)
|
Invert all instructions.
|
def inverse(self):
"""Invert all instructions."""
for index, instruction in enumerate(self.instructions):
self.instructions[index] = instruction.inverse()
return self
|
Add controls to all instructions.
|
def q_if(self, *qregs):
"""Add controls to all instructions."""
for gate in self.instructions:
gate.q_if(*qregs)
return self
|
Add classical control register to all instructions.
|
def c_if(self, classical, val):
"""Add classical control register to all instructions."""
for gate in self.instructions:
gate.c_if(classical, val)
return self
|
Subscribes to an event so when it s emitted all the callbacks subscribed will be executed. We are not allowing double registration.
|
def subscribe(self, event, callback):
"""Subscribes to an event, so when it's emitted all the callbacks subscribed,
will be executed. We are not allowing double registration.
Args
event (string): The event to subscribed in the form of:
"terra.<component>.<method>.<action>"
callback (callable): The callback that will be executed when an event is
emitted.
"""
if not callable(callback):
raise QiskitError("Callback is not a callable!")
if event not in self._subscribers:
self._subscribers[event] = []
new_subscription = self._Subscription(event, callback)
if new_subscription in self._subscribers[event]:
# We are not allowing double subscription
return False
self._subscribers[event].append(new_subscription)
return True
|
Emits an event if there are any subscribers.
|
def dispatch(self, event, *args, **kwargs):
"""Emits an event if there are any subscribers.
Args
event (String): The event to be emitted
args: Arguments linked with the event
kwargs: Named arguments linked with the event
"""
# No event, no subscribers.
if event not in self._subscribers:
return
for subscriber in self._subscribers[event]:
subscriber.callback(*args, **kwargs)
|
Unsubscribe the specific callback to the event.
|
def unsubscribe(self, event, callback):
""" Unsubscribe the specific callback to the event.
Args
event (String): The event to unsubscribe
callback (callable): The callback that won't be executed anymore
Returns
True: if we have successfully unsubscribed to the event
False: if there's no callback previously registered
"""
try:
self._subscribers[event].remove(self._Subscription(event, callback))
except KeyError:
return False
return True
|
Triggers an event and associates some data to it so if there are any subscribers their callback will be called synchronously.
|
def publish(self, event, *args, **kwargs):
""" Triggers an event, and associates some data to it, so if there are any
subscribers, their callback will be called synchronously. """
return self._broker.dispatch(event, *args, **kwargs)
|
Apply initialize to circuit.
|
def initialize(self, params, qubits):
"""Apply initialize to circuit."""
if isinstance(qubits, QuantumRegister):
qubits = qubits[:]
else:
qubits = _convert_to_bits([qubits], [qbit for qreg in self.qregs for qbit in qreg])[0]
return self.append(Initialize(params), qubits)
|
Calculate a subcircuit that implements this initialization
|
def _define(self):
"""Calculate a subcircuit that implements this initialization
Implements a recursive initialization algorithm, including optimizations,
from "Synthesis of Quantum Logic Circuits" Shende, Bullock, Markov
https://arxiv.org/abs/quant-ph/0406176v5
Additionally implements some extra optimizations: remove zero rotations and
double cnots.
"""
# call to generate the circuit that takes the desired vector to zero
disentangling_circuit = self.gates_to_uncompute()
# invert the circuit to create the desired vector from zero (assuming
# the qubits are in the zero state)
initialize_instr = disentangling_circuit.to_instruction().inverse()
q = QuantumRegister(self.num_qubits, 'q')
initialize_circuit = QuantumCircuit(q, name='init_def')
for qubit in q:
initialize_circuit.append(Reset(), [qubit])
initialize_circuit.append(initialize_instr, q[:])
self.definition = initialize_circuit.data
|
Call to create a circuit with gates that take the desired vector to zero.
|
def gates_to_uncompute(self):
"""
Call to create a circuit with gates that take the
desired vector to zero.
Returns:
QuantumCircuit: circuit to take self.params vector to |00..0>
"""
q = QuantumRegister(self.num_qubits)
circuit = QuantumCircuit(q, name='disentangler')
# kick start the peeling loop, and disentangle one-by-one from LSB to MSB
remaining_param = self.params
for i in range(self.num_qubits):
# work out which rotations must be done to disentangle the LSB
# qubit (we peel away one qubit at a time)
(remaining_param,
thetas,
phis) = Initialize._rotations_to_disentangle(remaining_param)
# perform the required rotations to decouple the LSB qubit (so that
# it can be "factored" out, leaving a shorter amplitude vector to peel away)
rz_mult = self._multiplex(RZGate, phis)
ry_mult = self._multiplex(RYGate, thetas)
circuit.append(rz_mult.to_instruction(), q[i:self.num_qubits])
circuit.append(ry_mult.to_instruction(), q[i:self.num_qubits])
return circuit
|
Static internal method to work out Ry and Rz rotation angles used to disentangle the LSB qubit. These rotations make up the block diagonal matrix U ( i. e. multiplexor ) that disentangles the LSB.
|
def _rotations_to_disentangle(local_param):
"""
Static internal method to work out Ry and Rz rotation angles used
to disentangle the LSB qubit.
These rotations make up the block diagonal matrix U (i.e. multiplexor)
that disentangles the LSB.
[[Ry(theta_1).Rz(phi_1) 0 . . 0],
[0 Ry(theta_2).Rz(phi_2) . 0],
.
.
0 0 Ry(theta_2^n).Rz(phi_2^n)]]
"""
remaining_vector = []
thetas = []
phis = []
param_len = len(local_param)
for i in range(param_len // 2):
# Ry and Rz rotations to move bloch vector from 0 to "imaginary"
# qubit
# (imagine a qubit state signified by the amplitudes at index 2*i
# and 2*(i+1), corresponding to the select qubits of the
# multiplexor being in state |i>)
(remains,
add_theta,
add_phi) = Initialize._bloch_angles(local_param[2 * i: 2 * (i + 1)])
remaining_vector.append(remains)
# rotations for all imaginary qubits of the full vector
# to move from where it is to zero, hence the negative sign
thetas.append(-add_theta)
phis.append(-add_phi)
return remaining_vector, thetas, phis
|
Static internal method to work out rotation to create the passed in qubit from the zero vector.
|
def _bloch_angles(pair_of_complex):
"""
Static internal method to work out rotation to create the passed in
qubit from the zero vector.
"""
[a_complex, b_complex] = pair_of_complex
# Force a and b to be complex, as otherwise numpy.angle might fail.
a_complex = complex(a_complex)
b_complex = complex(b_complex)
mag_a = np.absolute(a_complex)
final_r = float(np.sqrt(mag_a ** 2 + np.absolute(b_complex) ** 2))
if final_r < _EPS:
theta = 0
phi = 0
final_r = 0
final_t = 0
else:
theta = float(2 * np.arccos(mag_a / final_r))
a_arg = np.angle(a_complex)
b_arg = np.angle(b_complex)
final_t = a_arg + b_arg
phi = b_arg - a_arg
return final_r * np.exp(1.J * final_t / 2), theta, phi
|
Return a recursive implementation of a multiplexor circuit where each instruction itself has a decomposition based on smaller multiplexors.
|
def _multiplex(self, target_gate, list_of_angles):
"""
Return a recursive implementation of a multiplexor circuit,
where each instruction itself has a decomposition based on
smaller multiplexors.
The LSB is the multiplexor "data" and the other bits are multiplexor "select".
