RawFeatureVector

RawFeatureVector.add_bits(bits)

Add Bits to the circuit.

RawFeatureVector.add_calibration(gate, qubits, schedule, params=None)

Register a low-level, custom pulse definition for the given gate.

Parameters

• gate (Union[Gate, str]) – Gate information.
• qubits (Union[int, Tuple[int]]) – List of qubits to be measured.
• schedule (Schedule) – Schedule information.
• params (Optional[List[Union[float, Parameter]]]) – A list of parameters.

Raises

Exception – if the gate is of type string and params is None.

RawFeatureVector.add_register(*regs)

Add registers.

RawFeatureVector.append(instruction, qargs=None, cargs=None)

Append one or more instructions to the end of the circuit, modifying the circuit in place. Expands qargs and cargs.

Parameters

• instruction (qiskit.circuit.Instruction) – Instruction instance to append
• qargs (list(argument)) – qubits to attach instruction to
• cargs (list(argument)) – clbits to attach instruction to

Returns

a handle to the instruction that was just added

Return type

qiskit.circuit.Instruction

Raises

• CircuitError – if object passed is a subclass of Instruction
• CircuitError – if object passed is neither subclass nor an instance of Instruction

RawFeatureVector.assign_parameters(parameters, inplace=False)

Call the initialize instruction.

RawFeatureVector.barrier(*qargs)

Apply Barrier. If qargs is None, applies to all.

RawFeatureVector.bind_parameters(values)

Bind parameters.

static RawFeatureVector.cast(value, _type)

Best effort to cast value to type. Otherwise, returns the value.

RawFeatureVector.cbit_argument_conversion(clbit_representation)

Converts several classical bit representations (such as indexes, range, etc.) into a list of classical bits.

Parameters

clbit_representation (Object) – representation to expand

Returns

Where each tuple is a classical bit.

Return type

List(tuple)

RawFeatureVector.ccx(control_qubit1, control_qubit2, target_qubit, ctrl_state=None)

Apply CCXGate.

RawFeatureVector.ch(control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CHGate.

classmethod RawFeatureVector.cls_instances()

Return the current number of instances of this class, useful for auto naming.

classmethod RawFeatureVector.cls_prefix()

Return the prefix to use for auto naming.

RawFeatureVector.cnot(control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CXGate.

RawFeatureVector.combine(rhs)

DEPRECATED - Returns rhs appended to self if self contains compatible registers.

Two circuits are compatible if they contain the same registers or if they contain different registers with unique names. The returned circuit will contain all unique registers between both circuits.

Return self + rhs as a new object.

Parameters

rhs (QuantumCircuit) – The quantum circuit to append to the right hand side.

Returns

Returns a new QuantumCircuit object

Return type

QuantumCircuit

Raises

QiskitError – if the rhs circuit is not compatible

RawFeatureVector.compose(other, qubits=None, clbits=None, front=False, inplace=False, wrap=False)

Compose circuit with other circuit or instruction, optionally permuting wires.

other can be narrower or of equal width to self.

Parameters

• other (qiskit.circuit.Instruction orQuantumCircuit or BaseOperator) – (sub)circuit to compose onto self.
• qubits (list[Qubit|int]) – qubits of self to compose onto.
• clbits (list[Clbit|int]) – clbits of self to compose onto.
• front (bool) – If True, front composition will be performed (not implemented yet).
• inplace (bool) – If True, modify the object. Otherwise return composed circuit.
• wrap (bool) – If True, wraps the other circuit into a gate (or instruction, depending on whether it contains only unitary instructions) before composing it onto self.

Returns

the composed circuit (returns None if inplace==True).

Return type

QuantumCircuit

Raises

• CircuitError – if composing on the front.
• QiskitError – if other is wider or there are duplicate edge mappings.

Examples:

lhs.compose(rhs, qubits=[3, 2], inplace=True)
 
.. parsed-literal::
 
                ┌───┐                   ┌─────┐                ┌───┐
    lqr_1_0: ───┤ H ├───    rqr_0: ──■──┤ Tdg ├    lqr_1_0: ───┤ H ├───────────────
                ├───┤              ┌─┴─┐└─────┘                ├───┤
    lqr_1_1: ───┤ X ├───    rqr_1: ┤ X ├───────    lqr_1_1: ───┤ X ├───────────────
             ┌──┴───┴──┐           └───┘                    ┌──┴───┴──┐┌───┐
    lqr_1_2: ┤ U1(0.1) ├  +                     =  lqr_1_2: ┤ U1(0.1) ├┤ X ├───────
             └─────────┘                                    └─────────┘└─┬─┘┌─────┐
    lqr_2_0: ─────■─────                           lqr_2_0: ─────■───────■──┤ Tdg ├
                ┌─┴─┐                                          ┌─┴─┐        └─────┘
    lqr_2_1: ───┤ X ├───                           lqr_2_1: ───┤ X ├───────────────
                └───┘                                          └───┘
    lcr_0: 0 ═══════════                           lcr_0: 0 ═══════════════════════
 
    lcr_1: 0 ═══════════                           lcr_1: 0 ═══════════════════════

RawFeatureVector.control(num_ctrl_qubits=1, label=None, ctrl_state=None)

Control this circuit on num_ctrl_qubits qubits.

Parameters

• num_ctrl_qubits (int) – The number of control qubits.
• label (str) – An optional label to give the controlled operation for visualization.
• ctrl_state (str or int) – The control state in decimal or as a bitstring (e.g. ‘111’). If None, use 2**num_ctrl_qubits - 1.

Returns

The controlled version of this circuit.

Return type

QuantumCircuit

Raises

CircuitError – If the circuit contains a non-unitary operation and cannot be controlled.

RawFeatureVector.copy(name=None)

Copy the circuit.

Parameters

name (str) – name to be given to the copied circuit. If None, then the name stays the same

Returns

a deepcopy of the current circuit, with the specified name

Return type

QuantumCircuit

RawFeatureVector.count_ops()

Count each operation kind in the circuit.

Returns

a breakdown of how many operations of each kind, sorted by amount.

Return type

OrderedDict

RawFeatureVector.cp(theta, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CPhaseGate.

RawFeatureVector.crx(theta, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CRXGate.

RawFeatureVector.cry(theta, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CRYGate.

RawFeatureVector.crz(theta, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CRZGate.

RawFeatureVector.cswap(control_qubit, target_qubit1, target_qubit2, label=None, ctrl_state=None)

Apply CSwapGate.

RawFeatureVector.csx(control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CSXGate.

RawFeatureVector.cu(theta, phi, lam, gamma, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CUGate.

