# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# 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.
"""
A Solovay-Kitaev synthesis plugin to Qiskit's transpiler.
"""
from __future__ import annotations
import numpy as np
from qiskit.converters import circuit_to_dag
from qiskit.circuit.gate import Gate
from qiskit.dagcircuit import DAGCircuit
from qiskit.synthesis.discrete_basis.solovay_kitaev import SolovayKitaevDecomposition
from qiskit.synthesis.discrete_basis.generate_basis_approximations import (
generate_basic_approximations,
)
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.transpiler.exceptions import TranspilerError
from .plugin import UnitarySynthesisPlugin
[ドキュメント]class SolovayKitaev(TransformationPass):
r"""Approximately decompose 1q gates to a discrete basis using the Solovay-Kitaev algorithm.
The Solovay-Kitaev theorem [1] states that any single qubit gate can be approximated to
arbitrary precision by a set of fixed single-qubit gates, if the set generates a dense
subset in :math:`SU(2)`. This is an important result, since it means that any single-qubit
gate can be expressed in terms of a discrete, universal gate set that we know how to implement
fault-tolerantly. Therefore, the Solovay-Kitaev algorithm allows us to take any
non-fault tolerant circuit and rephrase it in a fault-tolerant manner.
This implementation of the Solovay-Kitaev algorithm is based on [2].
For example, the following circuit
.. parsed-literal::
┌─────────┐
q_0: ┤ RX(0.8) ├
└─────────┘
can be decomposed into
.. parsed-literal::
global phase: 7π/8
┌───┐┌───┐┌───┐
q_0: ┤ H ├┤ T ├┤ H ├
└───┘└───┘└───┘
with an L2-error of approximately 0.01.
Examples:
Per default, the basis gate set is ``["t", "tdg", "h"]``:
.. code-block::
import numpy as np
from qiskit.circuit import QuantumCircuit
from qiskit.transpiler.passes.synthesis import SolovayKitaev
from qiskit.quantum_info import Operator
circuit = QuantumCircuit(1)
circuit.rx(0.8, 0)
print("Original circuit:")
print(circuit.draw())
skd = SolovayKitaev(recursion_degree=2)
discretized = skd(circuit)
print("Discretized circuit:")
print(discretized.draw())
print("Error:", np.linalg.norm(Operator(circuit).data - Operator(discretized).data))
.. parsed-literal::
Original circuit:
┌─────────┐
q: ┤ Rx(0.8) ├
└─────────┘
Discretized circuit:
global phase: 7π/8
┌───┐┌───┐┌───┐
q: ┤ H ├┤ T ├┤ H ├
└───┘└───┘└───┘
Error: 2.828408279166474
For individual basis gate sets, the ``generate_basic_approximations`` function can be used:
.. code-block::
from qiskit.synthesis import generate_basic_approximations
from qiskit.transpiler.passes import SolovayKitaev
basis = ["s", "sdg", "t", "tdg", "z", "h"]
approx = generate_basic_approximations(basis, depth=3)
skd = SolovayKitaev(recursion_degree=2, basic_approximations=approx)
References:
[1]: Kitaev, A Yu (1997). Quantum computations: algorithms and error correction.
Russian Mathematical Surveys. 52 (6): 1191–1249.
`Online <https://iopscience.iop.org/article/10.1070/RM1997v052n06ABEH002155>`_.
[2]: Dawson, Christopher M.; Nielsen, Michael A. (2005) The Solovay-Kitaev Algorithm.
`arXiv:quant-ph/0505030 <https://arxiv.org/abs/quant-ph/0505030>`_.
"""
def __init__(
self,
recursion_degree: int = 3,
basic_approximations: str | dict[str, np.ndarray] | None = None,
) -> None:
"""
Args:
recursion_degree: The recursion depth for the Solovay-Kitaev algorithm.
A larger recursion depth increases the accuracy and length of the
decomposition.
basic_approximations: The basic approximations for the finding the best discrete
decomposition at the root of the recursion. If a string, it specifies the ``.npy``
file to load the approximations from. If a dictionary, it contains
``{label: SO(3)-matrix}`` pairs. If None, a default based on the H, T and Tdg gates
up to combinations of depth 10 is generated.
"""
super().__init__()
self.recursion_degree = recursion_degree
self._sk = SolovayKitaevDecomposition(basic_approximations)
[ドキュメント] def run(self, dag: DAGCircuit) -> DAGCircuit:
"""Run the ``SolovayKitaev`` pass on `dag`.