Args:
target_gate (Gate): Ry or Rz gate to apply to target qubit, multiplexed
over all other "select" qubits
list_of_angles (list[float]): list of rotation angles to apply Ry and Rz
Returns:
DAGCircuit: the circuit implementing the multiplexor's action
"""
list_len = len(list_of_angles)
local_num_qubits = int(math.log2(list_len)) + 1
q = QuantumRegister(local_num_qubits)
circuit = QuantumCircuit(q, name="multiplex" + local_num_qubits.__str__())
lsb = q[0]
msb = q[local_num_qubits - 1]
# case of no multiplexing: base case for recursion
if local_num_qubits == 1:
circuit.append(target_gate(list_of_angles[0]), [q[0]])
return circuit
# calc angle weights, assuming recursion (that is the lower-level
# requested angles have been correctly implemented by recursion
angle_weight = scipy.kron([[0.5, 0.5], [0.5, -0.5]],
np.identity(2 ** (local_num_qubits - 2)))
# calc the combo angles
list_of_angles = angle_weight.dot(np.array(list_of_angles)).tolist()
# recursive step on half the angles fulfilling the above assumption
multiplex_1 = self._multiplex(target_gate, list_of_angles[0:(list_len // 2)])
circuit.append(multiplex_1.to_instruction(), q[0:-1])
# attach CNOT as follows, thereby flipping the LSB qubit
circuit.append(CnotGate(), [msb, lsb])
# implement extra efficiency from the paper of cancelling adjacent
# CNOTs (by leaving out last CNOT and reversing (NOT inverting) the
# second lower-level multiplex)
multiplex_2 = self._multiplex(target_gate, list_of_angles[(list_len // 2):])
if list_len > 1:
circuit.append(multiplex_2.to_instruction().mirror(), q[0:-1])
else:
circuit.append(multiplex_2.to_instruction(), q[0:-1])
# attach a final CNOT
circuit.append(CnotGate(), [msb, lsb])
return circuit
|
Populates a Layout from a dictionary. The dictionary must be a bijective mapping between virtual qubits ( tuple ) and physical qubits ( int ).
|
def from_dict(self, input_dict):
"""
Populates a Layout from a dictionary.
The dictionary must be a bijective mapping between
virtual qubits (tuple) and physical qubits (int).
Args:
input_dict (dict):
e.g.:
{(QuantumRegister(3, 'qr'), 0): 0,
(QuantumRegister(3, 'qr'), 1): 1,
(QuantumRegister(3, 'qr'), 2): 2}
Can be written more concisely as follows:
virtual to physical:
{qr[0]: 0,
qr[1]: 1,
qr[2]: 2}
physical to virtual:
{0: qr[0],
1: qr[1],
2: qr[2]}
"""
# TODO (luciano): Remove this full block after 0.8.
# its here to support {("qr", 0): ("q", 0),...}
if len(input_dict) >= 1:
key = list(input_dict.keys())[0]
value = input_dict[key]
if (isinstance(key, tuple) and # pylint: disable=too-many-boolean-expressions
len(key) == 2 and
isinstance(key[0], str) and
isinstance(key[1], int) and
isinstance(value, tuple) and
len(value) == 2 and
isinstance(key[0], str) and
isinstance(key[1], int)):
warnings.warn("This form of dictionary (i.e. {(\"%s\",%s):(\"%s\",%s), ...} is "
"going to be deprecated after 0.8." % (key + value),
DeprecationWarning)
qreg_names = {qubit[0] for qubit in input_dict.keys()}
qregs = {}
for qreg_name in qreg_names:
qregs[qreg_name] = QuantumRegister(
max([qubit[1] for qubit in input_dict.keys() if qubit[0] == qreg_name]) + 1,
qreg_name)
new_input_dict = {}
for key, value in input_dict.items():
new_input_dict[value[1]] = (qregs[key[0]], key[1])
input_dict = new_input_dict
for key, value in input_dict.items():
virtual, physical = Layout.order_based_on_type(key, value)
self._p2v[physical] = virtual
if virtual is None:
continue
self._v2p[virtual] = physical
|
decides which one is physical/ virtual based on the type. Returns ( virtual physical )
|
def order_based_on_type(value1, value2):
"""decides which one is physical/virtual based on the type. Returns (virtual, physical)"""
if isinstance(value1, int) and Layout.is_virtual(value2):
physical = value1
virtual = value2
elif isinstance(value2, int) and Layout.is_virtual(value1):
physical = value2
virtual = value1
else:
raise LayoutError('The map (%s -> %s) has to be a ((Register, integer) -> integer)'
' or the other way around.' % (type(value1), type(value2)))
return virtual, physical
|
Checks if value has the format of a virtual qubit
|
def is_virtual(value):
"""Checks if value has the format of a virtual qubit """
return value is None or isinstance(value, tuple) and len(value) == 2 and isinstance(
value[0], Register) and isinstance(value[1], int)
|
Returns a copy of a Layout instance.
|
def copy(self):
"""Returns a copy of a Layout instance."""
layout_copy = type(self)()
layout_copy._p2v = self._p2v.copy()
layout_copy._v2p = self._v2p.copy()
return layout_copy
|
Adds a map element between bit and physical_bit. If physical_bit is not defined bit will be mapped to a new physical bit ( extending the length of the layout by one. ) Args: virtual_bit ( tuple ): A ( qu ) bit. For example ( QuantumRegister ( 3 qr ) 2 ). physical_bit ( int ): A physical bit. For example 3.
|
def add(self, virtual_bit, physical_bit=None):
"""
Adds a map element between `bit` and `physical_bit`. If `physical_bit` is not
defined, `bit` will be mapped to a new physical bit (extending the length of the
layout by one.)
Args:
virtual_bit (tuple): A (qu)bit. For example, (QuantumRegister(3, 'qr'), 2).
physical_bit (int): A physical bit. For example, 3.
"""
if physical_bit is None:
physical_candidate = len(self)
while physical_candidate in self._p2v:
physical_candidate += 1
physical_bit = physical_candidate
self[virtual_bit] = physical_bit
|
Swaps the map between left and right. Args: left ( tuple or int ): Item to swap with right. right ( tuple or int ): Item to swap with left. Raises: LayoutError: If left and right have not the same type.
|
def swap(self, left, right):
"""Swaps the map between left and right.
Args:
left (tuple or int): Item to swap with right.
right (tuple or int): Item to swap with left.
Raises:
LayoutError: If left and right have not the same type.
"""
if type(left) is not type(right):
raise LayoutError('The method swap only works with elements of the same type.')
temp = self[left]
self[left] = self[right]
self[right] = temp
|
Combines self and another_layout into an edge map.
|
def combine_into_edge_map(self, another_layout):
"""Combines self and another_layout into an "edge map".
For example::
self another_layout resulting edge map
qr_1 -> 0 0 <- q_2 qr_1 -> q_2
qr_2 -> 2 2 <- q_1 qr_2 -> q_1
qr_3 -> 3 3 <- q_0 qr_3 -> q_0
The edge map is used to compose dags via, for example, compose_back.
Args:
another_layout (Layout): The other layout to combine.
Returns:
dict: A "edge map".
Raises:
LayoutError: another_layout can be bigger than self, but not smaller. Otherwise, raises.
"""
edge_map = dict()
for virtual, physical in self.get_virtual_bits().items():
if physical not in another_layout._p2v:
raise LayoutError('The wire_map_from_layouts() method does not support when the'
' other layout (another_layout) is smaller.')
edge_map[virtual] = another_layout[physical]
return edge_map
|
Creates a trivial ( one - to - one ) Layout with the registers in regs. Args: * regs ( Registers ): registers to include in the layout. Returns: Layout: A layout with all the regs in the given order.
|
def generate_trivial_layout(*regs):
"""
Creates a trivial ("one-to-one") Layout with the registers in `regs`.
Args:
*regs (Registers): registers to include in the layout.
Returns:
Layout: A layout with all the `regs` in the given order.
"""
layout = Layout()
for reg in regs:
layout.add_register(reg)
return layout
|
Converts a list of integers to a Layout mapping virtual qubits ( index of the list ) to physical qubits ( the list values ).
|
def from_intlist(int_list, *qregs):
"""Converts a list of integers to a Layout
mapping virtual qubits (index of the list) to
physical qubits (the list values).
Args:
int_list (list): A list of integers.
*qregs (QuantumRegisters): The quantum registers to apply
the layout to.
Returns:
Layout: The corresponding Layout object.
Raises:
LayoutError: Invalid input layout.
"""
if not all((isinstance(i, int) for i in int_list)):
raise LayoutError('Expected a list of ints')
if len(int_list) != len(set(int_list)):
raise LayoutError('Duplicate values not permitted; Layout is bijective.')
n_qubits = sum(reg.size for reg in qregs)
# Check if list is too short to cover all qubits
if len(int_list) < n_qubits:
err_msg = 'Integer list length must equal number of qubits in circuit.'
raise LayoutError(err_msg)
out = Layout()
main_idx = 0
for qreg in qregs:
for idx in range(qreg.size):
out[(qreg, idx)] = int_list[main_idx]
main_idx += 1
if main_idx != len(int_list):
for int_item in int_list[main_idx:]:
out[int_item] = None
return out
|
Populates a Layout from a list containing virtual qubits --- ( QuantumRegister int ) tuples --- or None.
|
def from_tuplelist(tuple_list):
"""
Populates a Layout from a list containing virtual
qubits---(QuantumRegister, int) tuples---, or None.
