Code source de qiskit.quantum_info.operators.operator

# This code is part of Qiskit.
# (C) Copyright IBM 2017, 2019.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

Matrix Operator class.

import copy
import re
from numbers import Number

import numpy as np

from qiskit.circuit.quantumcircuit import QuantumCircuit
from qiskit.circuit.instruction import Instruction
from qiskit.circuit.operation import Operation
from qiskit.circuit.library.standard_gates import IGate, XGate, YGate, ZGate, HGate, SGate, TGate
from qiskit.exceptions import QiskitError
from qiskit.quantum_info.operators.predicates import is_unitary_matrix, matrix_equal
from qiskit.quantum_info.operators.linear_op import LinearOp
from qiskit.quantum_info.operators.mixins import generate_apidocs

[docs]class Operator(LinearOp): r"""Matrix operator class This represents a matrix operator :math:`M` that will :meth:`~Statevector.evolve` a :class:`Statevector` :math:`|\psi\rangle` by matrix-vector multiplication .. math:: |\psi\rangle \mapsto M|\psi\rangle, and will :meth:`~DensityMatrix.evolve` a :class:`DensityMatrix` :math:`\rho` by left and right multiplication .. math:: \rho \mapsto M \rho M^\dagger. """ def __init__(self, data, input_dims=None, output_dims=None): """Initialize an operator object. Args: data (QuantumCircuit or Operation or BaseOperator or matrix): data to initialize operator. input_dims (tuple): the input subsystem dimensions. [Default: None] output_dims (tuple): the output subsystem dimensions. [Default: None] Raises: QiskitError: if input data cannot be initialized as an operator. Additional Information: If the input or output dimensions are None, they will be automatically determined from the input data. If the input data is a Numpy array of shape (2**N, 2**N) qubit systems will be used. If the input operator is not an N-qubit operator, it will assign a single subsystem with dimension specified by the shape of the input. """ op_shape = None if isinstance(data, (list, np.ndarray)): # Default initialization from list or numpy array matrix self._data = np.asarray(data, dtype=complex) elif isinstance(data, (QuantumCircuit, Operation)): # If the input is a Terra QuantumCircuit or Operation we # perform a simulation to construct the unitary operator. # This will only work if the circuit or instruction can be # defined in terms of unitary gate instructions which have a # 'to_matrix' method defined. Any other instructions such as # conditional gates, measure, or reset will cause an # exception to be raised. self._data = self._init_instruction(data).data elif hasattr(data, "to_operator"): # If the data object has a 'to_operator' attribute this is given # higher preference than the 'to_matrix' method for initializing # an Operator object. data = data.to_operator() self._data = op_shape = data._op_shape elif hasattr(data, "to_matrix"): # If no 'to_operator' attribute exists we next look for a # 'to_matrix' attribute to a matrix that will be cast into # a complex numpy matrix. self._data = np.asarray(data.to_matrix(), dtype=complex) else: raise QiskitError("Invalid input data format for Operator") super().__init__( op_shape=op_shape, input_dims=input_dims, output_dims=output_dims, shape=self._data.shape, ) def __array__(self, dtype=None): if dtype: return np.asarray(, dtype=dtype) return def __repr__(self): prefix = "Operator(" pad = len(prefix) * " " return "{}{},\n{}input_dims={}, output_dims={})".format( prefix, np.array2string(, separator=", ", prefix=prefix), pad, self.input_dims(), self.output_dims(), ) def __eq__(self, other): """Test if two Operators are equal.""" if not super().__eq__(other): return False return np.allclose(,, rtol=self.rtol, atol=self.atol) @property def data(self): """Return data.""" return self._data @property def settings(self): """Return operator settings.""" return { "data": self._data, "input_dims": self.input_dims(), "output_dims": self.output_dims(), }