RawFeatureVector.cu1(theta, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CU1Gate.

RawFeatureVector.cu3(theta, phi, lam, control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CU3Gate.

RawFeatureVector.cx(control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CXGate.

RawFeatureVector.cy(control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CYGate.

RawFeatureVector.cz(control_qubit, target_qubit, label=None, ctrl_state=None)

Apply CZGate.

RawFeatureVector.dcx(qubit1, qubit2)

Apply DCXGate.

RawFeatureVector.decompose()

Call a decomposition pass on this circuit, to decompose one level (shallow decompose).

Returns

a circuit one level decomposed

Return type

QuantumCircuit

RawFeatureVector.delay(duration, qarg=None, unit='dt')

Apply Delay. If qarg is None, applies to all qubits. When applying to multiple qubits, delays with the same duration will be created.

Parameters

• duration (int or float or ParameterExpression) – duration of the delay.
• qarg (Object) – qubit argument to apply this delay.
• unit (str) – unit of the duration. Supported units: ‘s’, ‘ms’, ‘us’, ‘ns’, ‘ps’, ‘dt’. Default is dt, i.e. integer time unit depending on the target backend.

Returns

the attached delay instruction.

Return type

qiskit.Instruction

Raises

CircuitError – if arguments have bad format.

RawFeatureVector.depth()

Return circuit depth (i.e., length of critical path). This does not include compiler or simulator directives such as ‘barrier’ or ‘snapshot’.

Returns

Depth of circuit.

Return type

int

Notes

The circuit depth and the DAG depth need not be the same.

RawFeatureVector.diagonal(diag, qubit)

Attach a diagonal gate to a circuit.

The decomposition is based on Theorem 7 given in “Synthesis of Quantum Logic Circuits” by Shende et al. (https://arxiv.org/pdf/quant-ph/0406176.pdf).

Parameters

• diag (list) – list of the 2^k diagonal entries (for a diagonal gate on k qubits). Must contain at least two entries
• qubit (QuantumRegister|list) – list of k qubits the diagonal is acting on (the order of the qubits specifies the computational basis in which the diagonal gate is provided: the first element in diag acts on the state where all the qubits in q are in the state 0, the second entry acts on the state where all the qubits q[1],…,q[k-1] are in the state zero and q[0] is in the state 1, and so on)

Returns

the diagonal gate which was attached to the circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the list of the diagonal entries or the qubit list is in bad format; if the number of diagonal entries is not 2^k, where k denotes the number of qubits

RawFeatureVector.draw(output=None, scale=None, filename=None, style=None, interactive=False, plot_barriers=True, reverse_bits=False, justify=None, vertical_compression='medium', idle_wires=True, with_layout=True, fold=None, ax=None, initial_state=False, cregbundle=True)

Draw the quantum circuit. Use the output parameter to choose the drawing format:

text: ASCII art TextDrawing that can be printed in the console.

matplotlib: images with color rendered purely in Python.

latex: high-quality images compiled via latex.

latex_source: raw uncompiled latex output.

Parameters

• output (str) – select the output method to use for drawing the circuit. Valid choices are text, mpl, latex, latex_source. By default the text drawer is used unless the user config file (usually ~/.qiskit/settings.conf) has an alternative backend set as the default. For example, circuit_drawer = latex. If the output kwarg is set, that backend will always be used over the default in the user config file.
• scale (float) – scale of image to draw (shrink if < 1.0). Only used by the mpl, latex and latex_source outputs. Defaults to 1.0.
• filename (str) – file path to save image to. Defaults to None.
• style (dict or str) – dictionary of style or file name of style json file. This option is only used by the mpl or latex output type. If style is a str, it is used as the path to a json file which contains a style dict. The file will be opened, parsed, and then any style elements in the dict will replace the default values in the input dict. A file to be loaded must end in .json, but the name entered here can omit .json. For example, style='iqx.json' or style='iqx'. If style is a dict and the 'name' key is set, that name will be used to load a json file, followed by loading the other items in the style dict. For example, style={'name': 'iqx'}. If style is not a str and name is not a key in the style dict, then the default value from the user config file (usually ~/.qiskit/settings.conf) will be used, for example, circuit_mpl_style = iqx. If none of these are set, the default style will be used. The search path for style json files can be specified in the user config, for example, circuit_mpl_style_path = /home/user/styles:/home/user. See: DefaultStyle for more information on the contents.
• interactive (bool) – when set to 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. Defaults to False.
• reverse_bits (bool) – when set to True, reverse the bit order inside registers for the output visualization. Defaults to False.
• 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.
• vertical_compression (string) – high, medium or low. It merges the lines generated by the text output so the drawing will take less vertical room. Default is medium. Only used by the text output, will be silently ignored otherwise.
• idle_wires (bool) – include idle wires (wires with no circuit elements) in output visualization. Default is True.
• with_layout (bool) – include layout information, with labels on the physical layout. Default is True.
• fold (int) – sets pagination. It can be disabled using -1. In text, sets the length of the lines. This is useful when the drawing does not fit in the console. If None (default), it will try to guess the console width using shutil.get_terminal_size(). However, if running in jupyter, the default line length is set to 80 characters. In mpl, it is the number of (visual) layers before folding. Default is 25.
• ax (matplotlib.axes.Axes) – Only used by the mpl backend. An optional Axes object to be used for the visualization output. If none is specified, a new matplotlib Figure will be created and used. Additionally, if specified there will be no returned Figure since it is redundant.
• initial_state (bool) – optional. Adds |0> in the beginning of the wire. Default is False.
• cregbundle (bool) – optional. If set True, bundle classical registers. Default is True.

Returns

TextDrawing or matplotlib.figure or PIL.Image or str:

• TextDrawing (output=’text’)

  A drawing that can be printed as ascii art.

• matplotlib.figure.Figure (output=’mpl’)

  A matplotlib figure object for the circuit diagram.

• PIL.Image (output=’latex’)

  An in-memory representation of the image of the circuit diagram.

• str (output=’latex_source’)

  The LaTeX source code for visualizing the circuit diagram.

Raises

• VisualizationError – when an invalid output method is selected
• ImportError – when the output methods requires non-installed libraries.

Example

from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit
from qiskit.tools.visualization import circuit_drawer
q = QuantumRegister(1)
c = ClassicalRegister(1)
qc = QuantumCircuit(q, c)
qc.h(q)
qc.measure(q, c)
qc.draw(output='mpl', style={'backgroundcolor': '#EEEEEE'})

RawFeatureVector.ecr(qubit1, qubit2)

Apply ECRGate.