Args:
tuple_list (list):
e.g.: [qr[0], None, qr[2], qr[3]]
Returns:
Layout: the corresponding Layout object
Raises:
LayoutError: If the elements are not (Register, integer) or None
"""
out = Layout()
for physical, virtual in enumerate(tuple_list):
if virtual is None:
continue
elif Layout.is_virtual(virtual):
if virtual in out._v2p:
raise LayoutError('Duplicate values not permitted; Layout is bijective.')
out[virtual] = physical
else:
raise LayoutError("The list should contain elements of the form"
" (Register, integer) or None")
return out
|
Apply Toffoli to from ctl1 and ctl2 to tgt.
|
def ccx(self, ctl1, ctl2, tgt):
"""Apply Toffoli to from ctl1 and ctl2 to tgt."""
return self.append(ToffoliGate(), [ctl1, ctl2, tgt], [])
|
gate ccx a b c { h c ; cx b c ; tdg c ; cx a c ; t c ; cx b c ; tdg c ; cx a c ; t b ; t c ; h c ; cx a b ; t a ; tdg b ; cx a b ; }
|
def _define(self):
"""
gate ccx a,b,c
{
h c; cx b,c; tdg c; cx a,c;
t c; cx b,c; tdg c; cx a,c;
t b; t c; h c; cx a,b;
t a; tdg b; cx a,b;}
"""
definition = []
q = QuantumRegister(3, "q")
rule = [
(HGate(), [q[2]], []),
(CnotGate(), [q[1], q[2]], []),
(TdgGate(), [q[2]], []),
(CnotGate(), [q[0], q[2]], []),
(TGate(), [q[2]], []),
(CnotGate(), [q[1], q[2]], []),
(TdgGate(), [q[2]], []),
(CnotGate(), [q[0], q[2]], []),
(TGate(), [q[1]], []),
(TGate(), [q[2]], []),
(HGate(), [q[2]], []),
(CnotGate(), [q[0], q[1]], []),
(TGate(), [q[0]], []),
(TdgGate(), [q[1]], []),
(CnotGate(), [q[0], q[1]], [])
]
for inst in rule:
definition.append(inst)
self.definition = definition
|
collect blocks of adjacent gates acting on a pair of cx qubits.
|
def run(self, dag):
"""collect blocks of adjacent gates acting on a pair of "cx" qubits.
The blocks contain "op" nodes in topological sort order
such that all gates in a block act on the same pair of
qubits and are adjacent in the circuit. the blocks are built
by examining predecessors and successors of "cx" gates in
the circuit. u1, u2, u3, cx, id gates will be included.
Return a list of tuples of "op" node labels.
"""
# Initiate the commutation set
self.property_set['commutation_set'] = defaultdict(list)
good_names = ["cx", "u1", "u2", "u3", "id"]
block_list = []
nodes = list(dag.topological_nodes())
nodes_seen = dict(zip(nodes, [False] * len(nodes)))
for nd in dag.topological_op_nodes():
group = []
# Explore predecessors and successors of cx gates
if nd.name == "cx" and nd.condition is None and not nodes_seen[nd]:
these_qubits = sorted(nd.qargs)
# Explore predecessors of the "cx" node
pred = list(dag.predecessors(nd))
explore = True
while explore:
pred_next = []
# If there is one predecessor, add it if it's on the right qubits
if len(pred) == 1 and not nodes_seen[pred[0]]:
pnd = pred[0]
if pnd.name in good_names:
if (pnd.name == "cx" and sorted(pnd.qargs) == these_qubits) or \
pnd.name != "cx":
group.append(pnd)
nodes_seen[pnd] = True
pred_next.extend(dag.predecessors(pnd))
# If there are two, then we consider cases
elif len(pred) == 2:
# First, check if there is a relationship
if pred[0] in dag.predecessors(pred[1]):
sorted_pred = [pred[1]] # was [pred[1], pred[0]]
elif pred[1] in dag.predecessors(pred[0]):
sorted_pred = [pred[0]] # was [pred[0], pred[1]]
else:
# We need to avoid accidentally adding a cx on these_qubits
# since these must have a dependency through the other predecessor
# in this case
if pred[0].name == "cx" and sorted(pred[0].qargs) == these_qubits:
sorted_pred = [pred[1]]
elif pred[1].name == "cx" and sorted(pred[1].qargs) == these_qubits:
sorted_pred = [pred[0]]
else:
sorted_pred = pred
if len(sorted_pred) == 2 and sorted_pred[0].name == "cx" and \
sorted_pred[1].name == "cx":
break # stop immediately if we hit a pair of cx
# Examine each predecessor
for pnd in sorted_pred:
if pnd.name not in good_names:
continue
# If a predecessor is a single qubit gate, add it
if pnd.name != "cx":
if not nodes_seen[pnd]:
group.append(pnd)
nodes_seen[pnd] = True
pred_next.extend(dag.predecessors(pnd))
# If cx, check qubits
else:
pred_qubits = sorted(pnd.qargs)
if pred_qubits == these_qubits:
# add if on same qubits
if not nodes_seen[pnd]:
group.append(pnd)
nodes_seen[pnd] = True
pred_next.extend(dag.predecessors(pnd))
else:
# remove qubit from consideration if not
these_qubits = list(set(these_qubits) -
set(pred_qubits))
# Update predecessors
# Stop if there aren't any more
pred = list(set(pred_next))
if not pred:
explore = False
# Reverse the predecessor list and append the "cx" node
group.reverse()
group.append(nd)
nodes_seen[nd] = True
# Reset these_qubits
these_qubits = sorted(nd.qargs)
# Explore successors of the "cx" node
succ = list(dag.successors(nd))
explore = True
while explore:
succ_next = []
# If there is one successor, add it if its on the right qubits
if len(succ) == 1 and not nodes_seen[succ[0]]:
snd = succ[0]
if snd.name in good_names:
if (snd.name == "cx" and sorted(snd.qargs) == these_qubits) or \
snd.name != "cx":
group.append(snd)
nodes_seen[snd] = True
succ_next.extend(dag.successors(snd))
# If there are two, then we consider cases
elif len(succ) == 2:
# First, check if there is a relationship
if succ[0] in dag.successors(succ[1]):
sorted_succ = [succ[1]] # was [succ[1], succ[0]]
elif succ[1] in dag.successors(succ[0]):
sorted_succ = [succ[0]] # was [succ[0], succ[1]]
else:
# We need to avoid accidentally adding a cx on these_qubits
# since these must have a dependency through the other successor
# in this case
if succ[0].name == "cx" and sorted(succ[0].qargs) == these_qubits:
sorted_succ = [succ[1]]
elif succ[1].name == "cx" and sorted(succ[1].qargs) == these_qubits:
sorted_succ = [succ[0]]
else:
sorted_succ = succ
if len(sorted_succ) == 2 and \
sorted_succ[0].name == "cx" and \
sorted_succ[1].name == "cx":
break # stop immediately if we hit a pair of cx
# Examine each successor
for snd in sorted_succ:
if snd.name not in good_names:
continue
# If a successor is a single qubit gate, add it
if snd.name != "cx":
if not nodes_seen[snd]:
group.append(snd)
nodes_seen[snd] = True
succ_next.extend(dag.successors(snd))
else:
# If cx, check qubits
succ_qubits = sorted(snd.qargs)
if succ_qubits == these_qubits:
# add if on same qubits
if not nodes_seen[snd]:
group.append(snd)
nodes_seen[snd] = True
succ_next.extend(dag.successors(snd))
else:
# remove qubit from consideration if not
these_qubits = list(set(these_qubits) -
set(succ_qubits))
# Update successors
# Stop if there aren't any more
succ = list(set(succ_next))
if not succ:
explore = False
block_list.append(tuple(group))
self.property_set['block_list'] = block_list
return dag
|
Apply u2 to q.
|
def u2(self, phi, lam, q):
"""Apply u2 to q."""
return self.append(U2Gate(phi, lam), [q], [])
|
Return a Numpy. array for the U3 gate.
|
def to_matrix(self):
"""Return a Numpy.array for the U3 gate."""
isqrt2 = 1 / numpy.sqrt(2)
phi, lam = self.params
phi, lam = float(phi), float(lam)
return numpy.array([[isqrt2, -numpy.exp(1j * lam) * isqrt2],
[
numpy.exp(1j * phi) * isqrt2,
numpy.exp(1j * (phi + lam)) * isqrt2
]],
dtype=complex)
|
Return a new schedule with schedule inserted within self at start_time.
|
def insert(self, start_time: int, schedule: ScheduleComponent) -> 'ScheduleComponent':
"""Return a new schedule with `schedule` inserted within `self` at `start_time`.