[docs] @classmethod def from_label(cls, label): """Return a tensor product of single-qubit operators. Args: label (string): single-qubit operator string. Returns: Operator: The N-qubit operator. Raises: QiskitError: if the label contains invalid characters, or the length of the label is larger than an explicitly specified num_qubits. Additional Information: The labels correspond to the single-qubit matrices: 'I': [[1, 0], [0, 1]] 'X': [[0, 1], [1, 0]] 'Y': [[0, -1j], [1j, 0]] 'Z': [[1, 0], [0, -1]] 'H': [[1, 1], [1, -1]] / sqrt(2) 'S': [[1, 0], [0 , 1j]] 'T': [[1, 0], [0, (1+1j) / sqrt(2)]] '0': [[1, 0], [0, 0]] '1': [[0, 0], [0, 1]] '+': [[0.5, 0.5], [0.5 , 0.5]] '-': [[0.5, -0.5], [-0.5 , 0.5]] 'r': [[0.5, -0.5j], [0.5j , 0.5]] 'l': [[0.5, 0.5j], [-0.5j , 0.5]] """ # Check label is valid label_mats = { "I": IGate().to_matrix(), "X": XGate().to_matrix(), "Y": YGate().to_matrix(), "Z": ZGate().to_matrix(), "H": HGate().to_matrix(), "S": SGate().to_matrix(), "T": TGate().to_matrix(), "0": np.array([[1, 0], [0, 0]], dtype=complex), "1": np.array([[0, 0], [0, 1]], dtype=complex), "+": np.array([[0.5, 0.5], [0.5, 0.5]], dtype=complex), "-": np.array([[0.5, -0.5], [-0.5, 0.5]], dtype=complex), "r": np.array([[0.5, -0.5j], [0.5j, 0.5]], dtype=complex), "l": np.array([[0.5, 0.5j], [-0.5j, 0.5]], dtype=complex), } if re.match(r"^[IXYZHST01rl\-+]+$", label) is None: raise QiskitError("Label contains invalid characters.") # Initialize an identity matrix and apply each gate num_qubits = len(label) op = Operator(np.eye(2**num_qubits, dtype=complex)) for qubit, char in enumerate(reversed(label)): if char != "I": op = op.compose(label_mats[char], qargs=[qubit]) return op
[docs] @classmethod def from_circuit(cls, circuit, ignore_set_layout=False, layout=None): """Create a new Operator object from a :class:`.QuantumCircuit` While a :class:`~.QuantumCircuit` object can passed directly as ``data`` to the class constructor this provides no options on how the circuit is used to create an :class:`.Operator`. This constructor method lets you control how the :class:`.Operator` is created so it can be adjusted for a particular use case. By default this constructor method will permute the qubits based on a configured initial layout (i.e. after it was transpiled). It also provides an option to manually provide a :class:`.Layout` object directly. Args: circuit (QuantumCircuit): The :class:`.QuantumCircuit` to create an Operator object from. ignore_set_layout (bool): When set to ``True`` if the input ``circuit`` has a layout set it will be ignored layout (Layout): If specified this kwarg can be used to specify a particular layout to use to permute the qubits in the created :class:`.Operator`. If this is specified it will be used instead of a layout contained in the ``circuit`` input. If specified the virtual bits in the :class:`~.Layout` must be present in the ``circuit`` input. Returns: Operator: An operator representing the input circuit """ dimension = 2**circuit.num_qubits op = cls(np.eye(dimension)) if layout is None: if not ignore_set_layout: layout = getattr(circuit, "_layout", None) else: from qiskit.transpiler.layout import TranspileLayout # pylint: disable=cyclic-import layout = TranspileLayout( initial_layout=layout, input_qubit_mapping={qubit: index for index, qubit in enumerate(circuit.qubits)}, ) qargs = None # If there was a layout specified (either from the circuit # or via user input) use that to set qargs to permute qubits # based on that layout if layout is not None: physical_to_virtual = layout.initial_layout.get_physical_bits() qargs = [ layout.input_qubit_mapping[physical_to_virtual[physical_bit]] for physical_bit in range(len(physical_to_virtual)) ] # Convert circuit to an instruction instruction = circuit.to_instruction() op._append_instruction(instruction, qargs=qargs) return op
[docs] def is_unitary(self, atol=None, rtol=None): """Return True if operator is a unitary matrix.""" if atol is None: atol = self.atol if rtol is None: rtol = self.rtol return is_unitary_matrix(self._data, rtol=rtol, atol=atol)
[docs] def to_operator(self): """Convert operator to matrix operator class""" return self
[docs] def to_instruction(self): """Convert to a UnitaryGate instruction.""" # pylint: disable=cyclic-import from qiskit.extensions.unitary import UnitaryGate return UnitaryGate(
[docs] def conjugate(self): # Make a shallow copy and update array ret = copy.copy(self) ret._data = np.conj(self._data) return ret
[docs] def transpose(self): # Make a shallow copy and update array ret = copy.copy(self) ret._data = np.transpose(self._data) ret._op_shape = self._op_shape.transpose() return ret