RawFeatureVector.extend(rhs)

DEPRECATED - Append QuantumCircuit to the RHS if it contains compatible registers.

Two circuits are compatible if they contain the same registers or if they contain different registers with unique names. The returned circuit will contain all unique registers between both circuits.

Modify and return self.

Parameters

rhs (QuantumCircuit) – The quantum circuit to append to the right hand side.

Returns

Returns this QuantumCircuit object (which has been modified)

Return type

QuantumCircuit

Raises

QiskitError – if the rhs circuit is not compatible

RawFeatureVector.fredkin(control_qubit, target_qubit1, target_qubit2)

Apply CSwapGate.

static RawFeatureVector.from_qasm_file(path)

Take in a QASM file and generate a QuantumCircuit object.

Parameters

path (str) – Path to the file for a QASM program

Returns

The QuantumCircuit object for the input QASM

Return type

QuantumCircuit

static RawFeatureVector.from_qasm_str(qasm_str)

Take in a QASM string and generate a QuantumCircuit object.

Parameters

qasm_str (str) – A QASM program string

Returns

The QuantumCircuit object for the input QASM

Return type

QuantumCircuit

RawFeatureVector.get_instructions(name)

Get instructions matching name.

Parameters

name (str) – The name of instruction to.

Returns

list of (instruction, qargs, cargs).

Return type

list(tuple)

RawFeatureVector.h(qubit)

Apply HGate.

RawFeatureVector.hamiltonian(operator, time, qubits, label=None)

Apply hamiltonian evolution to qubits.

RawFeatureVector.has_register(register)

Test if this circuit has the register r.

Parameters

register (Register) – a quantum or classical register.

Returns

True if the register is contained in this circuit.

Return type

bool

RawFeatureVector.i(qubit)

Apply IGate.

RawFeatureVector.id(qubit)

Apply IGate.

RawFeatureVector.initialize(params, qubits=None)

Initialize qubits in a specific state.

Qubit initialization is done by first resetting the qubits to $|0\rangle$ followed by an state preparing unitary. Both these steps are included in the Initialize instruction.

Parameters

• params (str or list or int) –

  • str: labels of basis states of the Pauli eigenstates Z, X, Y. See

    from_label(). Notice the order of the labels is reversed with respect to the qubit index to be applied to. Example label ‘01’ initializes the qubit zero to |1> and the qubit one to |0>.

  • list: vector of complex amplitudes to initialize to.

  • int: an integer that is used as a bitmap indicating which qubits to initialize

    to |1>. Example: setting params to 5 would initialize qubit 0 and qubit 2 to |1> and qubit 1 to |0>.

• qubits (QuantumRegister or int) –

  • QuantumRegister: A list of qubits to be initialized [Default: None].
  • int: Index of qubit to initialized [Default: None].

Returns

a handle to the instruction that was just initialized

Return type

qiskit.circuit.Instruction

Examples

Prepare a qubit in the state $(|0\rangle - |1\rangle) / \sqrt{2}$.

import numpy as np
from qiskit import QuantumCircuit
 
circuit = QuantumCircuit(1)
circuit.initialize([1/np.sqrt(2), -1/np.sqrt(2)], 0)
circuit.draw()

     ┌──────────────────────────────┐
q_0: ┤ Initialize(0.70711,-0.70711) ├
     └──────────────────────────────┘

output:

┌──────────────────────────────┐

q_0: ┤ initialize(0.70711,-0.70711) ├

└──────────────────────────────┘

Initialize from a string two qubits in the state |10>. The order of the labels is reversed with respect to qubit index. More information about labels for basis states are in from_label().

import numpy as np
from qiskit import QuantumCircuit
 
circuit = QuantumCircuit(2)
circuit.initialize('01', circuit.qubits)
circuit.draw()

     ┌──────────────────┐
q_0: ┤0                 ├
     │  Initialize(0,1) │
q_1: ┤1                 ├
     └──────────────────┘

output:

┌──────────────────┐

q_0: ┤0 ├

│ initialize(0,1) │

q_1: ┤1 ├

└──────────────────┘

Initialize two qubits from an array of complex amplitudes .. jupyter-execute:

import numpy as np
from qiskit import QuantumCircuit
 
circuit = QuantumCircuit(2)
circuit.initialize([0, 1/np.sqrt(2), -1.j/np.sqrt(2), 0], circuit.qubits)
circuit.draw()

output:

┌────────────────────────────────────┐

q_0: ┤0 ├

│ initialize(0,0.70711,-0.70711j,0) │

q_1: ┤1 ├

└────────────────────────────────────┘

RawFeatureVector.inverse()

Invert (take adjoint of) this circuit.

This is done by recursively inverting all gates.

Returns

the inverted circuit

Return type

QuantumCircuit

Raises

CircuitError – if the circuit cannot be inverted.

Examples

input:

┌───┐

q_0: ┤ H ├─────■──────

└───┘┌────┴─────┐

q_1: ─────┤ RX(1.57) ├

└──────────┘

output:

┌───┐

q_0: ──────■──────┤ H ├

┌─────┴─────┐└───┘

q_1: ┤ RX(-1.57) ├─────

└───────────┘

RawFeatureVector.iso(isometry, q_input, q_ancillas_for_output, q_ancillas_zero=None, q_ancillas_dirty=None, epsilon=1e-10)

Attach an arbitrary isometry from m to n qubits to a circuit. In particular, this allows to attach arbitrary unitaries on n qubits (m=n) or to prepare any state on n qubits (m=0). The decomposition used here was introduced by Iten et al. in https://arxiv.org/abs/1501.06911.

Parameters

• isometry (ndarray) – an isometry from m to n qubits, i.e., a (complex) ndarray of dimension 2^n×2^m with orthonormal columns (given in the computational basis specified by the order of the ancillas and the input qubits, where the ancillas are considered to be more significant than the input qubits.).
• q_input (QuantumRegister|list[Qubit]) – list of m qubits where the input to the isometry is fed in (empty list for state preparation).
• q_ancillas_for_output (QuantumRegister|list[Qubit]) – list of n-m ancilla qubits that are used for the output of the isometry and which are assumed to start in the zero state. The qubits are listed with increasing significance.
• q_ancillas_zero (QuantumRegister|list[Qubit]) – list of ancilla qubits which are assumed to start in the zero state. Default is q_ancillas_zero = None.
• q_ancillas_dirty (QuantumRegister|list[Qubit]) – list of ancilla qubits which can start in an arbitrary state. Default is q_ancillas_dirty = None.
• epsilon (float) – error tolerance of calculations. Default is epsilon = _EPS.

