# Copyright 2025 Qilimanjaro Quantum Tech
#
# 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 __future__ import annotations
from abc import ABC
from typing import TYPE_CHECKING, Callable, TypeVar, cast, overload
from qilisdk.functionals.functional_result import FunctionalResult
from qilisdk.functionals.sampling import Sampling
from qilisdk.functionals.time_evolution import TimeEvolution
from qilisdk.functionals.variational_program import VariationalProgram
from qilisdk.functionals.variational_program_result import VariationalProgramResult
from qilisdk.settings import get_settings
if TYPE_CHECKING:
from qilisdk.functionals.functional import Functional, PrimitiveFunctional
from qilisdk.functionals.sampling_result import SamplingResult
from qilisdk.functionals.time_evolution_result import TimeEvolutionResult
from qilisdk.noise_models.noise_model import NoiseModel
[docs]
TResult = TypeVar("TResult", bound=FunctionalResult)
[docs]
class Backend(ABC):
def __init__(self) -> None:
self._handlers: dict[type[Functional], Callable[[Functional, NoiseModel | None], FunctionalResult]] = {
Sampling: lambda f, noise_model: self._execute_sampling(cast("Sampling", f), noise_model),
TimeEvolution: lambda f, noise_model: self._execute_time_evolution(cast("TimeEvolution", f), noise_model),
VariationalProgram: lambda f, noise_model: self._execute_variational_program(
cast("VariationalProgram", f), noise_model
),
}
@overload
[docs]
def execute(self, functional: Sampling, noise_model: NoiseModel | None = None) -> SamplingResult: ...
@overload
def execute(self, functional: TimeEvolution, noise_model: NoiseModel | None = None) -> TimeEvolutionResult: ...
@overload
def execute(
self, functional: VariationalProgram[Sampling], noise_model: NoiseModel | None = None
) -> VariationalProgramResult[SamplingResult]: ...
@overload
def execute(
self, functional: VariationalProgram[TimeEvolution], noise_model: NoiseModel | None = None
) -> VariationalProgramResult[TimeEvolutionResult]: ...
@overload
def execute(self, functional: PrimitiveFunctional[TResult], noise_model: NoiseModel | None = None) -> TResult: ...
def execute(self, functional: Functional, noise_model: NoiseModel | None = None) -> FunctionalResult:
try:
handler = self._handlers[type(functional)]
except KeyError as exc:
raise NotImplementedError(
f"{type(self).__qualname__} does not support {type(functional).__qualname__}"
) from exc
return handler(functional, noise_model)
def _execute_sampling(self, functional: Sampling, noise_model: NoiseModel | None = None) -> SamplingResult:
raise NotImplementedError(f"{type(self).__qualname__} has no Sampling implementation")
def _execute_time_evolution(
self, functional: TimeEvolution, noise_model: NoiseModel | None = None
) -> TimeEvolutionResult:
raise NotImplementedError(f"{type(self).__qualname__} has no TimeEvolution implementation")
def _execute_variational_program(
self, functional: VariationalProgram[PrimitiveFunctional[TResult]], noise_model: NoiseModel | None = None
) -> VariationalProgramResult[TResult]:
"""Optimize a Parameterized Program (:class:`~qilisdk.functionals.variational_program.VariationalProgram`)
and returns the optimal parameters and results.
Args:
functional (VariationalProgram): The variational program to be optimized.
Returns:
ParameterizedProgramResults: The final optimizer and functional results.
Raises:
ValueError: If the functional is not parameterized.
"""
def evaluate_sample(parameters: list[float]) -> float:
param_names = functional.functional.get_parameter_names()
param_bounds = functional.functional.get_parameter_bounds()
new_param_dict = {}
for i, param in enumerate(parameters):
name = param_names[i]
lower_bound, upper_bound = param_bounds[name]
if lower_bound != upper_bound:
new_param_dict[name] = param
err = functional.check_parameter_constraints(new_param_dict)
if err > 0:
return err
functional.functional.set_parameters(new_param_dict)
results = self.execute(functional.functional)
final_results = functional.cost_function.compute_cost(results)
if isinstance(final_results, float):
return final_results
if isinstance(final_results, complex) and abs(final_results.imag) < get_settings().atol:
return final_results.real
raise ValueError(f"Unsupported result type {type(final_results)}.")
if len(functional.functional.get_parameters()) == 0:
raise ValueError("Functional provided is not parameterized.")
optimizer_result = functional.optimizer.optimize(
cost_function=evaluate_sample,
init_parameters=list(functional.functional.get_parameters().values()),
bounds=list(functional.functional.get_parameter_bounds().values()),
store_intermediate_results=functional.store_intermediate_results,
)
param_names = functional.functional.get_parameter_names()
optimal_parameter_dict = {param_names[i]: param for i, param in enumerate(optimizer_result.optimal_parameters)}
err = functional.check_parameter_constraints(optimal_parameter_dict)
if err > 0:
raise ValueError(
"Optimizer Failed at finding an optimal solution. Check the parameter constraints or try with a different optimization method."
)
functional.functional.set_parameters(optimal_parameter_dict)
optimal_results: TResult = cast("TResult", self.execute(functional.functional))
return VariationalProgramResult(optimizer_result=optimizer_result, result=optimal_results)
