Código fonte de qiskit.synthesis.discrete_basis.generate_basis_approximations

# This code is part of Qiskit.
# (C) Copyright IBM 2017, 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.

"""Functions to generate the basic approximations of single qubit gates for Solovay-Kitaev."""

from __future__ import annotations

import warnings
import collections
import numpy as np

import qiskit.circuit.library.standard_gates as gates
from qiskit.circuit import Gate
from qiskit.quantum_info.operators.predicates import matrix_equal
from qiskit.utils import optionals

from .gate_sequence import GateSequence

Node = collections.namedtuple("Node", ("labels", "sequence", "children"))

_1q_inverses = {
    "i": "i",
    "x": "x",
    "y": "y",
    "z": "z",
    "h": "h",
    "t": "tdg",
    "tdg": "t",
    "s": "sdg",
    "sdg": "s",

_1q_gates = {
    "i": gates.IGate(),
    "x": gates.XGate(),
    "y": gates.YGate(),
    "z": gates.ZGate(),
    "h": gates.HGate(),
    "t": gates.TGate(),
    "tdg": gates.TdgGate(),
    "s": gates.SGate(),
    "sdg": gates.SdgGate(),
    "sx": gates.SXGate(),
    "sxdg": gates.SXdgGate(),

def _check_candidate(candidate, existing_sequences, tol=1e-10):
    if optionals.HAS_SKLEARN:
        return _check_candidate_kdtree(candidate, existing_sequences, tol)

        "The SolovayKitaev algorithm relies on scikit-learn's KDTree for a "
        "fast search over the basis approximations. Without this, we fallback onto a "
        "greedy search with is significantly slower. We highly suggest to install "
        "scikit-learn to use this feature.",
    return _check_candidate_greedy(candidate, existing_sequences, tol)

def _check_candidate_greedy(candidate, existing_sequences, tol=1e-10):
    # do a quick, string-based check if the same sequence already exists
    if any(candidate.name == existing.name for existing in existing_sequences):
        return False

    for existing in existing_sequences:
        if matrix_equal(existing.product_su2, candidate.product_su2, ignore_phase=True, atol=tol):
            # is the new sequence less or more efficient?
            return len(candidate.gates) < len(existing.gates)
    return True

def _check_candidate_kdtree(candidate, existing_sequences, tol=1e-10):
    """Check if there's a candidate implementing the same matrix up to ``tol``.

    This uses a k-d tree search and is much faster than the greedy, list-based search.
    from sklearn.neighbors import KDTree

    # do a quick, string-based check if the same sequence already exists
    if any(candidate.name == existing.name for existing in existing_sequences):
        return False

    points = np.array([sequence.product.flatten() for sequence in existing_sequences])
    candidate = np.array([candidate.product.flatten()])

    kdtree = KDTree(points)
    dist, _ = kdtree.query(candidate)

    return dist[0][0] > tol

def _process_node(node: Node, basis: list[str], sequences: list[GateSequence]):
    inverse_last = _1q_inverses[node.labels[-1]] if node.labels else None

    for label in basis:
        if label == inverse_last:

        sequence = node.sequence.copy()

        if _check_candidate(sequence, sequences):
            node.children.append(Node(node.labels + (label,), sequence, []))

    return node.children

[documentos]def generate_basic_approximations( basis_gates: list[str | Gate], depth: int, filename: str | None = None ) -> list[GateSequence]: """Generates a list of ``GateSequence``s with the gates in ``basic_gates``. Args: basis_gates: The gates from which to create the sequences of gates. depth: The maximum depth of the approximations. filename: If provided, the basic approximations are stored in this file. Returns: List of ``GateSequences`` using the gates in ``basic_gates``. Raises: ValueError: If ``basis_gates`` contains an invalid gate identifier. """ basis = [] for gate in basis_gates: if isinstance(gate, str): if gate not in _1q_gates.keys(): raise ValueError(f"Invalid gate identifier: {gate}") basis.append(gate) else: # gate is a qiskit.circuit.Gate basis.append(gate.name) tree = Node((), GateSequence(), []) cur_level = [tree] sequences = [tree.sequence] for _ in [None] * depth: next_level = [] for node in cur_level: next_level.extend(_process_node(node, basis, sequences)) cur_level = next_level if filename is not None: data = {} for sequence in sequences: gatestring = sequence.name data[gatestring] = sequence.product np.save(filename, data) return sequences