Args:
start_time: time to be inserted
schedule: schedule to be inserted
"""
return ops.insert(self, start_time, schedule)
|
Checks if the attribute name is in the list of attributes to protect. If so raises TranspilerAccessError.
|
def _check_if_fenced(self, name):
"""
Checks if the attribute name is in the list of attributes to protect. If so, raises
TranspilerAccessError.
Args:
name (string): the attribute name to check
Raises:
TranspilerAccessError: when name is the list of attributes to protect.
"""
if name in object.__getattribute__(self, '_attributes_to_fence'):
raise TranspilerAccessError("The fenced %s has the property %s protected" %
(type(object.__getattribute__(self, '_wrapped')), name))
|
Return True if completely - positive trace - preserving.
|
def is_cptp(self, atol=None, rtol=None):
"""Return True if completely-positive trace-preserving."""
if atol is None:
atol = self._atol
if rtol is None:
rtol = self._rtol
if self._data[1] is not None:
return False
check = np.dot(np.transpose(np.conj(self._data[0])), self._data[0])
return is_identity_matrix(check, rtol=self._rtol, atol=self._atol)
|
Return the conjugate of the QuantumChannel.
|
def conjugate(self):
"""Return the conjugate of the QuantumChannel."""
# pylint: disable=assignment-from-no-return
stine_l = np.conjugate(self._data[0])
stine_r = None
if self._data[1] is not None:
stine_r = np.conjugate(self._data[1])
return Stinespring((stine_l, stine_r), self.input_dims(),
self.output_dims())
|
Return the transpose of the QuantumChannel.
|
def transpose(self):
"""Return the transpose of the QuantumChannel."""
din, dout = self.dim
dtr = self._data[0].shape[0] // dout
stine = [None, None]
for i, mat in enumerate(self._data):
if mat is not None:
stine[i] = np.reshape(
np.transpose(np.reshape(mat, (dout, dtr, din)), (2, 1, 0)),
(din * dtr, dout))
return Stinespring(
tuple(stine),
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 subclass.
qargs (list): a list of subsystem positions to compose other on.
front (bool): If False compose in standard order other(self(input))
otherwise compose in reverse order self(other(input))
[default: False]
Returns:
Stinespring: The composition channel as a Stinespring object.
Raises:
QiskitError: if other cannot be converted to a channel or
has incompatible dimensions.
"""
if qargs is not None:
return Stinespring(
SuperOp(self).compose(other, qargs=qargs, front=front))
# Convert other to Kraus
if not isinstance(other, Kraus):
other = Kraus(other)
# Check dimensions match up
if front and self._input_dim != other._output_dim:
raise QiskitError(
'input_dim of self must match output_dim of other')
if not front and self._output_dim != other._input_dim:
raise QiskitError(
'input_dim of other must match output_dim of self')
# Since we cannot directly compose two channels in Stinespring
# representation we convert to the Kraus representation
return Stinespring(Kraus(self).compose(other, front=front))
|
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:
Stinespring: the matrix power of the SuperOp converted to a
Stinespring channel.
Raises:
QiskitError: if the input and output dimensions of the
QuantumChannel are not equal, or the power is not an integer.
"""
if n > 0:
return super().power(n)
return Stinespring(SuperOp(self).power(n))
|
Return the QuantumChannel self + other.
|
def multiply(self, other):
"""Return the QuantumChannel self + other.
Args:
other (complex): a complex number.
Returns:
Stinespring: the scalar multiplication other * self as a
Stinespring object.
Raises:
QiskitError: if other is not a valid scalar.
"""
if not isinstance(other, Number):
raise QiskitError("other is not a number")
# If the number is complex or negative we need to convert to
# general Stinespring representation so we first convert to
# the Choi representation
if isinstance(other, complex) or other < 1:
# Convert to Choi-matrix
return Stinespring(Choi(self).multiply(other))
# If the number is real we can update the Kraus operators
# directly
num = np.sqrt(other)
stine_l, stine_r = self._data
stine_l = num * self._data[0]
stine_r = None
if self._data[1] is not None:
stine_r = num * self._data[1]
return Stinespring((stine_l, stine_r), self.input_dims(),
self.output_dims())
|
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.
Returns:
QuantumState: the output quantum state.
Raises:
QiskitError: if the operator dimension does not match the
specified QuantumState subsystem dimensions.
"""
# If subsystem evolution we use the SuperOp representation
if qargs is not None:
return SuperOp(self)._evolve(state, qargs)
# Otherwise we compute full evolution directly
state = self._format_state(state)
if state.shape[0] != self._input_dim:
raise QiskitError(
"QuantumChannel input dimension is not equal to state dimension."
)
if state.ndim == 1 and self._data[1] is None and \
self._data[0].shape[0] // self._output_dim == 1:
# If the shape of the stinespring operator is equal to the output_dim
# evolution of a state vector psi -> stine.psi
return np.dot(self._data[0], state)
# Otherwise we always return a density matrix
state = self._format_state(state, density_matrix=True)
stine_l, stine_r = self._data
if stine_r is None:
stine_r = stine_l
din, dout = self.dim
dtr = stine_l.shape[0] // dout
shape = (dout, dtr, din)
return np.einsum('iAB,BC,jAC->ij', np.reshape(stine_l, shape), state,
np.reshape(np.conjugate(stine_r), shape))
|
Return the tensor product channel.
|
def _tensor_product(self, other, reverse=False):
"""Return the tensor product channel.
Args:
other (QuantumChannel): a quantum channel subclass.
reverse (bool): If False return self ⊗ other, if True return
if True return (other ⊗ self) [Default: False]
Returns:
Stinespring: the tensor product channel as a Stinespring object.
Raises:
QiskitError: if other cannot be converted to a channel.
"""
# Convert other to Stinespring
if not isinstance(other, Stinespring):
other = Stinespring(other)
# Tensor stinespring ops
sa_l, sa_r = self._data
sb_l, sb_r = other._data
# Reshuffle tensor dimensions
din_a, dout_a = self.dim
din_b, dout_b = other.dim
dtr_a = sa_l.shape[0] // dout_a
dtr_b = sb_l.shape[0] // dout_b
if reverse:
shape_in = (dout_b, dtr_b, dout_a, dtr_a, din_b * din_a)
shape_out = (dout_b * dtr_b * dout_a * dtr_a, din_b * din_a)
else:
shape_in = (dout_a, dtr_a, dout_b, dtr_b, din_a * din_b)
shape_out = (dout_a * dtr_a * dout_b * dtr_b, din_a * din_b)
# Compute left stinepsring op
if reverse:
input_dims = self.input_dims() + other.input_dims()
output_dims = self.output_dims() + other.output_dims()
sab_l = np.kron(sb_l, sa_l)
else:
input_dims = other.input_dims() + self.input_dims()
output_dims = other.output_dims() + self.output_dims()
sab_l = np.kron(sa_l, sb_l)
# Reravel indicies
sab_l = np.reshape(
np.transpose(np.reshape(sab_l, shape_in), (0, 2, 1, 3, 4)),
shape_out)
# Compute right stinespring op
if sa_r is None and sb_r is None:
sab_r = None
else:
if sa_r is None:
sa_r = sa_l
elif sb_r is None:
sb_r = sb_l
if reverse:
sab_r = np.kron(sb_r, sa_r)
else:
sab_r = np.kron(sa_r, sb_r)
# Reravel indicies
sab_r = np.reshape(
np.transpose(np.reshape(sab_r, shape_in), (0, 2, 1, 3, 4)),
shape_out)
return Stinespring((sab_l, sab_r), input_dims, output_dims)
|
Runs the BasicSwap pass on dag. Args: dag ( DAGCircuit ): DAG to map.
|
def run(self, dag):
"""
Runs the BasicSwap pass on `dag`.
Args:
dag (DAGCircuit): DAG to map.
Returns:
DAGCircuit: A mapped DAG.
