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Source code for qiskit_optimization.translators.ising

# This code is part of Qiskit.
#
# (C) Copyright IBM 2019, 2022.
#
# 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.

"""Translator between an Ising Hamiltonian and a quadratic program"""

import math
from typing import Tuple, Union

import numpy as np

from qiskit.opflow import I, ListOp, OperatorBase, PauliOp, PauliSumOp, SummedOp
from qiskit.quantum_info import Pauli
from qiskit_optimization.exceptions import QiskitOptimizationError
from qiskit_optimization.problems.quadratic_program import QuadraticProgram


[docs]def to_ising(quad_prog: QuadraticProgram) -> Tuple[OperatorBase, float]: """Return the Ising Hamiltonian of this problem. Variables are mapped to qubits in the same order, i.e., i-th variable is mapped to i-th qubit. See https://github.com/Qiskit/qiskit-terra/issues/1148 for details. Args: quad_prog: The problem to be translated. Returns: A tuple (qubit_op, offset) comprising the qubit operator for the problem and offset for the constant value in the Ising Hamiltonian. Raises: QiskitOptimizationError: If an integer variable or a continuous variable exists in the problem. QiskitOptimizationError: If constraints exist in the problem. """ # if problem has variables that are not binary, raise an error if quad_prog.get_num_vars() > quad_prog.get_num_binary_vars(): raise QiskitOptimizationError( "The type of all variables must be binary. " "You can use `QuadraticProgramToQubo` converter " "to convert integer variables to binary variables. " "If the problem contains continuous variables, `to_ising` cannot handle it. " "You might be able to solve it with `ADMMOptimizer`." ) # if constraints exist, raise an error if quad_prog.linear_constraints or quad_prog.quadratic_constraints: raise QiskitOptimizationError( "There must be no constraint in the problem. " "You can use `QuadraticProgramToQubo` converter " "to convert constraints to penalty terms of the objective function." ) # initialize Hamiltonian. num_nodes = quad_prog.get_num_vars() pauli_list = [] offset = 0.0 zero = np.zeros(num_nodes, dtype=bool) # set a sign corresponding to a maximized or minimized problem. # sign == 1 is for minimized problem. sign == -1 is for maximized problem. sense = quad_prog.objective.sense.value # convert a constant part of the object function into Hamiltonian. offset += quad_prog.objective.constant * sense # convert linear parts of the object function into Hamiltonian. for idx, coef in quad_prog.objective.linear.to_dict().items(): z_p = zero.copy() weight = coef * sense / 2 z_p[idx] = True pauli_list.append(PauliOp(Pauli((z_p, zero)), -weight)) offset += weight # create Pauli terms for (i, j), coeff in quad_prog.objective.quadratic.to_dict().items(): weight = coeff * sense / 4 if i == j: offset += weight else: z_p = zero.copy() z_p[i] = True z_p[j] = True pauli_list.append(PauliOp(Pauli((z_p, zero)), weight)) z_p = zero.copy() z_p[i] = True pauli_list.append(PauliOp(Pauli((z_p, zero)), -weight)) z_p = zero.copy() z_p[j] = True pauli_list.append(PauliOp(Pauli((z_p, zero)), -weight)) offset += weight # Remove paulis whose coefficients are zeros. qubit_op = sum(pauli_list) # qubit_op could be the integer 0, in this case return an identity operator of # appropriate size if isinstance(qubit_op, OperatorBase): qubit_op = qubit_op.reduce() else: qubit_op = I ^ num_nodes return qubit_op, offset
[docs]def from_ising( qubit_op: Union[OperatorBase, PauliSumOp], offset: float = 0.0, linear: bool = False, ) -> QuadraticProgram: r"""Create a quadratic program from a qubit operator and a shift value. Variables are mapped to qubits in the same order, i.e., i-th variable is mapped to i-th qubit. See https://github.com/Qiskit/qiskit-terra/issues/1148 for details. Args: qubit_op: The qubit operator of the problem. offset: The constant term in the Ising Hamiltonian. linear: If linear is True, :math:`x^2` is treated as a linear term since :math:`x^2 = x` for :math:`x \in \{0,1\}`. Otherwise, :math:`x^2` is treat as a quadratic term. The default value is False. Returns: The quadratic program corresponding to the qubit operator. Raises: QiskitOptimizationError: if there are Pauli Xs or Ys in any Pauli term QiskitOptimizationError: if there are more than 2 Pauli Zs in any Pauli term QiskitOptimizationError: if any Pauli term has an imaginary coefficient NotImplementedError: If the input operator is a ListOp """ if isinstance(qubit_op, PauliSumOp): qubit_op = qubit_op.to_pauli_op() # No support for ListOp yet, this can be added in future # pylint: disable=unidiomatic-typecheck if type(qubit_op) == ListOp: raise NotImplementedError( "Conversion of a ListOp is not supported, convert each " "operator in the ListOp separately." ) quad_prog = QuadraticProgram() quad_prog.binary_var_list(qubit_op.num_qubits) if not isinstance(qubit_op, SummedOp): pauli_list = [qubit_op.to_pauli_op()] else: pauli_list = qubit_op.to_pauli_op() # prepare a matrix of coefficients of Pauli terms # `pauli_coeffs_diag` is the diagonal part # `pauli_coeffs_triu` is the upper triangular part pauli_coeffs_diag = [0.0] * qubit_op.num_qubits pauli_coeffs_triu = {} for pauli_op in pauli_list: pauli_op = pauli_op.to_pauli_op() pauli = pauli_op.primitive coeff = pauli_op.coeff if not math.isclose(coeff.imag, 0.0, abs_tol=1e-10): raise QiskitOptimizationError(f"Imaginary coefficient exists: {pauli_op}") if np.any(pauli.x): raise QiskitOptimizationError(f"Pauli X or Y exists in the Pauli term: {pauli}") # indices of Pauli Zs in the Pauli term z_index = np.where(pauli.z)[0] num_z = len(z_index) if num_z == 1: pauli_coeffs_diag[z_index[0]] = coeff.real elif num_z == 2: pauli_coeffs_triu[z_index[0], z_index[1]] = coeff.real else: raise QiskitOptimizationError( f"There are more than 2 Pauli Zs in the Pauli term: {pauli}" ) linear_terms = {} quadratic_terms = {} # For quadratic pauli terms of operator # x_i * x_j = (1 - Z_i - Z_j + Z_i * Z_j)/4 for (i, j), weight in pauli_coeffs_triu.items(): # Add a quadratic term to the object function of `QuadraticProgram` # The coefficient of the quadratic term in `QuadraticProgram` is # 4 * weight of the pauli quadratic_terms[i, j] = 4 * weight pauli_coeffs_diag[i] += weight pauli_coeffs_diag[j] += weight offset -= weight # After processing quadratic pauli terms, only linear paulis are left # x_i = (1 - Z_i)/2 for i, weight in enumerate(pauli_coeffs_diag): # Add a linear term to the object function of `QuadraticProgram` # The coefficient of the linear term in `QuadraticProgram` is # 2 * weight of the pauli if linear: linear_terms[i] = -2 * weight else: quadratic_terms[i, i] = -2 * weight offset += weight quad_prog.minimize(constant=offset, linear=linear_terms, quadratic=quadratic_terms) return quad_prog

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