Returns

the isometry is attached to the quantum circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the array is not an isometry of the correct size corresponding to the provided number of qubits.

RawFeatureVector.isometry(isometry, q_input, q_ancillas_for_output, q_ancillas_zero=None, q_ancillas_dirty=None, epsilon=1e-10)

Attach an arbitrary isometry from m to n qubits to a circuit. In particular, this allows to attach arbitrary unitaries on n qubits (m=n) or to prepare any state on n qubits (m=0). The decomposition used here was introduced by Iten et al. in https://arxiv.org/abs/1501.06911.

Parameters

• isometry (ndarray) – an isometry from m to n qubits, i.e., a (complex) ndarray of dimension 2^n×2^m with orthonormal columns (given in the computational basis specified by the order of the ancillas and the input qubits, where the ancillas are considered to be more significant than the input qubits.).
• q_input (QuantumRegister|list[Qubit]) – list of m qubits where the input to the isometry is fed in (empty list for state preparation).
• q_ancillas_for_output (QuantumRegister|list[Qubit]) – list of n-m ancilla qubits that are used for the output of the isometry and which are assumed to start in the zero state. The qubits are listed with increasing significance.
• q_ancillas_zero (QuantumRegister|list[Qubit]) – list of ancilla qubits which are assumed to start in the zero state. Default is q_ancillas_zero = None.
• q_ancillas_dirty (QuantumRegister|list[Qubit]) – list of ancilla qubits which can start in an arbitrary state. Default is q_ancillas_dirty = None.
• epsilon (float) – error tolerance of calculations. Default is epsilon = _EPS.

Returns

the isometry is attached to the quantum circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the array is not an isometry of the correct size corresponding to the provided number of qubits.

RawFeatureVector.iswap(qubit1, qubit2)

Apply iSwapGate.

RawFeatureVector.mcp(lam, control_qubits, target_qubit)

Apply MCPhaseGate.

RawFeatureVector.mcrx(theta, q_controls, q_target, use_basis_gates=False)

Apply Multiple-Controlled X rotation gate

Parameters

• self (QuantumCircuit) – The QuantumCircuit object to apply the mcrx gate on.
• theta (float) – angle theta
• q_controls (list(Qubit)) – The list of control qubits
• q_target (Qubit) – The target qubit
• use_basis_gates (bool) – use p, u, cx

Raises

QiskitError – parameter errors

RawFeatureVector.mcry(theta, q_controls, q_target, q_ancillae=None, mode=None, use_basis_gates=False)

Apply Multiple-Controlled Y rotation gate

Parameters

• self (QuantumCircuit) – The QuantumCircuit object to apply the mcry gate on.
• theta (float) – angle theta
• q_controls (list(Qubit)) – The list of control qubits
• q_target (Qubit) – The target qubit
• q_ancillae (QuantumRegister or tuple(QuantumRegister, int)) – The list of ancillary qubits.
• mode (string) – The implementation mode to use
• use_basis_gates (bool) – use p, u, cx

Raises

QiskitError – parameter errors

RawFeatureVector.mcrz(lam, q_controls, q_target, use_basis_gates=False)

Apply Multiple-Controlled Z rotation gate

Parameters

• self (QuantumCircuit) – The QuantumCircuit object to apply the mcrz gate on.
• lam (float) – angle lambda
• q_controls (list(Qubit)) – The list of control qubits
• q_target (Qubit) – The target qubit
• use_basis_gates (bool) – use p, u, cx

Raises

QiskitError – parameter errors

RawFeatureVector.mct(control_qubits, target_qubit, ancilla_qubits=None, mode='noancilla')

Apply MCXGate.

RawFeatureVector.mcu1(lam, control_qubits, target_qubit)

Apply MCU1Gate.

RawFeatureVector.mcx(control_qubits, target_qubit, ancilla_qubits=None, mode='noancilla')

Apply MCXGate.

The multi-cX gate can be implemented using different techniques, which use different numbers of ancilla qubits and have varying circuit depth. These modes are: - ‘noancilla’: Requires 0 ancilla qubits. - ‘recursion’: Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0. - ‘v-chain’: Requires 2 less ancillas than the number of control qubits. - ‘v-chain-dirty’: Same as for the clean ancillas (but the circuit will be longer).

RawFeatureVector.measure(qubit, cbit)

Measure quantum bit into classical bit (tuples).

Parameters

• qubit (QuantumRegister|list|tuple) – quantum register
• cbit (ClassicalRegister|list|tuple) – classical register

Returns

the attached measure instruction.

Return type

qiskit.Instruction

Raises

CircuitError – if qubit is not in this circuit or bad format; if cbit is not in this circuit or not creg.

RawFeatureVector.measure_active(inplace=True)

Adds measurement to all non-idle qubits. Creates a new ClassicalRegister with a size equal to the number of non-idle qubits being measured.

Returns a new circuit with measurements if inplace=False.

Parameters

inplace (bool) – All measurements inplace or return new circuit.

Returns

Returns circuit with measurements when inplace = False.

Return type

QuantumCircuit

RawFeatureVector.measure_all(inplace=True)

Adds measurement to all qubits. Creates a new ClassicalRegister with a size equal to the number of qubits being measured.

Returns a new circuit with measurements if inplace=False.

Parameters

inplace (bool) – All measurements inplace or return new circuit.

Returns

Returns circuit with measurements when inplace = False.

Return type

QuantumCircuit

RawFeatureVector.ms(theta, qubits)

Apply MSGate.

RawFeatureVector.num_connected_components(unitary_only=False)

How many non-entangled subcircuits can the circuit be factored to.

Parameters

unitary_only (bool) – Compute only unitary part of graph.

Returns

Number of connected components in circuit.

Return type

int

RawFeatureVector.num_nonlocal_gates()

Return number of non-local gates (i.e. involving 2+ qubits).

Conditional nonlocal gates are also included.

RawFeatureVector.num_tensor_factors()

Computes the number of tensor factors in the unitary (quantum) part of the circuit only.

Notes

This is here for backwards compatibility, and will be removed in a future release of Qiskit. You should call num_unitary_factors instead.

RawFeatureVector.num_unitary_factors()

Computes the number of tensor factors in the unitary (quantum) part of the circuit only.

RawFeatureVector.p(theta, qubit)

Apply PhaseGate.

RawFeatureVector.pauli(pauli_string, qubits)

Apply PauliGate.

RawFeatureVector.power(power, matrix_power=False)

Raise this circuit to the power of power.

If power is a positive integer and matrix_power is False, this implementation defaults to calling repeat. Otherwise, if the circuit is unitary, the matrix is computed to calculate the matrix power.

