# Copyright (C) 2024 qBraid
#
# This file is part of the qBraid-SDK
#
# The qBraid-SDK is free software released under the GNU General Public License v3
# or later. You can redistribute and/or modify it under the terms of the GPL v3.
# See the LICENSE file in the project root or <https://www.gnu.org/licenses/gpl-3.0.html>.
#
# THERE IS NO WARRANTY for the qBraid-SDK, as per Section 15 of the GPL v3.
"""
Module defining GateModelProgram Class
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, Optional
import numpy as np
from qbraid.programs.program import QuantumProgram
if TYPE_CHECKING:
import qbraid.runtime
[docs]
class GateModelProgram(QuantumProgram, ABC):
"""Abstract class for qbraid program wrapper objects."""
@property
@abstractmethod
def qubits(self) -> list[Any]:
"""Return the qubits acted upon by the operations in this circuit"""
@property
def num_qubits(self) -> int:
"""Return the number of qubits in the circuit."""
return len(self.qubits)
@property
@abstractmethod
def num_clbits(self) -> Optional[int]:
"""Return the number of classical bits in the circuit."""
@property
def depth(self) -> int:
"""Return the circuit depth (i.e., length of critical path)."""
raise NotImplementedError
def _unitary(self) -> np.ndarray:
"""Calculate unitary of circuit."""
raise NotImplementedError
def unitary(self) -> np.ndarray:
"""Calculate unitary of circuit."""
if self.spec.alias in ["pyquil", "qiskit", "qasm3"]:
return self.unitary_rev_qubits()
return self._unitary()
def unitary_rev_qubits(self) -> np.ndarray:
"""Peforms Kronecker (tensor) product factor permutation of given matrix.
Returns a matrix equivalent to that computed from a quantum circuit if its
qubit indicies were reversed.
Args:
matrix (np.ndarray): The input matrix, assumed to be a 2^N x 2^N square matrix
where N is an integer.
Returns:
np.ndarray: The matrix with permuted Kronecker product factors.
Raises:
ValueError: If the input matrix is not square or its size is not a power of 2.
"""
matrix = self._unitary()
if matrix.shape[0] != matrix.shape[1] or (matrix.shape[0] & (matrix.shape[0] - 1)) != 0:
raise ValueError("Input matrix must be a square matrix of size 2^N for some integer N.")
# Determine the number of qubits from the matrix size
num_qubits = int(np.log2(matrix.shape[0]))
# Create an empty matrix of the same size
permuted_matrix = np.zeros((2**num_qubits, 2**num_qubits), dtype=complex)
for i in range(2**num_qubits):
for j in range(2**num_qubits):
# pylint: disable=consider-using-generator
# Convert indices to binary representations (qubit states)
bits_i = [((i >> bit) & 1) for bit in range(num_qubits)]
bits_j = [((j >> bit) & 1) for bit in range(num_qubits)]
# Reverse the bits
reversed_i = sum([bit << (num_qubits - 1 - k) for k, bit in enumerate(bits_i)])
reversed_j = sum([bit << (num_qubits - 1 - k) for k, bit in enumerate(bits_j)])
# Update the new matrix
permuted_matrix[reversed_i, reversed_j] = matrix[i, j]
return permuted_matrix
def unitary_little_endian(self) -> np.ndarray:
"""Converts unitary calculated using big-endian system to its
equivalent form in a little-endian system.
Args:
matrix: big-endian unitary
Raises:
ValueError: If input matrix is not unitary
Returns:
little-endian unitary
"""
matrix = self.unitary()
rank = len(matrix)
if not np.allclose(np.eye(rank), matrix.dot(matrix.T.conj())):
raise ValueError("Input matrix must be unitary.")
num_qubits = int(np.log2(rank))
tensor_be = matrix.reshape([2] * 2 * num_qubits)
indicies_in = list(reversed(range(num_qubits)))
indicies_out = [i + num_qubits for i in indicies_in]
tensor_le = np.einsum(tensor_be, indicies_in + indicies_out)
return tensor_le.reshape([rank, rank])
def remove_idle_qubits(self) -> None:
"""Remove empty registers of circuit."""
raise NotImplementedError
def reverse_qubit_order(self) -> None:
"""Rerverse qubit ordering of circuit."""
raise NotImplementedError
def transform(self, device: qbraid.runtime.QuantumDevice) -> None:
"""Transform program to according to device target profile."""
return None