Raises:
TranspilerError: if the coupling map or the layout are not
compatible with the DAG
"""
new_dag = DAGCircuit()
if self.initial_layout is None:
if self.property_set["layout"]:
self.initial_layout = self.property_set["layout"]
else:
self.initial_layout = Layout.generate_trivial_layout(*dag.qregs.values())
if len(dag.qubits()) != len(self.initial_layout):
raise TranspilerError('The layout does not match the amount of qubits in the DAG')
if len(self.coupling_map.physical_qubits) != len(self.initial_layout):
raise TranspilerError(
"Mappers require to have the layout to be the same size as the coupling map")
current_layout = self.initial_layout.copy()
for layer in dag.serial_layers():
subdag = layer['graph']
for gate in subdag.twoQ_gates():
physical_q0 = current_layout[gate.qargs[0]]
physical_q1 = current_layout[gate.qargs[1]]
if self.coupling_map.distance(physical_q0, physical_q1) != 1:
# Insert a new layer with the SWAP(s).
swap_layer = DAGCircuit()
path = self.coupling_map.shortest_undirected_path(physical_q0, physical_q1)
for swap in range(len(path) - 2):
connected_wire_1 = path[swap]
connected_wire_2 = path[swap + 1]
qubit_1 = current_layout[connected_wire_1]
qubit_2 = current_layout[connected_wire_2]
# create qregs
for qreg in current_layout.get_registers():
if qreg not in swap_layer.qregs.values():
swap_layer.add_qreg(qreg)
# create the swap operation
swap_layer.apply_operation_back(SwapGate(),
qargs=[qubit_1, qubit_2],
cargs=[])
# layer insertion
edge_map = current_layout.combine_into_edge_map(self.initial_layout)
new_dag.compose_back(swap_layer, edge_map)
# update current_layout
for swap in range(len(path) - 2):
current_layout.swap(path[swap], path[swap + 1])
edge_map = current_layout.combine_into_edge_map(self.initial_layout)
new_dag.extend_back(subdag, edge_map)
return new_dag
|
Find a swap circuit that implements a permutation for this layer.
|
def _layer_permutation(layer_partition, initial_layout, layout, qubit_subset,
coupling, trials, qregs, rng):
"""Find a swap circuit that implements a permutation for this layer.
Args:
layer_partition (list): The layer_partition is a list of (qu)bit
lists and each qubit is a tuple (qreg, index).
initial_layout (Layout): The initial layout passed.
layout (Layout): The layout is a Layout object mapping virtual
qubits in the input circuit to physical qubits in the coupling
graph. It reflects the current positions of the data.
qubit_subset (list): The qubit_subset is the set of qubits in
the coupling graph that we have chosen to map into, as tuples
(Register, index).
coupling (CouplingMap): Directed graph representing a coupling map.
This coupling map should be one that was provided to the
stochastic mapper.
trials (int): Number of attempts the randomized algorithm makes.
qregs (OrderedDict): Ordered dict of registers from input DAG.
rng (RandomState): Random number generator.
Returns:
Tuple: success_flag, best_circuit, best_depth, best_layout, trivial_flag
Raises:
TranspilerError: if anything went wrong.
"""
logger.debug("layer_permutation: layer_partition = %s",
pformat(layer_partition))
logger.debug("layer_permutation: layout = %s",
pformat(layout.get_virtual_bits()))
logger.debug("layer_permutation: qubit_subset = %s",
pformat(qubit_subset))
logger.debug("layer_permutation: trials = %s", trials)
gates = [] # list of lists of tuples [[(register, index), ...], ...]
for gate_args in layer_partition:
if len(gate_args) > 2:
raise TranspilerError("Layer contains > 2-qubit gates")
elif len(gate_args) == 2:
gates.append(tuple(gate_args))
logger.debug("layer_permutation: gates = %s", pformat(gates))
# Can we already apply the gates? If so, there is no work to do.
dist = sum([coupling.distance(layout[g[0]], layout[g[1]])
for g in gates])
logger.debug("layer_permutation: distance = %s", dist)
if dist == len(gates):
logger.debug("layer_permutation: nothing to do")
circ = DAGCircuit()
for register in layout.get_virtual_bits().keys():
if register[0] not in circ.qregs.values():
circ.add_qreg(register[0])
return True, circ, 0, layout, (not bool(gates))
# Begin loop over trials of randomized algorithm
num_qubits = len(layout)
best_depth = inf # initialize best depth
best_edges = None # best edges found
best_circuit = None # initialize best swap circuit
best_layout = None # initialize best final layout
cdist2 = coupling._dist_matrix**2
# Scaling matrix
scale = np.zeros((num_qubits, num_qubits))
int_qubit_subset = regtuple_to_numeric(qubit_subset, qregs)
int_gates = gates_to_idx(gates, qregs)
int_layout = nlayout_from_layout(layout, qregs, coupling.size())
trial_circuit = DAGCircuit() # SWAP circuit for this trial
for register in layout.get_virtual_bits().keys():
if register[0] not in trial_circuit.qregs.values():
trial_circuit.add_qreg(register[0])
slice_circuit = DAGCircuit() # circuit for this swap slice
for register in layout.get_virtual_bits().keys():
if register[0] not in slice_circuit.qregs.values():
slice_circuit.add_qreg(register[0])
edges = np.asarray(coupling.get_edges(), dtype=np.int32).ravel()
cdist = coupling._dist_matrix
for trial in range(trials):
logger.debug("layer_permutation: trial %s", trial)
# This is one Trial --------------------------------------
dist, optim_edges, trial_layout, depth_step = swap_trial(num_qubits, int_layout,
int_qubit_subset,
int_gates, cdist2,
cdist, edges, scale,
rng)
logger.debug("layer_permutation: final distance for this trial = %s", dist)
if dist == len(gates) and depth_step < best_depth:
logger.debug("layer_permutation: got circuit with improved depth %s",
depth_step)
best_edges = optim_edges
best_layout = trial_layout
best_depth = min(best_depth, depth_step)
# Break out of trial loop if we found a depth 1 circuit
# since we can't improve it further
if best_depth == 1:
break
# If we have no best circuit for this layer, all of the
# trials have failed
if best_layout is None:
logger.debug("layer_permutation: failed!")
return False, None, None, None, False
edgs = best_edges.edges()
for idx in range(best_edges.size//2):
slice_circuit.apply_operation_back(
SwapGate(), [initial_layout[edgs[2*idx]], initial_layout[edgs[2*idx+1]]], [])
trial_circuit.extend_back(slice_circuit)
best_circuit = trial_circuit
# Otherwise, we return our result for this layer
logger.debug("layer_permutation: success!")
best_lay = best_layout.to_layout(qregs)
return True, best_circuit, best_depth, best_lay, False
|
Takes ( QuantumRegister int ) tuples and converts them into an integer array.
|
def regtuple_to_numeric(items, qregs):
"""Takes (QuantumRegister, int) tuples and converts
them into an integer array.
Args:
items (list): List of tuples of (QuantumRegister, int)
to convert.
qregs (dict): List of )QuantumRegister, int) tuples.
Returns:
ndarray: Array of integers.
"""
sizes = [qr.size for qr in qregs.values()]
reg_idx = np.cumsum([0]+sizes)
regint = {}
for ind, qreg in enumerate(qregs.values()):
regint[qreg] = ind
out = np.zeros(len(items), dtype=np.int32)
for idx, val in enumerate(items):
out[idx] = reg_idx[regint[val[0]]]+val[1]
return out
|
Converts gate tuples into a nested list of integers.
|
def gates_to_idx(gates, qregs):
"""Converts gate tuples into a nested list of integers.
Args:
gates (list): List of (QuantumRegister, int) pairs
representing gates.
qregs (dict): List of )QuantumRegister, int) tuples.
Returns:
list: Nested list of integers for gates.
"""
sizes = [qr.size for qr in qregs.values()]
reg_idx = np.cumsum([0]+sizes)
regint = {}
for ind, qreg in enumerate(qregs.values()):
regint[qreg] = ind
out = np.zeros(2*len(gates), dtype=np.int32)
for idx, gate in enumerate(gates):
out[2*idx] = reg_idx[regint[gate[0][0]]]+gate[0][1]
out[2*idx+1] = reg_idx[regint[gate[1][0]]]+gate[1][1]
return out
|
Run the StochasticSwap pass on dag.
|
def run(self, dag):
"""
Run the StochasticSwap pass on `dag`.
Args:
dag (DAGCircuit): DAG to map.
Returns:
DAGCircuit: A mapped DAG.