Parameters

• power (int) – The power to raise this circuit to.
• matrix_power (bool) – If True, the circuit is converted to a matrix and then the matrix power is computed. If False, and power is a positive integer, the implementation defaults to repeat.

Raises

CircuitError – If the circuit needs to be converted to a gate but it is not unitary.

Returns

A circuit implementing this circuit raised to the power of power.

Return type

QuantumCircuit

RawFeatureVector.qasm(formatted=False, filename=None, encoding=None)

Return OpenQASM string.

Parameters

• formatted (bool) – Return formatted Qasm string.
• filename (str) – Save Qasm to file with name ‘filename’.
• encoding (str) – Optionally specify the encoding to use for the output file if filename is specified. By default this is set to the system’s default encoding (ie whatever locale.getpreferredencoding() returns) and can be set to any valid codec or alias from stdlib’s codec module

Returns

If formatted=False.

Return type

str

Raises

• MissingOptionalLibraryError – If pygments is not installed and formatted is True.
• QasmError – If circuit has free parameters.

RawFeatureVector.qbit_argument_conversion(qubit_representation)

Converts several qubit representations (such as indexes, range, etc.) into a list of qubits.

Parameters

qubit_representation (Object) – representation to expand

Returns

Where each tuple is a qubit.

Return type

List(tuple)

RawFeatureVector.qubit_duration(*qubits)

Return the duration between the start and stop time of the first and last instructions, excluding delays, over the supplied qubits. Its time unit is self.unit.

Parameters

*qubits – Qubits within self to include.

Return type

float

Returns

Return the duration between the first start and last stop time of non-delay instructions

RawFeatureVector.qubit_start_time(*qubits)

Return the start time of the first instruction, excluding delays, over the supplied qubits. Its time unit is self.unit.

Return 0 if there are no instructions over qubits

Parameters

• *qubits – Qubits within self to include. Integers are allowed for qubits, indicating
• of self.qubits. (indices) –

Return type

float

Returns

Return the start time of the first instruction, excluding delays, over the qubits

Raises

CircuitError – if self is a not-yet scheduled circuit.

RawFeatureVector.qubit_stop_time(*qubits)

Return the stop time of the last instruction, excluding delays, over the supplied qubits. Its time unit is self.unit.

Return 0 if there are no instructions over qubits

Parameters

• *qubits – Qubits within self to include. Integers are allowed for qubits, indicating
• of self.qubits. (indices) –

Return type

float

Returns

Return the stop time of the last instruction, excluding delays, over the qubits

Raises

CircuitError – if self is a not-yet scheduled circuit.

RawFeatureVector.r(theta, phi, qubit)

Apply RGate.

RawFeatureVector.rcccx(control_qubit1, control_qubit2, control_qubit3, target_qubit)

Apply RC3XGate.

RawFeatureVector.rccx(control_qubit1, control_qubit2, target_qubit)

Apply RCCXGate.

RawFeatureVector.remove_final_measurements(inplace=True)

Removes final measurement on all qubits if they are present. Deletes the ClassicalRegister that was used to store the values from these measurements if it is idle.

Returns a new circuit without measurements if inplace=False.

Parameters

inplace (bool) – All measurements removed inplace or return new circuit.

Returns

Returns circuit with measurements removed when inplace = False.

Return type

QuantumCircuit

RawFeatureVector.repeat(reps)

Repeat this circuit reps times.

Parameters

reps (int) – How often this circuit should be repeated.

Returns

A circuit containing reps repetitions of this circuit.

Return type

QuantumCircuit

RawFeatureVector.reset(qubit)

Reset q.

RawFeatureVector.reverse_bits()

Return a circuit with the opposite order of wires.

The circuit is “vertically” flipped. If a circuit is defined over multiple registers, the resulting circuit will have the same registers but with their order flipped.

This method is useful for converting a circuit written in little-endian convention to the big-endian equivalent, and vice versa.

Returns

the circuit with reversed bit order.

Return type

QuantumCircuit

Examples

input:

┌───┐

q_0: ┤ H ├─────■──────

└───┘┌────┴─────┐

q_1: ─────┤ RX(1.57) ├

└──────────┘

output:

┌──────────┐

q_0: ─────┤ RX(1.57) ├

┌───┐└────┬─────┘

q_1: ┤ H ├─────■──────

└───┘

RawFeatureVector.reverse_ops()

Reverse the circuit by reversing the order of instructions.

This is done by recursively reversing all instructions. It does not invert (adjoint) any gate.

Returns

the reversed circuit.

Return type

QuantumCircuit

Examples

input:

┌───┐

q_0: ┤ H ├─────■──────

└───┘┌────┴─────┐

q_1: ─────┤ RX(1.57) ├

└──────────┘

output:

┌───┐

q_0: ─────■──────┤ H ├

┌────┴─────┐└───┘

q_1: ┤ RX(1.57) ├─────

└──────────┘

RawFeatureVector.rv(vx, vy, vz, qubit)

Apply RVGate.

RawFeatureVector.rx(theta, qubit, label=None)

Apply RXGate.

RawFeatureVector.rxx(theta, qubit1, qubit2)

Apply RXXGate.

RawFeatureVector.ry(theta, qubit, label=None)

Apply RYGate.

RawFeatureVector.ryy(theta, qubit1, qubit2)

Apply RYYGate.

RawFeatureVector.rz(phi, qubit)

Apply RZGate.

RawFeatureVector.rzx(theta, qubit1, qubit2)

Apply RZXGate.

RawFeatureVector.rzz(theta, qubit1, qubit2)

Apply RZZGate.

RawFeatureVector.s(qubit)

Apply SGate.

RawFeatureVector.save_amplitudes(params, label='amplitudes', pershot=False, conditional=False)

Save complex statevector amplitudes.

Parameters

• params (List[int] or List[str]) – the basis states to return amplitudes for.
• label (str) – the key for retrieving saved data from results.
• pershot (bool) – if True save a list of amplitudes vectors for each shot of the simulation rather than the a single amplitude vector [Default: False].
• conditional (bool) – if True save the amplitudes vector conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if params is invalid for the specified number of qubits.

RawFeatureVector.save_amplitudes_squared(params, label='amplitudes_squared', unnormalized=False, pershot=False, conditional=False)

Save squared statevector amplitudes (probabilities).