Args:
dag: The input dag.
Returns:
Output dag with 1q gates synthesized in the discrete target basis.
Raises:
TranspilerError: if a gates does not have to_matrix
"""
for node in dag.op_nodes():
if not node.op.num_qubits == 1:
continue # ignore all non-single qubit gates
# we do not check the input matrix as we know it comes from a Qiskit gate, as this
# we know it will generate a valid SU(2) matrix
check_input = not isinstance(node.op, Gate)
if not hasattr(node.op, "to_matrix"):
raise TranspilerError(
f"SolovayKitaev does not support gate without to_matrix method: {node.op.name}"
)
matrix = node.op.to_matrix()
# call solovay kitaev
approximation = self._sk.run(
matrix, self.recursion_degree, return_dag=True, check_input=check_input
)
# convert to a dag and replace the gate by the approximation
dag.substitute_node_with_dag(node, approximation)
return dag
[ドキュメント]class SolovayKitaevSynthesis(UnitarySynthesisPlugin):
"""A Solovay-Kitaev Qiskit unitary synthesis plugin.
This plugin is invoked by :func:`~.compiler.transpile` when the ``unitary_synthesis_method``
parameter is set to ``"sk"``.
This plugin supports customization and additional parameters can be passed to the plugin
by passing a dictionary as the ``unitary_synthesis_plugin_config`` parameter of
the :func:`~qiskit.compiler.transpile` function.
Supported parameters in the dictionary:
basis_approximations (str | dict):
The basic approximations for the finding the best discrete decomposition at the root of the
recursion. If a string, it specifies the ``.npy`` file to load the approximations from.
If a dictionary, it contains ``{label: SO(3)-matrix}`` pairs. If None, a default based on
the specified ``basis_gates`` and ``depth`` is generated.
basis_gates (list):
A list of strings specifying the discrete basis gates to decompose to. If None,
defaults to ``["h", "t", "tdg"]``.
depth (int):
The gate-depth of the basic approximations. All possible, unique combinations of the
basis gates up to length ``depth`` are considered. If None, defaults to 10.
recursion_degree (int):
The number of times the decomposition is recursively improved. If None, defaults to 3.
"""
# we cache an instance of the Solovay-Kitaev class to generate the
# computationally expensive basis approximation of single qubit gates only once
_sk = None
@property
def max_qubits(self):
"""Maximum number of supported qubits is ``1``."""
return 1
@property
def min_qubits(self):
"""Minimum number of supported qubits is ``1``."""
return 1
@property
def supports_natural_direction(self):
"""The plugin does not support natural direction, it does not assume
bidirectional two qubit gates."""
return True
@property
def supports_pulse_optimize(self):
"""The plugin does not support optimization of pulses."""
return False
@property
def supports_gate_lengths(self):
"""The plugin does not support gate lengths."""
return False
@property
def supports_gate_errors(self):
"""The plugin does not support gate errors."""
return False
@property
def supported_bases(self):
"""The plugin does not support bases for synthesis."""
return None
@property
def supports_basis_gates(self):
"""The plugin does not support basis gates. By default it synthesis to the
``["h", "t", "tdg"]`` gate basis."""
return True
@property
def supports_coupling_map(self):
"""The plugin does not support coupling maps."""
return False
[ドキュメント] def run(self, unitary, **options):
# Runtime imports to avoid the overhead of these imports for
# plugin discovery and only use them if the plugin is run/used
config = options.get("config") or {}
recursion_degree = config.get("recursion_degree", 3)
# if we didn't yet construct the Solovay-Kitaev instance, which contains
# the basic approximations, do it now
if SolovayKitaevSynthesis._sk is None:
basic_approximations = config.get("basic_approximations", None)
basis_gates = options.get("basis_gates", ["h", "t", "tdg"])
# if the basic approximations are not generated and not given,
# try to generate them if the basis set is specified
if basic_approximations is None:
depth = config.get("depth", 10)
basic_approximations = generate_basic_approximations(basis_gates, depth)
SolovayKitaevSynthesis._sk = SolovayKitaevDecomposition(basic_approximations)
approximate_circuit = SolovayKitaevSynthesis._sk.run(unitary, recursion_degree)
dag_circuit = circuit_to_dag(approximate_circuit)
return dag_circuit