Raises:
TranspilerError: if the coupling map or the layout are not
compatible with the DAG
"""
if self.initial_layout is None:
if self.property_set["layout"]:
self.initial_layout = self.property_set["layout"]
else:
self.initial_layout = Layout.generate_trivial_layout(*dag.qregs.values())
if len(dag.qubits()) != len(self.initial_layout):
raise TranspilerError('The layout does not match the amount of qubits in the DAG')
if len(self.coupling_map.physical_qubits) != len(self.initial_layout):
raise TranspilerError(
"Mappers require to have the layout to be the same size as the coupling map")
self.input_layout = self.initial_layout.copy()
self.qregs = dag.qregs
if self.seed is None:
self.seed = np.random.randint(0, np.iinfo(np.int32).max)
self.rng = np.random.RandomState(self.seed)
logger.debug("StochasticSwap RandomState seeded with seed=%s", self.seed)
new_dag = self._mapper(dag, self.coupling_map, trials=self.trials)
# self.property_set["layout"] = self.initial_layout
return new_dag
|
Find a swap circuit that implements a permutation for this layer.
|
def _layer_permutation(self, layer_partition, layout, qubit_subset,
coupling, trials):
"""Find a swap circuit that implements a permutation for this layer.
The goal is to swap qubits such that qubits in the same two-qubit gates
are adjacent.
Based on S. Bravyi's algorithm.
layer_partition (list): The layer_partition is a list of (qu)bit
lists and each qubit is a tuple (qreg, index).
layout (Layout): The layout is a Layout object mapping virtual
qubits in the input circuit to physical qubits in the coupling
graph. It reflects the current positions of the data.
qubit_subset (list): The qubit_subset is the set of qubits in
the coupling graph that we have chosen to map into, as tuples
(Register, index).
coupling (CouplingMap): Directed graph representing a coupling map.
This coupling map should be one that was provided to the
stochastic mapper.
trials (int): Number of attempts the randomized algorithm makes.
Returns:
Tuple: success_flag, best_circuit, best_depth, best_layout, trivial_flag
If success_flag is True, then best_circuit contains a DAGCircuit with
the swap circuit, best_depth contains the depth of the swap circuit,
and best_layout contains the new positions of the data qubits after the
swap circuit has been applied. The trivial_flag is set if the layer
has no multi-qubit gates.
Raises:
TranspilerError: if anything went wrong.
"""
return _layer_permutation(layer_partition, self.initial_layout,
layout, qubit_subset,
coupling, trials,
self.qregs, self.rng)
|
Provide a DAGCircuit for a new mapped layer.
|
def _layer_update(self, i, first_layer, best_layout, best_depth,
best_circuit, layer_list):
"""Provide a DAGCircuit for a new mapped layer.
i (int) = layer number
first_layer (bool) = True if this is the first layer in the
circuit with any multi-qubit gates
best_layout (Layout) = layout returned from _layer_permutation
best_depth (int) = depth returned from _layer_permutation
best_circuit (DAGCircuit) = swap circuit returned
from _layer_permutation
layer_list (list) = list of DAGCircuit objects for each layer,
output of DAGCircuit layers() method
Return a DAGCircuit object to append to the output DAGCircuit
that the _mapper method is building.
"""
layout = best_layout
logger.debug("layer_update: layout = %s", pformat(layout))
logger.debug("layer_update: self.initial_layout = %s", pformat(self.initial_layout))
dagcircuit_output = DAGCircuit()
for register in layout.get_virtual_bits().keys():
if register[0] not in dagcircuit_output.qregs.values():
dagcircuit_output.add_qreg(register[0])
# If this is the first layer with multi-qubit gates,
# output all layers up to this point and ignore any
# swap gates. Set the initial layout.
if first_layer:
logger.debug("layer_update: first multi-qubit gate layer")
# Output all layers up to this point
for j in range(i + 1):
# Make qubit edge map and extend by classical bits
edge_map = layout.combine_into_edge_map(self.initial_layout)
for bit in dagcircuit_output.clbits():
edge_map[bit] = bit
dagcircuit_output.compose_back(layer_list[j]["graph"], edge_map)
# Otherwise, we output the current layer and the associated swap gates.
else:
# Output any swaps
if best_depth > 0:
logger.debug("layer_update: there are swaps in this layer, "
"depth %d", best_depth)
dagcircuit_output.extend_back(best_circuit)
else:
logger.debug("layer_update: there are no swaps in this layer")
# Make qubit edge map and extend by classical bits
edge_map = layout.combine_into_edge_map(self.initial_layout)
for bit in dagcircuit_output.clbits():
edge_map[bit] = bit
# Output this layer
dagcircuit_output.compose_back(layer_list[i]["graph"], edge_map)
return dagcircuit_output
|
Map a DAGCircuit onto a CouplingMap using swap gates.
|
def _mapper(self, circuit_graph, coupling_graph,
trials=20):
"""Map a DAGCircuit onto a CouplingMap using swap gates.
Use self.initial_layout for the initial layout.
Args:
circuit_graph (DAGCircuit): input DAG circuit
coupling_graph (CouplingMap): coupling graph to map onto
trials (int): number of trials.
Returns:
DAGCircuit: object containing a circuit equivalent to
circuit_graph that respects couplings in coupling_graph
Layout: a layout object mapping qubits of circuit_graph into
qubits of coupling_graph. The layout may differ from the
initial_layout if the first layer of gates cannot be
executed on the initial_layout, since in this case
it is more efficient to modify the layout instead of swapping
Dict: a final-layer qubit permutation
Raises:
TranspilerError: if there was any error during the mapping
or with the parameters.
"""
# Schedule the input circuit by calling layers()
layerlist = list(circuit_graph.layers())
logger.debug("schedule:")
for i, v in enumerate(layerlist):
logger.debug(" %d: %s", i, v["partition"])
if self.initial_layout is not None:
qubit_subset = self.initial_layout.get_virtual_bits().keys()
else:
# Supply a default layout for this dag
self.initial_layout = Layout()
physical_qubit = 0
for qreg in circuit_graph.qregs.values():
for index in range(qreg.size):
self.initial_layout[(qreg, index)] = physical_qubit
physical_qubit += 1
qubit_subset = self.initial_layout.get_virtual_bits().keys()
# Restrict the coupling map to the image of the layout
coupling_graph = coupling_graph.subgraph(
self.initial_layout.get_physical_bits().keys())
if coupling_graph.size() < len(self.initial_layout):
raise TranspilerError("Coupling map too small for default layout")
self.input_layout = self.initial_layout.copy()
# Find swap circuit to preceed to each layer of input circuit
layout = self.initial_layout.copy()
# Construct an empty DAGCircuit with the same set of
# qregs and cregs as the input circuit
dagcircuit_output = DAGCircuit()
dagcircuit_output.name = circuit_graph.name
for qreg in circuit_graph.qregs.values():
dagcircuit_output.add_qreg(qreg)
for creg in circuit_graph.cregs.values():
dagcircuit_output.add_creg(creg)
# Make a trivial wire mapping between the subcircuits
# returned by _layer_update and the circuit we build
identity_wire_map = {}
for qubit in circuit_graph.qubits():
identity_wire_map[qubit] = qubit
for bit in circuit_graph.clbits():
identity_wire_map[bit] = bit
first_layer = True # True until first layer is output
logger.debug("initial_layout = %s", layout)
# Iterate over layers
for i, layer in enumerate(layerlist):
# Attempt to find a permutation for this layer
success_flag, best_circuit, best_depth, best_layout, trivial_flag \
= self._layer_permutation(layer["partition"], layout,
qubit_subset, coupling_graph,
trials)
logger.debug("mapper: layer %d", i)
logger.debug("mapper: success_flag=%s,best_depth=%s,trivial_flag=%s",
success_flag, str(best_depth), trivial_flag)
# If this fails, try one gate at a time in this layer
if not success_flag:
logger.debug("mapper: failed, layer %d, "
"retrying sequentially", i)
serial_layerlist = list(layer["graph"].serial_layers())
# Go through each gate in the layer
for j, serial_layer in enumerate(serial_layerlist):
success_flag, best_circuit, best_depth, best_layout, trivial_flag = \
self._layer_permutation(
serial_layer["partition"],
layout, qubit_subset,
coupling_graph,
trials)
logger.debug("mapper: layer %d, sublayer %d", i, j)
logger.debug("mapper: success_flag=%s,best_depth=%s,"
"trivial_flag=%s",
success_flag, str(best_depth), trivial_flag)
# Give up if we fail again
if not success_flag:
raise TranspilerError("swap mapper failed: " +
"layer %d, sublayer %d" % (i, j))
# If this layer is only single-qubit gates,
# and we have yet to see multi-qubit gates,
# continue to the next inner iteration
if trivial_flag and first_layer:
logger.debug("mapper: skip to next sublayer")
continue
if first_layer:
self.initial_layout = layout
# Update the record of qubit positions
# for each inner iteration
layout = best_layout
# Update the DAG
dagcircuit_output.extend_back(
self._layer_update(j,
first_layer,
best_layout,
best_depth,
best_circuit,
serial_layerlist),
identity_wire_map)
if first_layer:
first_layer = False
else:
# Update the record of qubit positions for each iteration
layout = best_layout
if first_layer:
self.initial_layout = layout
# Update the DAG
dagcircuit_output.extend_back(
self._layer_update(i,
first_layer,
best_layout,
best_depth,
best_circuit,
layerlist),
identity_wire_map)
if first_layer:
first_layer = False
# This is the final edgemap. We might use it to correctly replace
# any measurements that needed to be removed earlier.
logger.debug("mapper: self.initial_layout = %s", pformat(self.initial_layout))
logger.debug("mapper: layout = %s", pformat(layout))
last_edgemap = layout.combine_into_edge_map(self.initial_layout)
logger.debug("mapper: last_edgemap = %s", pformat(last_edgemap))
# If first_layer is still set, the circuit only has single-qubit gates
# so we can use the initial layout to output the entire circuit
# This code is dead due to changes to first_layer above.
if first_layer:
logger.debug("mapper: first_layer flag still set")
layout = self.initial_layout
for i, layer in enumerate(layerlist):
edge_map = layout.combine_into_edge_map(self.initial_layout)
dagcircuit_output.compose_back(layer["graph"], edge_map)
return dagcircuit_output
|
Return the Pauli group with 4^n elements.
|
def pauli_group(number_of_qubits, case='weight'):
"""Return the Pauli group with 4^n elements.
The phases have been removed.
case 'weight' is ordered by Pauli weights and
case 'tensor' is ordered by I,X,Y,Z counting lowest qubit fastest.
Args:
number_of_qubits (int): number of qubits
case (str): determines ordering of group elements ('weight' or 'tensor')
Returns:
list: list of Pauli objects
Raises:
QiskitError: case is not 'weight' or 'tensor'
QiskitError: number_of_qubits is larger than 4
"""
if number_of_qubits < 5:
temp_set = []
if case == 'weight':
tmp = pauli_group(number_of_qubits, case='tensor')
# sort on the weight of the Pauli operator
return sorted(tmp, key=lambda x: -np.count_nonzero(
np.array(x.to_label(), 'c') == b'I'))
elif case == 'tensor':
# the Pauli set is in tensor order II IX IY IZ XI ...
for k in range(4 ** number_of_qubits):
z = np.zeros(number_of_qubits, dtype=np.bool)
x = np.zeros(number_of_qubits, dtype=np.bool)
# looping over all the qubits
for j in range(number_of_qubits):
# making the Pauli for each j fill it in from the
# end first
element = (k // (4 ** j)) % 4
if element == 1:
x[j] = True
elif element == 2:
z[j] = True
x[j] = True
elif element == 3:
z[j] = True
temp_set.append(Pauli(z, x))
return temp_set
else:
raise QiskitError("Only support 'weight' or 'tensor' cases "
"but you have {}.".format(case))
raise QiskitError("Only support number of qubits is less than 5")
|
r Take pauli string to construct pauli.
|
def from_label(cls, label):
r"""Take pauli string to construct pauli.
The qubit index of pauli label is q_{n-1} ... q_0.
E.g., a pauli is $P_{n-1} \otimes ... \otimes P_0$
Args:
label (str): pauli label
Returns:
Pauli: the constructed pauli
Raises:
QiskitError: invalid character in the label
"""
z = np.zeros(len(label), dtype=np.bool)
x = np.zeros(len(label), dtype=np.bool)
for i, char in enumerate(label):
if char == 'X':
x[-i - 1] = True
elif char == 'Z':
z[-i - 1] = True
elif char == 'Y':
z[-i - 1] = True
x[-i - 1] = True
elif char != 'I':
raise QiskitError("Pauli string must be only consisted of 'I', 'X', "
"'Y' or 'Z' but you have {}.".format(char))
return cls(z=z, x=x)
|
Construct pauli from boolean array.
|
def _init_from_bool(self, z, x):
"""Construct pauli from boolean array.
Args:
z (numpy.ndarray): boolean, z vector
x (numpy.ndarray): boolean, x vector
Returns:
Pauli: self
Raises:
QiskitError: if z or x are None or the length of z and x are different.
"""
if z is None:
raise QiskitError("z vector must not be None.")
if x is None:
raise QiskitError("x vector must not be None.")
if len(z) != len(x):
raise QiskitError("length of z and x vectors must be "
"the same. (z: {} vs x: {})".format(len(z), len(x)))
z = _make_np_bool(z)
x = _make_np_bool(x)
self._z = z
self._x = x
return self
|
r Multiply two Paulis and track the phase.
|
def sgn_prod(p1, p2):
r"""
Multiply two Paulis and track the phase.
$P_3 = P_1 \otimes P_2$: X*Y
Args:
p1 (Pauli): pauli 1
p2 (Pauli): pauli 2
Returns:
Pauli: the multiplied pauli
complex: the sign of the multiplication, 1, -1, 1j or -1j
"""
phase = Pauli._prod_phase(p1, p2)
new_pauli = p1 * p2
return new_pauli, phase
|
r Convert Pauli to a sparse matrix representation ( CSR format ).
|
def to_spmatrix(self):
r"""
Convert Pauli to a sparse matrix representation (CSR format).
Order is q_{n-1} .... q_0, i.e., $P_{n-1} \otimes ... P_0$
Returns:
scipy.sparse.csr_matrix: a sparse matrix with CSR format that
represnets the pauli.
"""
mat = sparse.coo_matrix(1)
for z, x in zip(self._z, self._x):
if not z and not x: # I
mat = sparse.bmat([[mat, None], [None, mat]], format='coo')
elif z and not x: # Z
mat = sparse.bmat([[mat, None], [None, -mat]], format='coo')
elif not z and x: # X
mat = sparse.bmat([[None, mat], [mat, None]], format='coo')
else: # Y
mat = mat * 1j
mat = sparse.bmat([[None, -mat], [mat, None]], format='coo')
return mat.tocsr()
|
Convert to Operator object.
|
def to_operator(self):
"""Convert to Operator object."""
# Place import here to avoid cyclic import from circuit visualization
from qiskit.quantum_info.operators.operator import Operator
return Operator(self.to_matrix())
|
Convert to Pauli circuit instruction.
|
def to_instruction(self):
"""Convert to Pauli circuit instruction."""
from qiskit.circuit import QuantumCircuit, QuantumRegister
from qiskit.extensions.standard import IdGate, XGate, YGate, ZGate
gates = {'I': IdGate(), 'X': XGate(), 'Y': YGate(), 'Z': ZGate()}
label = self.to_label()
n_qubits = self.numberofqubits
qreg = QuantumRegister(n_qubits)
circuit = QuantumCircuit(qreg, name='Pauli:{}'.format(label))
for i, pauli in enumerate(reversed(label)):
circuit.append(gates[pauli], [qreg[i]])
return circuit.to_instruction()
|
Update partial or entire z.
|
def update_z(self, z, indices=None):
"""
Update partial or entire z.
Args:
z (numpy.ndarray or list): to-be-updated z
indices (numpy.ndarray or list or optional): to-be-updated qubit indices
Returns:
Pauli: self
Raises:
QiskitError: when updating whole z, the number of qubits must be the same.
"""
z = _make_np_bool(z)
if indices is None:
if len(self._z) != len(z):
raise QiskitError("During updating whole z, you can not "
"change the number of qubits.")
self._z = z
else:
if not isinstance(indices, list) and not isinstance(indices, np.ndarray):
indices = [indices]
for p, idx in enumerate(indices):
self._z[idx] = z[p]
return self
|
Update partial or entire x.
|
def update_x(self, x, indices=None):
"""
Update partial or entire x.