Parameters

• params (List[int] or List[str]) – the basis states to return amplitudes for.
• label (str) – the key for retrieving saved data from results.
• unnormalized (bool) – If True return save the unnormalized accumulated probabilities over all shots [Default: False].
• pershot (bool) – if True save a list of probability vectors for each shot of the simulation rather than the a single amplitude vector [Default: False].
• conditional (bool) – if True save the probability vector conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if params is invalid for the specified number of qubits.

RawFeatureVector.save_density_matrix(qubits=None, label='density_matrix', unnormalized=False, pershot=False, conditional=False)

Save the current simulator quantum state as a density matrix.

Parameters

• qubits (list or None) – the qubits to save reduced density matrix on. If None the full density matrix of qubits will be saved [Default: None].
• label (str) – the key for retrieving saved data from results.
• unnormalized (bool) – If True return save the unnormalized accumulated or conditional accumulated density matrix over all shots [Default: False].
• pershot (bool) – if True save a list of density matrices for each shot of the simulation rather than the average over all shots [Default: False].
• conditional (bool) – if True save the average or pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_expectation_value(operator, qubits, label='expectation_value', unnormalized=False, pershot=False, conditional=False)

Save the expectation value of a Hermitian operator.

Parameters

• operator (Pauli orSparsePauliOp orOperator) – a Hermitian operator.
• qubits (list) – circuit qubits to apply instruction.
• label (str) – the key for retrieving saved data from results.
• unnormalized (bool) – If True return save the unnormalized accumulated or conditional accumulated expectation value over all shot [Default: False].
• pershot (bool) – if True save a list of expectation values for each shot of the simulation rather than the average over all shots [Default: False].
• conditional (bool) – if True save the average or pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if the input operator is invalid or not Hermitian.

RawFeatureVector.save_expectation_value_variance(operator, qubits, label='expectation_value_variance', unnormalized=False, pershot=False, conditional=False)

Save the expectation value of a Hermitian operator.

Parameters

• operator (Pauli orSparsePauliOp orOperator) – a Hermitian operator.
• qubits (list) – circuit qubits to apply instruction.
• label (str) – the key for retrieving saved data from results.
• unnormalized (bool) – If True return save the unnormalized accumulated or conditional accumulated expectation value and variance over all shot [Default: False].
• pershot (bool) – if True save a list of expectation values and variances for each shot of the simulation rather than the average over all shots [Default: False].
• conditional (bool) – if True save the data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if the input operator is invalid or not Hermitian.

RawFeatureVector.save_matrix_product_state(label='matrix_product_state', pershot=False, conditional=False)

Save the current simulator quantum state as a matrix product state.

Parameters

• label (str) – the key for retrieving saved data from results.
• pershot (bool) – if True save the mps for each shot of the simulation [Default: False].
• conditional (bool) – if True save pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_probabilities(qubits=None, label='probabilities', unnormalized=False, pershot=False, conditional=False)

Save measurement outcome probabilities vector.

Parameters

• qubits (list or None) – the qubits to apply snapshot to. If None all qubits will be snapshot [Default: None].
• label (str) – the key for retrieving saved data from results.
• unnormalized (bool) – If True return save the unnormalized accumulated probabilities over all shots [Default: False].
• pershot (bool) – if True save a list of probabilities for each shot of the simulation rather than the average over all shots [Default: False].
• conditional (bool) – if True save the probabilities data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_probabilities_dict(qubits=None, label='probabilities', unnormalized=False, pershot=False, conditional=False)

Save measurement outcome probabilities vector.

Parameters

• qubits (list or None) – the qubits to apply snapshot to. If None all qubits will be snapshot [Default: None].
• label (str) – the key for retrieving saved data from results.
• unnormalized (bool) – If True return save the unnormalized accumulated probabilities over all shots [Default: False].
• pershot (bool) – if True save a list of probabilities for each shot of the simulation rather than the average over all shots [Default: False].
• conditional (bool) – if True save the probabilities data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_stabilizer(label='stabilizer', pershot=False, conditional=False)

Save the current stabilizer simulator quantum state as a Clifford.

Parameters

• label (str) – the key for retrieving saved data from results.
• pershot (bool) – if True save a list of Cliffords for each shot of the simulation [Default: False].
• conditional (bool) – if True save pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_state(label=None, pershot=False, conditional=False)

Save the current simulator quantum state.

Parameters

• label (str or None) – Optional, the key for retrieving saved data from results. If None the key will be the state type of the simulator.
• pershot (bool) – if True save a list of statevectors for each shot of the simulation [Default: False].
• conditional (bool) – if True save pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_statevector(label='statevector', pershot=False, conditional=False)

Save the current simulator quantum state as a statevector.

Parameters

• pershot (bool) – if True save a list of statevectors for each shot of the simulation [Default: False].
• label (str) – the key for retrieving saved data from results.
• conditional (bool) – if True save pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_statevector_dict(label='statevector', pershot=False, conditional=False)

Save the current simulator quantum state as a statevector as a dict.

Parameters

• label (str) – the key for retrieving saved data from results.
• pershot (bool) – if True save a list of statevectors for each shot of the simulation [Default: False].
• conditional (bool) – if True save pershot data conditional on the current classical register values [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_superop(label='superop', pershot=False)

Save the current state of the superop simulator.

Parameters

• label (str) – the key for retrieving saved data from results.
• pershot (bool) – if True save a list of SuperOp matrices for each shot of the simulation [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.save_unitary(label='unitary', pershot=False)

Save the current state of the unitary simulator.

Parameters

• label (str) – the key for retrieving saved data from results.
• pershot (bool) – if True save a list of unitaries for each shot of the simulation [Default: False].

Returns

with attached instruction.

Return type

QuantumCircuit

RawFeatureVector.sdg(qubit)

Apply SdgGate.

RawFeatureVector.set_density_matrix(state)

Set the density matrix state of the simulator.

Parameters

state (DensityMatrix) – a density matrix.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – If the density matrix is the incorrect size for the current circuit.

RawFeatureVector.set_matrix_product_state(state)

Set the matrix product state of the simulator.

Parameters

state (Tuple[List[Tuple[np.array[complex_t]]]], List[List[float]]) – A matrix_product_state.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – If the structure of the state is incorrect

RawFeatureVector.set_stabilizer(state)

Set the Clifford stabilizer state of the simulator.

Parameters

state (Clifford) – A clifford operator.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – If the state is the incorrect size for the current circuit.

RawFeatureVector.set_statevector(state)

Set the statevector state of the simulator.

Parameters

state (Statevector) – A state matrix.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – If the state is the incorrect size for the current circuit.

RawFeatureVector.set_superop(state)

Set the superop state of the simulator.

Parameters

state (QuantumChannel) – A CPTP quantum channel.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

• ExtensionError – If the state is the incorrect size for the current circuit.
• ExtensionError – if the input QuantumChannel is not CPTP.

