# Copyright (C) Unitary Fund
#
# This source code is licensed under the GPL license (v3) found in the
# LICENSE file in the root directory of this source tree.
"""Defines input / output types for a quantum computer (simulator):
* SUPPORTED_PROGRAM_TYPES: All supported packages / circuits which Mitiq can
interface with,
* QPROGRAM: All supported packages / circuits which are installed in the
environment Mitiq is run in, and
* QuantumResult: An object returned by a quantum computer (simulator) running
a quantum program from which expectation values to be mitigated can be
computed. Note this includes expectation values themselves.
"""
from collections import Counter
from dataclasses import dataclass
from enum import Enum, EnumMeta
from typing import (
Any,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import numpy as np
import numpy.typing as npt
from cirq import Circuit as _Circuit
class EnhancedEnumMeta(EnumMeta):
def __str__(cls) -> str:
return ", ".join(
[member.name.lower() for member in cast(Type[Enum], cls)]
)
class EnhancedEnum(Enum, metaclass=EnhancedEnumMeta):
# This is for backwards compatibility with the old representation
# of SUPPORTED_PROGRAM_TYPES, which was a dictionary
@classmethod
def keys(cls) -> list[str]:
return [member.name.lower() for member in cls]
try:
from pyquil import Program as _Program
except ImportError: # pragma: no cover
_Program = _Circuit # type: ignore
try:
from qiskit import QuantumCircuit as _QuantumCircuit
except ImportError: # pragma: no cover
_QuantumCircuit = _Circuit
try:
from braket.circuits import Circuit as _BKCircuit
except ImportError: # pragma: no cover
_BKCircuit = _Circuit
try:
from pennylane.tape import QuantumTape as _QuantumTape
except ImportError: # pragma: no cover
_QuantumTape = _Circuit
try:
from qibo.models.circuit import Circuit as _QiboCircuit
except ImportError: # pragma: no cover
_QiboCircuit = _Circuit
# Supported + installed quantum programs.
QPROGRAM = Union[
_Circuit, _Program, _QuantumCircuit, _BKCircuit, _QuantumTape, _QiboCircuit
]
# Supported quantum programs.
class SUPPORTED_PROGRAM_TYPES(EnhancedEnum):
BRAKET = _BKCircuit
CIRQ = _Circuit
PENNYLANE = _QuantumTape
PYQUIL = _Program
QIBO = _QiboCircuit
QISKIT = _QuantumCircuit
# Define MeasurementResult, a result obtained by measuring qubits on a quantum
# computer.
Bitstring = Union[str, List[int]]
[docs]
@dataclass
class MeasurementResult:
"""Mitiq object for collecting the bitstrings sampled from a quantum
computer when executing a circuit. This is one of the possible types
(see :class:`~mitiq.typing.QuantumResult`) that an
:class:`.Executor` can return.
Args:
result:
The sequence of measured bitstrings.
qubit_indices:
The qubit indices associated to each
bit in a bitstring (from left to right).
If not given, Mitiq assumes the default ordering
``tuple(range(self.nqubits))``, where ``self.nqubits``
is the bitstring length deduced from ``result``.
Example:
>>> mr = MeasurementResult(["001", "010", "001"])
>>> mr.get_counts()
{'001': 2, '010': 1}
Warning:
Use caution when selecting the default option for ``qubit_indices``,
especially when estimating an :class:`.Observable`
acting on a subset of qubits. In this case Mitiq
only applies measurement gates to the specific qubits and, therefore,
it is essential to specify the corresponding ``qubit_indices``.
"""
result: Sequence[Bitstring]
qubit_indices: Optional[Tuple[int, ...]] = None
def __post_init__(self) -> None:
# Validate arguments
if isinstance(self.result, dict):
raise TypeError(
"Use the MeasurementResult.from_counts method to instantiate "
"a MeasurementResult object from a dictionary."
)
symbols = set(b for bits in self.result for b in bits)
if not (symbols.issubset({0, 1}) or symbols.issubset({"0", "1"})):
raise ValueError("Bitstrings should look like '011' or [0, 1, 1].")
if symbols.issubset({"0", "1"}):
# Convert to list of integers
int_result = [[int(b) for b in bits] for bits in self.result]
self.result: List[List[int]] = list(int_result)
if isinstance(self.result, np.ndarray):
self.result = self.result.tolist()
self._bitstrings = np.array(self.result)
if not self.qubit_indices:
self.qubit_indices = tuple(range(self.nqubits))
else:
if len(self.qubit_indices) != self.nqubits:
raise ValueError(
f"MeasurementResult has {self.nqubits} qubit(s) but there "
f"are {len(self.qubit_indices)} `qubit_indices`."
)
self._measurements = dict(zip(self.qubit_indices, self._bitstrings.T))
@property
def shots(self) -> int:
return self._bitstrings.shape[0]
@property
def nqubits(self) -> int:
return (
self._bitstrings.shape[1]
if len(self._bitstrings.shape) >= 2
else 0
)
@property
def asarray(self) -> npt.NDArray[np.int64]:
return self._bitstrings
[docs]
@classmethod
def from_counts(
cls,
counts: Dict[str, int],
qubit_indices: Optional[Tuple[int, ...]] = None,
) -> "MeasurementResult":
"""Initializes a ``MeasurementResult`` from a dictionary of counts.
**Example**::
MeasurementResult.from_counts({"00": 175, "11": 177})
"""
counter = Counter(counts)
return cls(list(counter.elements()), qubit_indices)
[docs]
def get_counts(self) -> Dict[str, int]:
"""Returns a Python dictionary whose keys are the measured
bitstrings and whose values are the counts.
"""
strings = ["".join(map(str, bits)) for bits in self.result]
return dict(Counter(strings))
[docs]
def prob_distribution(self) -> Dict[str, float]:
"""Returns a Python dictionary whose keys are the measured
bitstrings and whose values are their empirical frequencies.
"""
return {k: v / self.shots for k, v in self.get_counts().items()}
[docs]
def to_dict(self) -> Dict[str, Any]:
"""Exports data to a Python dictionary.
Note: Information about the order measurements is not preserved.
"""
return {
"nqubits": self.nqubits,
"qubit_indices": self.qubit_indices,
"shots": self.shots,
"counts": self.get_counts(),
}
[docs]
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "MeasurementResult":
"""Loads a ``MeasurementResult`` from a Python dictionary.
Note: Only ``data["counts"]`` and ``data["qubit_indices"]`` are used
by this method. Total shots and number of qubits are deduced.
"""
return cls.from_counts(data["counts"], data["qubit_indices"])
[docs]
def filter_qubits(self, qubit_indices: List[int]) -> npt.NDArray[np.int64]:
"""Returns the bitstrings associated to a subset of qubits."""
return np.array([self._measurements[i] for i in qubit_indices]).T
def __repr__(self) -> str:
# We redefine __repr__ in this way to avoid very long output strings.
return "MeasurementResult: " + str(self.to_dict())
# An `executor` function inputs a quantum program and outputs an object from
# which expectation values can be computed. Explicitly, this object can be one
# of the following types:
QuantumResult = Union[
float, # The expectation value itself.
MeasurementResult, # Sampled bitstrings.
np.ndarray, # Density matrix.
# TODO: Support the following:
# Sequence[np.ndarray], # Wavefunctions sampled via quantum trajectories.
]