[docs] def compose(self, other, qargs=None, front=False): if qargs is None: qargs = getattr(other, "qargs", None) if not isinstance(other, Operator): other = Operator(other) # Validate dimensions are compatible and return the composed # operator dimensions new_shape = self._op_shape.compose(other._op_shape, qargs, front) input_dims = new_shape.dims_r() output_dims = new_shape.dims_l() # Full composition of operators if qargs is None: if front: # Composition self * other data =, else: # Composition other * self data =, self._data) ret = Operator(data, input_dims, output_dims) ret._op_shape = new_shape return ret # Compose with other on subsystem num_qargs_l, num_qargs_r = self._op_shape.num_qargs if front: num_indices = num_qargs_r shift = num_qargs_l right_mul = True else: num_indices = num_qargs_l shift = 0 right_mul = False # Reshape current matrix # Note that we must reverse the subsystem dimension order as # qubit 0 corresponds to the right-most position in the tensor # product, which is the last tensor wire index. tensor = np.reshape(, self._op_shape.tensor_shape) mat = np.reshape(, other._op_shape.tensor_shape) indices = [num_indices - 1 - qubit for qubit in qargs] final_shape = [np.product(output_dims), np.product(input_dims)] data = np.reshape( Operator._einsum_matmul(tensor, mat, indices, shift, right_mul), final_shape ) ret = Operator(data, input_dims, output_dims) ret._op_shape = new_shape return ret
[docs] def power(self, n): """Return the matrix power of the operator. Args: n (float): the power to raise the matrix to. Returns: Operator: the resulting operator ``O ** n``. Raises: QiskitError: if the input and output dimensions of the operator are not equal. """ if self.input_dims() != self.output_dims(): raise QiskitError("Can only power with input_dims = output_dims.") ret = copy.copy(self) ret._data = np.linalg.matrix_power(, n) return ret
[docs] def tensor(self, other): if not isinstance(other, Operator): other = Operator(other) return self._tensor(self, other)
[docs] def expand(self, other): if not isinstance(other, Operator): other = Operator(other) return self._tensor(other, self)
@classmethod def _tensor(cls, a, b): ret = copy.copy(a) ret._op_shape = a._op_shape.tensor(b._op_shape) ret._data = np.kron(, return ret def _add(self, other, qargs=None): """Return the operator self + other. If ``qargs`` are specified the other operator will be added assuming it is identity on all other subsystems. Args: other (Operator): an operator object. qargs (None or list): optional subsystems to add on (Default: None) Returns: Operator: the operator self + other. Raises: QiskitError: if other is not an operator, or has incompatible dimensions. """ # pylint: disable=cyclic-import from qiskit.quantum_info.operators.scalar_op import ScalarOp if qargs is None: qargs = getattr(other, "qargs", None) if not isinstance(other, Operator): other = Operator(other) self._op_shape._validate_add(other._op_shape, qargs) other = ScalarOp._pad_with_identity(self, other, qargs) ret = copy.copy(self) ret._data = + return ret def _multiply(self, other): """Return the operator self * other. Args: other (complex): a complex number. Returns: Operator: the operator other * self. Raises: QiskitError: if other is not a valid complex number. """ if not isinstance(other, Number): raise QiskitError("other is not a number") ret = copy.copy(self) ret._data = other * self._data return ret
[docs] def equiv(self, other, rtol=None, atol=None): """Return True if operators are equivalent up to global phase. Args: other (Operator): an operator object. rtol (float): relative tolerance value for comparison. atol (float): absolute tolerance value for comparison. Returns: bool: True if operators are equivalent up to global phase. """ if not isinstance(other, Operator): try: other = Operator(other) except QiskitError: return False if self.dim != other.dim: return False if atol is None: atol = self.atol if rtol is None: rtol = self.rtol return matrix_equal(,, ignore_phase=True, rtol=rtol, atol=atol)
[docs] def reverse_qargs(self): r"""Return an Operator with reversed subsystem ordering. For a tensor product operator this is equivalent to reversing the order of tensor product subsystems. For an operator :math:`A = A_{n-1} \otimes ... \otimes A_0` the returned operator will be :math:`A_0 \otimes ... \otimes A_{n-1}`. Returns: Operator: the operator with reversed subsystem order. """ ret = copy.copy(self) axes = tuple(range(self._op_shape._num_qargs_l - 1, -1, -1)) axes = axes + tuple(len(axes) + i for i in axes) ret._data = np.reshape( np.transpose(np.reshape(, self._op_shape.tensor_shape), axes), self._op_shape.shape, ) ret._op_shape = self._op_shape.reverse() return ret