RawFeatureVector.set_unitary(state)

Set the state state of the simulator.

Parameters

state (Operator) – A state matrix.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

• ExtensionError – If the state is the incorrect size for the current circuit.
• ExtensionError – if the input matrix is not unitary.

RawFeatureVector.size()

Returns total number of gate operations in circuit.

Returns

Total number of gate operations.

Return type

int

RawFeatureVector.snapshot(label, snapshot_type='statevector', qubits=None, params=None)

Take a statevector snapshot of the internal simulator representation. Works on all qubits, and prevents reordering (like barrier). :param label: a snapshot label to report the result :type label: str :param snapshot_type: the type of the snapshot. :type snapshot_type: str :param qubits: the qubits to apply snapshot to [Default: None]. :type qubits: list or None :param params: the parameters for snapshot_type [Default: None]. :type params: list or None

Returns

with attached command

Return type

QuantumCircuit

Raises

ExtensionError – malformed command

RawFeatureVector.snapshot_density_matrix(label, qubits=None)

Take a density matrix snapshot of simulator state.

Parameters

• label (str) – a snapshot label to report the result
• qubits (list or None) – the qubits to apply snapshot to. If None all qubits will be snapshot [Default: None].

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if snapshot is invalid.

RawFeatureVector.snapshot_expectation_value(label, op, qubits, single_shot=False, variance=False)

Take a snapshot of expectation value <O> of an Operator.

Parameters

• label (str) – a snapshot label to report the result
• op (Operator) – operator to snapshot
• qubits (list) – the qubits to snapshot.
• single_shot (bool) – return list for each shot rather than average [Default: False]
• variance (bool) – compute variance of values [Default: False]

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if snapshot is invalid.

RawFeatureVector.snapshot_probabilities(label, qubits, variance=False)

Take a probability snapshot of the simulator state.

Parameters

• label (str) – a snapshot label to report the result
• qubits (list) – the qubits to snapshot.
• variance (bool) – compute variance of probabilities [Default: False]

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if snapshot is invalid.

RawFeatureVector.snapshot_stabilizer(label)

Take a stabilizer snapshot of the simulator state.

Parameters

label (str) – a snapshot label to report the result.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if snapshot is invalid.

Additional Information:

This snapshot is always performed on all qubits in a circuit. The number of qubits parameter specifies the size of the instruction as a barrier and should be set to the number of qubits in the circuit.

RawFeatureVector.snapshot_statevector(label)

Take a statevector snapshot of the simulator state.

Parameters

label (str) – a snapshot label to report the result.

Returns

with attached instruction.

Return type

QuantumCircuit

Raises

ExtensionError – if snapshot is invalid.

Additional Information:

This snapshot is always performed on all qubits in a circuit. The number of qubits parameter specifies the size of the instruction as a barrier and should be set to the number of qubits in the circuit.

RawFeatureVector.squ(unitary_matrix, qubit, mode='ZYZ', up_to_diagonal=False, *, u=None)

Decompose an arbitrary 2*2 unitary into three rotation gates.

Note that the decomposition is up to a global phase shift. (This is a well known decomposition, which can be found for example in Nielsen and Chuang’s book “Quantum computation and quantum information”.)

Parameters

• unitary_matrix (ndarray) – 2*2 unitary (given as a (complex) ndarray).
• qubit (QuantumRegister | Qubit) – The qubit which the gate is acting on.
• mode (string) – determines the used decomposition by providing the rotation axes. The allowed modes are: “ZYZ” (default)
• up_to_diagonal (bool) – if set to True, the single-qubit unitary is decomposed up to a diagonal matrix, i.e. a unitary u’ is implemented such that there exists a 2*2 diagonal gate d with u = d.dot(u’)
• u (ndarray) – Deprecated, use unitary_matrix instead.

Returns

The single-qubit unitary instruction attached to the circuit.

Return type

InstructionSet

Raises

QiskitError – if the format is wrong; if the array u is not unitary

RawFeatureVector.swap(qubit1, qubit2)

Apply SwapGate.

RawFeatureVector.sx(qubit)

Apply SXGate.

RawFeatureVector.sxdg(qubit)

Apply SXdgGate.

RawFeatureVector.t(qubit)

Apply TGate.

RawFeatureVector.tdg(qubit)

Apply TdgGate.

RawFeatureVector.tensor(other, inplace=False)

Tensor self with other.

Remember that in the little-endian convention the leftmost operation will be at the bottom of the circuit. See also the docs for more information.

     ┌────────┐        ┌─────┐          ┌─────┐
q_0: ┤ bottom ├ ⊗ q_0: ┤ top ├  = q_0: ─┤ top ├──
     └────────┘        └─────┘         ┌┴─────┴─┐
                                  q_1: ┤ bottom ├
                                       └────────┘

Parameters

• other (QuantumCircuit) – The other circuit to tensor this circuit with.
• inplace (bool) – If True, modify the object. Otherwise return composed circuit.

Examples

from qiskit import QuantumCircuit
top = QuantumCircuit(1)
top.x(0);
bottom = QuantumCircuit(2)
bottom.cry(0.2, 0, 1);
tensored = bottom.tensor(top)
print(tensored.draw())

        ┌───┐   
q_0: ───┤ X ├───
        └───┘   
q_1: ─────■─────
     ┌────┴────┐
q_2: ┤ Ry(0.2) ├
     └─────────┘

Returns

The tensored circuit (returns None if inplace==True).

Return type

QuantumCircuit

RawFeatureVector.to_gate(parameter_map=None, label=None)

Create a Gate out of this circuit.

Parameters

• parameter_map (dict) – For parameterized circuits, a mapping from parameters in the circuit to parameters to be used in the gate. If None, existing circuit parameters will also parameterize the gate.
• label (str) – Optional gate label.

Returns

a composite gate encapsulating this circuit (can be decomposed back)

Return type

Gate

RawFeatureVector.to_instruction(parameter_map=None, label=None)

Create an Instruction out of this circuit.

Parameters

• parameter_map (dict) – For parameterized circuits, a mapping from parameters in the circuit to parameters to be used in the instruction. If None, existing circuit parameters will also parameterize the instruction.
• label (str) – Optional gate label.

Returns

a composite instruction encapsulating this circuit (can be decomposed back)

Return type

qiskit.circuit.Instruction

RawFeatureVector.toffoli(control_qubit1, control_qubit2, target_qubit)

Apply CCXGate.

RawFeatureVector.u(theta, phi, lam, qubit)

Apply UGate.

