# Source code for pyquil.api._qac

```
##############################################################################
# Copyright 2018 Rigetti Computing
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Union
from rpcq.messages import BinaryExecutableResponse, PyQuilExecutableResponse
from pyquil.paulis import PauliTerm
from pyquil.quil import Program
from pyquil.quilbase import Gate
[docs]class AbstractCompiler(ABC):
"""The abstract interface for a compiler."""
def get_version_info(self) -> Dict[str, Any]:
"""
Return version information for this compiler and its dependencies.
:return: Dictionary of version information.
"""
raise NotImplementedError
@abstractmethod
def quil_to_native_quil(self, program: Program, *, protoquil: Optional[bool] = None) -> Program:
"""
Compile an arbitrary quil program according to the ISA of target_device.
:param program: Arbitrary quil to compile
:param protoquil: Whether to restrict to protoquil (``None`` means defer to server)
:return: Native quil and compiler metadata
"""
@abstractmethod
def native_quil_to_executable(
self, nq_program: Program
) -> Union[BinaryExecutableResponse, PyQuilExecutableResponse]:
"""
Compile a native quil program to a binary executable.
:param nq_program: Native quil to compile
:return: An (opaque) binary executable
"""
def reset(self) -> None:
pass
class AbstractBenchmarker(ABC):
@abstractmethod
def apply_clifford_to_pauli(self, clifford: Program, pauli_in: PauliTerm) -> PauliTerm:
r"""
Given a circuit that consists only of elements of the Clifford group,
return its action on a PauliTerm.
In particular, for Clifford C, and Pauli P, this returns the PauliTerm
representing PCP^{\dagger}.
:param clifford: A Program that consists only of Clifford operations.
:param pauli_in: A PauliTerm to be acted on by clifford via conjugation.
:return: A PauliTerm corresponding to pauli_in * clifford * pauli_in^{\dagger}
"""
@abstractmethod
def generate_rb_sequence(
self,
depth: int,
gateset: Sequence[Gate],
seed: Optional[int] = None,
interleaver: Optional[Program] = None,
) -> List[Program]:
"""
Construct a randomized benchmarking experiment on the given qubits, decomposing into
gateset. If interleaver is not provided, the returned sequence will have the form
C_1 C_2 ... C_(depth-1) C_inv ,
where each C is a Clifford element drawn from gateset, C_{< depth} are randomly selected,
and C_inv is selected so that the entire sequence composes to the identity. If an
interleaver G (which must be a Clifford, and which will be decomposed into the native
gateset) is provided, then the sequence instead takes the form
C_1 G C_2 G ... C_(depth-1) G C_inv .
The JSON response is a list of lists of indices, or Nones. In the former case, they are the
index of the gate in the gateset.
:param int depth: The number of Clifford gates to include in the randomized benchmarking
experiment. This is different than the number of gates in the resulting experiment.
:param list gateset: A list of pyquil gates to decompose the Clifford elements into. These
must generate the clifford group on the qubits of interest. e.g. for one qubit
[RZ(np.pi/2), RX(np.pi/2)].
:param seed: A positive integer used to seed the PRNG.
:param interleaver: A Program object that encodes a Clifford element.
:return: A list of pyquil programs. Each pyquil program is a circuit that represents an
element of the Clifford group. When these programs are composed, the resulting Program
will be the randomized benchmarking experiment of the desired depth. e.g. if the return
programs are called cliffords then `sum(cliffords, Program())` will give the randomized
benchmarking experiment, which will compose to the identity program.
"""
```