# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2020.
#
# 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 http://www.apache.org/licenses/LICENSE-2.0.
#
# 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.
"""
Methods to create random operators.
"""
from __future__ import annotations
import numpy as np
from numpy.random import default_rng
from qiskit.exceptions import QiskitError
from qiskit.quantum_info.operators import Operator, Stinespring
# pylint: disable=unused-import
from .dihedral.random import random_cnotdihedral
from .symplectic.random import (
random_clifford,
random_pauli,
random_pauli_list,
random_pauli_table,
random_stabilizer_table,
)
DEFAULT_RNG = default_rng()
[Doku]def random_unitary(dims: int | tuple, seed: int | np.random.Generator | None = None):
"""Return a random unitary Operator.
The operator is sampled from the unitary Haar measure.
Args:
dims (int or tuple): the input dimensions of the Operator.
seed (int or np.random.Generator): Optional. Set a fixed seed or
generator for RNG.
Returns:
Operator: a unitary operator.
"""
if seed is None:
random_state = DEFAULT_RNG
elif isinstance(seed, np.random.Generator):
random_state = seed
else:
random_state = default_rng(seed)
dim = np.prod(dims)
from scipy import stats
mat = stats.unitary_group.rvs(dim, random_state=random_state)
return Operator(mat, input_dims=dims, output_dims=dims)
[Doku]def random_hermitian(
dims: int | tuple, traceless: bool = False, seed: int | np.random.Generator | None = None
):
"""Return a random hermitian Operator.
The operator is sampled from Gaussian Unitary Ensemble.
Args:
dims (int or tuple): the input dimension of the Operator.
traceless (bool): Optional. If True subtract diagonal entries to
return a traceless hermitian operator
(Default: False).
seed (int or np.random.Generator): Optional. Set a fixed seed or
generator for RNG.
Returns:
Operator: a Hermitian operator.
"""
if seed is None:
rng = DEFAULT_RNG
elif isinstance(seed, np.random.Generator):
rng = seed
else:
rng = default_rng(seed)
# Total dimension
dim = np.prod(dims)
from scipy import stats
if traceless:
mat = np.zeros((dim, dim), dtype=complex)
else:
# Generate diagonal part of matrix for Gaussian N(0, 1)
mat = np.diag(stats.norm.rvs(scale=1, size=dim, random_state=rng).astype(complex))
# Generate lower triangular values from Gaussian N(0, 0.5)
num_tril = (dim * (dim - 1)) // 2
real_tril = stats.norm.rvs(scale=0.5, size=num_tril, random_state=rng)
imag_tril = stats.norm.rvs(scale=0.5, size=num_tril, random_state=rng)
# Get lower triangular indices
rows, cols = np.tril_indices(dim, -1)
mat[(rows, cols)] = real_tril + 1j * imag_tril
mat[(cols, rows)] = real_tril - 1j * imag_tril
return Operator(mat, input_dims=dims, output_dims=dims)
[Doku]def random_quantum_channel(
input_dims: int | tuple | None = None,
output_dims: int | tuple | None = None,
rank: int | None = None,
seed: int | np.random.Generator | None = None,
):
"""Return a random CPTP quantum channel.
This constructs the Stinespring operator for the quantum channel by
sampling a random isometry from the unitary Haar measure.
Args:
input_dims (int or tuple): the input dimension of the channel.
output_dims (int or tuple): the input dimension of the channel.
rank (int): Optional. The rank of the quantum channel Choi-matrix.
seed (int or np.random.Generator): Optional. Set a fixed seed or
generator for RNG.
Returns:
Stinespring: a quantum channel operator.
Raises:
QiskitError: if rank or dimensions are invalid.
"""
# Determine total input and output dimensions
if input_dims is None and output_dims is None:
raise QiskitError(
"No dimensions specified: input_dims and output_dims cannot both be None."
)
if input_dims is None:
input_dims = output_dims
elif output_dims is None:
output_dims = input_dims
d_in = np.prod(input_dims)
d_out = np.prod(output_dims)
# If rank is not specified set to the maximum rank for the
# Choi matrix (input_dim * output_dim)
if rank is None or rank > d_in * d_out:
rank = d_in * d_out
if rank < 1:
raise QiskitError(f"Rank {rank} must be greater than 0.")
from scipy import stats
# Generate a random unitary matrix
unitary = stats.unitary_group.rvs(max(rank * d_out, d_in), random_state=seed)
# Truncate columns to produce an isometry
return Stinespring(unitary[:, :d_in], input_dims=input_dims, output_dims=output_dims)