RawFeatureVector.u1(theta, qubit)

Apply U1Gate.

RawFeatureVector.u2(phi, lam, qubit)

Apply U2Gate.

RawFeatureVector.u3(theta, phi, lam, qubit)

Apply U3Gate.

RawFeatureVector.uc(gate_list, q_controls, q_target, up_to_diagonal=False)

Attach a uniformly controlled gates (also called multiplexed gates) to a circuit.

The decomposition was introduced by Bergholm et al. in https://arxiv.org/pdf/quant-ph/0410066.pdf.

Parameters

• gate_list (list[ndarray]) – list of two qubit unitaries [U_0,…,U_{2^k-1}], where each single-qubit unitary U_i is a given as a 2*2 array
• q_controls (QuantumRegister|list[(QuantumRegister,int)]) – list of k control qubits. The qubits are ordered according to their significance in the computational basis. For example if q_controls=[q[1],q[2]] (with q = QuantumRegister(2)), the unitary U_0 is performed if q[1] and q[2] are in the state zero, U_1 is performed if q[2] is in the state zero and q[1] is in the state one, and so on
• q_target (QuantumRegister|(QuantumRegister,int)) – target qubit, where we act on with the single-qubit gates.
• up_to_diagonal (bool) – If set to True, the uniformly controlled gate is decomposed up to a diagonal gate, i.e. a unitary u’ is implemented such that there exists a diagonal gate d with u = d.dot(u’), where the unitary u describes the uniformly controlled gate

Returns

the uniformly controlled gate is attached to the circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the list number of control qubits does not correspond to the provided number of single-qubit unitaries; if an input is of the wrong type

RawFeatureVector.ucrx(angle_list, q_controls, q_target)

Attach a uniformly controlled (also called multiplexed) Rx rotation gate to a circuit.

The decomposition is base on https://arxiv.org/pdf/quant-ph/0406176.pdf by Shende et al.

Parameters

• angle_list (list) – list of (real) rotation angles $[a_0,...,a_{2^k-1}]$
• q_controls (QuantumRegister|list) – list of k control qubits (or empty list if no controls). The control qubits are ordered according to their significance in increasing order: For example if q_controls=[q[0],q[1]] (with q = QuantumRegister(2)), the rotation Rx(a_0) is performed if q[0] and q[1] are in the state zero, the rotation Rx(a_1) is performed if q[0] is in the state one and q[1] is in the state zero, and so on
• q_target (QuantumRegister|Qubit) – target qubit, where we act on with the single-qubit rotation gates

Returns

the uniformly controlled rotation gate is attached to the circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the list number of control qubits does not correspond to the provided number of single-qubit unitaries; if an input is of the wrong type

RawFeatureVector.ucry(angle_list, q_controls, q_target)

Attach a uniformly controlled (also called multiplexed) Ry rotation gate to a circuit.

The decomposition is base on https://arxiv.org/pdf/quant-ph/0406176.pdf by Shende et al.

Parameters

• angle_list (list[numbers) – list of (real) rotation angles $[a_0,...,a_{2^k-1}]$
• q_controls (QuantumRegister|list[Qubit]) – list of k control qubits (or empty list if no controls). The control qubits are ordered according to their significance in increasing order: For example if q_controls=[q[0],q[1]] (with q = QuantumRegister(2)), the rotation Ry(a_0) is performed if q[0] and q[1] are in the state zero, the rotation Ry(a_1) is performed if q[0] is in the state one and q[1] is in the state zero, and so on
• q_target (QuantumRegister|Qubit) – target qubit, where we act on with the single-qubit rotation gates

Returns

the uniformly controlled rotation gate is attached to the circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the list number of control qubits does not correspond to the provided number of single-qubit unitaries; if an input is of the wrong type

RawFeatureVector.ucrz(angle_list, q_controls, q_target)

Attach a uniformly controlled (also called multiplexed gates) Rz rotation gate to a circuit.

The decomposition is base on https://arxiv.org/pdf/quant-ph/0406176.pdf by Shende et al.

Parameters

• angle_list (list[numbers) – list of (real) rotation angles [a_0,…,a_{2^k-1}]
• q_controls (QuantumRegister|list[Qubit]) – list of k control qubits (or empty list if no controls). The control qubits are ordered according to their significance in increasing order: For example if q_controls=[q[1],q[2]] (with q = QuantumRegister(2)), the rotation Rz(a_0)is performed if q[1] and q[2] are in the state zero, the rotation Rz(a_1) is performed if q[1] is in the state one and q[2] is in the state zero, and so on
• q_target (QuantumRegister|Qubit) – target qubit, where we act on with the single-qubit rotation gates

Returns

the uniformly controlled rotation gate is attached to the circuit.

Return type

QuantumCircuit

Raises

QiskitError – if the list number of control qubits does not correspond to the provided number of single-qubit unitaries; if an input is of the wrong type

RawFeatureVector.unitary(obj, qubits, label=None)

Apply unitary gate to q.

RawFeatureVector.width()

Return number of qubits plus clbits in circuit.

Returns

Width of circuit.

Return type

int

RawFeatureVector.x(qubit, label=None)

Apply XGate.

RawFeatureVector.y(qubit)

Apply YGate.

RawFeatureVector.z(qubit)

Apply ZGate.

Returns a list of ancilla bits in the order that the registers were added.

Return calibration dictionary.

The custom pulse definition of a given gate is of the form

{‘gate_name’: {(qubits, params): schedule}}

Returns a list of classical bits in the order that the registers were added.

Default value: 'include "qelib1.inc";'

Return the feature dimension.

Return type

int

Returns

The feature dimension, which is 2 ** num_qubits.

Return the global phase of the circuit in radians.

Default value: 'OPENQASM 2.0;'

Default value: 16

The user provided metadata associated with the circuit

The metadata for the circuit is a user provided dict of metadata for the circuit. It will not be used to influence the execution or operation of the circuit, but it is expected to be passed between all transforms of the circuit (ie transpilation) and that providers will associate any circuit metadata with the results it returns from execution of that circuit.

Return the number of ancilla qubits.

Return number of classical bits.

Return type

int

Returns the number of qubits in this circuit.

Return type

int

Returns

The number of qubits.

Return the free parameters in the RawFeatureVector.

Return type

List[ParameterExpression]

Returns

A list of the free parameters.

Return the free parameters in the RawFeatureVector.

Return type

Set[ParameterExpression]

Returns

A set of the free parameters.

Default value: 'circuit'

A list of the quantum registers associated with the circuit.

Returns a list of quantum bits in the order that the registers were added.