@classmethod def _einsum_matmul(cls, tensor, mat, indices, shift=0, right_mul=False): """Perform a contraction using Numpy.einsum Args: tensor (np.array): a vector or matrix reshaped to a rank-N tensor. mat (np.array): a matrix reshaped to a rank-2M tensor. indices (list): tensor indices to contract with mat. shift (int): shift for indices of tensor to contract [Default: 0]. right_mul (bool): if True right multiply tensor by mat (else left multiply) [Default: False]. Returns: Numpy.ndarray: the matrix multiplied rank-N tensor. Raises: QiskitError: if mat is not an even rank tensor. """ rank = tensor.ndim rank_mat = mat.ndim if rank_mat % 2 != 0: raise QiskitError("Contracted matrix must have an even number of indices.") # Get einsum indices for tensor indices_tensor = list(range(rank)) for j, index in enumerate(indices): indices_tensor[index + shift] = rank + j # Get einsum indices for mat mat_contract = list(reversed(range(rank, rank + len(indices)))) mat_free = [index + shift for index in reversed(indices)] if right_mul: indices_mat = mat_contract + mat_free else: indices_mat = mat_free + mat_contract return np.einsum(tensor, indices_tensor, mat, indices_mat) @classmethod def _init_instruction(cls, instruction): """Convert a QuantumCircuit or Operation to an Operator.""" # Initialize an identity operator of the correct size of the circuit if hasattr(instruction, "__array__"): return Operator(np.array(instruction, dtype=complex)) dimension = 2**instruction.num_qubits op = Operator(np.eye(dimension)) # Convert circuit to an instruction if isinstance(instruction, QuantumCircuit): instruction = instruction.to_instruction() op._append_instruction(instruction) return op @classmethod def _instruction_to_matrix(cls, obj): """Return Operator for instruction if defined or None otherwise.""" # Note: to_matrix() is not a required method for Operations, so for now # we do not allow constructing matrices for general Operations. # However, for backward compatibility we need to support constructing matrices # for Cliffords, which happen to have a to_matrix() method. # pylint: disable=cyclic-import from qiskit.quantum_info import Clifford if not isinstance(obj, (Instruction, Clifford)): raise QiskitError("Input is neither an Instruction nor Clifford.") mat = None if hasattr(obj, "to_matrix"): # If instruction is a gate first we see if it has a # `to_matrix` definition and if so use that. try: mat = obj.to_matrix() except QiskitError: pass return mat def _append_instruction(self, obj, qargs=None): """Update the current Operator by apply an instruction.""" from qiskit.circuit.barrier import Barrier from .scalar_op import ScalarOp mat = self._instruction_to_matrix(obj) if mat is not None: # Perform the composition and inplace update the current state # of the operator op = self.compose(mat, qargs=qargs) self._data = elif isinstance(obj, Barrier): return else: # If the instruction doesn't have a matrix defined we use its # circuit decomposition definition if it exists, otherwise we # cannot compose this gate and raise an error. if obj.definition is None: raise QiskitError(f"Cannot apply Operation: {}") if not isinstance(obj.definition, QuantumCircuit): raise QiskitError( 'Operation "{}" ' "definition is {} but expected QuantumCircuit.".format(, type(obj.definition) ) ) if obj.definition.global_phase: dimension = 2**obj.num_qubits op = self.compose( ScalarOp(dimension, np.exp(1j * float(obj.definition.global_phase))), qargs=qargs, ) self._data = flat_instr = obj.definition bit_indices = { bit: index for bits in [flat_instr.qubits, flat_instr.clbits] for index, bit in enumerate(bits) } for instruction in flat_instr: if instruction.clbits: raise QiskitError( "Cannot apply operation with classical bits:" f" {}" ) # Get the integer position of the flat register if qargs is None: new_qargs = [bit_indices[tup] for tup in instruction.qubits] else: new_qargs = [qargs[bit_indices[tup]] for tup in instruction.qubits] self._append_instruction(instruction.operation, qargs=new_qargs)
# Update docstrings for API docs generate_apidocs(Operator)