Args:
x (numpy.ndarray or list): to-be-updated x
indices (numpy.ndarray or list or optional): to-be-updated qubit indices
Returns:
Pauli: self
Raises:
QiskitError: when updating whole x, the number of qubits must be the same.
"""
x = _make_np_bool(x)
if indices is None:
if len(self._x) != len(x):
raise QiskitError("During updating whole x, you can not change "
"the number of qubits.")
self._x = x
else:
if not isinstance(indices, list) and not isinstance(indices, np.ndarray):
indices = [indices]
for p, idx in enumerate(indices):
self._x[idx] = x[p]
return self
|
Insert or append pauli to the targeted indices.
|
def insert_paulis(self, indices=None, paulis=None, pauli_labels=None):
"""
Insert or append pauli to the targeted indices.
If indices is None, it means append at the end.
Args:
indices (list[int]): the qubit indices to be inserted
paulis (Pauli): the to-be-inserted or appended pauli
pauli_labels (list[str]): the to-be-inserted or appended pauli label
Note:
the indices refers to the localion of original paulis,
e.g. if indices = [0, 2], pauli_labels = ['Z', 'I'] and original pauli = 'ZYXI'
the pauli will be updated to ZY'I'XI'Z'
'Z' and 'I' are inserted before the qubit at 0 and 2.
Returns:
Pauli: self
Raises:
QiskitError: provide both `paulis` and `pauli_labels` at the same time
"""
if pauli_labels is not None:
if paulis is not None:
raise QiskitError("Please only provide either `paulis` or `pauli_labels`")
if isinstance(pauli_labels, str):
pauli_labels = list(pauli_labels)
# since pauli label is in reversed order.
paulis = Pauli.from_label(pauli_labels[::-1])
if indices is None: # append
self._z = np.concatenate((self._z, paulis.z))
self._x = np.concatenate((self._x, paulis.x))
else:
if not isinstance(indices, list):
indices = [indices]
self._z = np.insert(self._z, indices, paulis.z)
self._x = np.insert(self._x, indices, paulis.x)
return self
|
Append pauli at the end.
|
def append_paulis(self, paulis=None, pauli_labels=None):
"""
Append pauli at the end.
Args:
paulis (Pauli): the to-be-inserted or appended pauli
pauli_labels (list[str]): the to-be-inserted or appended pauli label
Returns:
Pauli: self
"""
return self.insert_paulis(None, paulis=paulis, pauli_labels=pauli_labels)
|
Delete pauli at the indices.
|
def delete_qubits(self, indices):
"""
Delete pauli at the indices.
Args:
indices(list[int]): the indices of to-be-deleted paulis
Returns:
Pauli: self
"""
if not isinstance(indices, list):
indices = [indices]
self._z = np.delete(self._z, indices)
self._x = np.delete(self._x, indices)
return self
|
Return a random Pauli on number of qubits.
|
def random(cls, num_qubits, seed=None):
"""Return a random Pauli on number of qubits.
Args:
num_qubits (int): the number of qubits
seed (int): Optional. To set a random seed.
Returns:
Pauli: the random pauli
"""
if seed is not None:
np.random.seed(seed)
z = np.random.randint(2, size=num_qubits).astype(np.bool)
x = np.random.randint(2, size=num_qubits).astype(np.bool)
return cls(z, x)
|
Generate single qubit pauli at index with pauli_label with length num_qubits.
|
def pauli_single(cls, num_qubits, index, pauli_label):
"""
Generate single qubit pauli at index with pauli_label with length num_qubits.
Args:
num_qubits (int): the length of pauli
index (int): the qubit index to insert the single qubii
pauli_label (str): pauli
Returns:
Pauli: single qubit pauli
"""
tmp = Pauli.from_label(pauli_label)
z = np.zeros(num_qubits, dtype=np.bool)
x = np.zeros(num_qubits, dtype=np.bool)
z[index] = tmp.z[0]
x[index] = tmp.x[0]
return cls(z, x)
|
Apply an arbitrary 1 - qubit unitary matrix.
|
def _add_unitary_single(self, gate, qubit):
"""Apply an arbitrary 1-qubit unitary matrix.
Args:
gate (matrix_like): a single qubit gate matrix
qubit (int): the qubit to apply gate to
"""
# Compute einsum index string for 1-qubit matrix multiplication
indexes = einsum_vecmul_index([qubit], self._number_of_qubits)
# Convert to complex rank-2 tensor
gate_tensor = np.array(gate, dtype=complex)
# Apply matrix multiplication
self._statevector = np.einsum(indexes, gate_tensor,
self._statevector,
dtype=complex,
casting='no')
|
Apply a two - qubit unitary matrix.
|
def _add_unitary_two(self, gate, qubit0, qubit1):
"""Apply a two-qubit unitary matrix.
Args:
gate (matrix_like): a the two-qubit gate matrix
qubit0 (int): gate qubit-0
qubit1 (int): gate qubit-1
"""
# Compute einsum index string for 1-qubit matrix multiplication
indexes = einsum_vecmul_index([qubit0, qubit1], self._number_of_qubits)
# Convert to complex rank-4 tensor
gate_tensor = np.reshape(np.array(gate, dtype=complex), 4 * [2])
# Apply matrix multiplication
self._statevector = np.einsum(indexes, gate_tensor,
self._statevector,
dtype=complex,
casting='no')
|
Simulate the outcome of measurement of a qubit.
|
def _get_measure_outcome(self, qubit):
"""Simulate the outcome of measurement of a qubit.
Args:
qubit (int): the qubit to measure
Return:
tuple: pair (outcome, probability) where outcome is '0' or '1' and
probability is the probability of the returned outcome.
"""
# Axis for numpy.sum to compute probabilities
axis = list(range(self._number_of_qubits))
axis.remove(self._number_of_qubits - 1 - qubit)
probabilities = np.sum(np.abs(self._statevector) ** 2, axis=tuple(axis))
# Compute einsum index string for 1-qubit matrix multiplication
random_number = self._local_random.rand()
if random_number < probabilities[0]:
return '0', probabilities[0]
# Else outcome was '1'
return '1', probabilities[1]
|
Generate memory samples from current statevector.
|
def _add_sample_measure(self, measure_params, num_samples):
"""Generate memory samples from current statevector.
Args:
measure_params (list): List of (qubit, cmembit) values for
measure instructions to sample.
num_samples (int): The number of memory samples to generate.
Returns:
list: A list of memory values in hex format.
"""
# Get unique qubits that are actually measured
measured_qubits = list({qubit for qubit, cmembit in measure_params})
num_measured = len(measured_qubits)
# Axis for numpy.sum to compute probabilities
axis = list(range(self._number_of_qubits))
for qubit in reversed(measured_qubits):
# Remove from largest qubit to smallest so list position is correct
# with respect to position from end of the list
axis.remove(self._number_of_qubits - 1 - qubit)
probabilities = np.reshape(np.sum(np.abs(self._statevector) ** 2,
axis=tuple(axis)),
2 ** num_measured)
# Generate samples on measured qubits
samples = self._local_random.choice(range(2 ** num_measured),
num_samples, p=probabilities)
# Convert to bit-strings
memory = []
for sample in samples:
classical_memory = self._classical_memory
for count, (qubit, cmembit) in enumerate(sorted(measure_params)):
qubit_outcome = int((sample & (1 << count)) >> count)
membit = 1 << cmembit
classical_memory = (classical_memory & (~membit)) | (qubit_outcome << cmembit)
value = bin(classical_memory)[2:]
memory.append(hex(int(value, 2)))
return memory
|
Apply a measure instruction to a qubit.
|
def _add_qasm_measure(self, qubit, cmembit, cregbit=None):
"""Apply a measure instruction to a qubit.
Args:
qubit (int): qubit is the qubit measured.
cmembit (int): is the classical memory bit to store outcome in.
cregbit (int, optional): is the classical register bit to store outcome in.
"""
# get measure outcome
outcome, probability = self._get_measure_outcome(qubit)
# update classical state
membit = 1 << cmembit
self._classical_memory = (self._classical_memory & (~membit)) | (int(outcome) << cmembit)
if cregbit is not None:
regbit = 1 << cregbit
self._classical_register = \
(self._classical_register & (~regbit)) | (int(outcome) << cregbit)
# update quantum state
if outcome == '0':
update_diag = [[1 / np.sqrt(probability), 0], [0, 0]]
else:
update_diag = [[0, 0], [0, 1 / np.sqrt(probability)]]
# update classical state
self._add_unitary_single(update_diag, qubit)
|
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