multilayer_optimizer

qubit_approximant.core.optimizer.multilayer_optimizer

Incremental optimizer

Classes

MultilayerOptimizer: Base class for the optimization of circuits with multiple layers. NonIncrementalOptimizer: This optimizer uses the parameters of an optimized L layer circuit as input for the optimization of a L+1 layer circuit. IncrementalOptimizer: This optimizer uses the parameters of an optimized L layer circuit as input for the optimization of a L+1 layer circuit.

IncrementalOptimizer(min_layer, max_layer, optimizer, new_layer_coef, new_layer_position)

Bases: MultilayerOptimizer

This optimizer uses the parameters of an optimized L layer circuit as input for the optimization of a L+1 layer circuit.

Attributes:

Name	Type	Description
new_layer_position	str	The position where to add the parameters of the new layer. For, example, it may be the initial or final layer of our circuit.

Parameters:

Name	Type	Description	Default
min_layer	int	Starting number of layers to optimize.	required
max_layer	int	Final number of layers to optimize.	required
optimizer	Optimizer	The optimizer used to find the optimum parameters.	required
new_layer_coef	float	The coefficient that multiplies the normal distribution of the new parameters in the additional layer.	required
new_layer_position	str	The position where to add the parameters of the new layer. For, example, it may be the initial or final layer of our circuit.	required

Source code in qubit_approximant/core/optimizer/multilayer_optimizer.py

def __init__(
    self,
    min_layer,
    max_layer,
    optimizer: Optimizer,
    new_layer_coef: float,
    new_layer_position: str,
) -> None:
    """
    Initialize a black box optimizer.

    Parameters
    ----------
    min_layer : int
        Starting number of layers to optimize.
    max_layer : int
        Final number of layers to optimize.
    optimizer : Optimizer
        The optimizer used to find the optimum parameters.
    new_layer_coef : float
        The coefficient that multiplies the normal distribution of the
        new parameters in the additional layer.
    new_layer_position : str
        The position where to add the parameters of the new layer. For,
        example, it may be the initial or final layer of our circuit.
    """
    if new_layer_position in IncrementalOptimizer.layer_positions:
        self.new_layer_position = new_layer_position
    else:
        raise ValueError(
            f"new_layer_position = {new_layer_position} is not supported. "
            "Try 'initial', 'middle', 'final' or 'random'"
        )
    super().__init__(min_layer, max_layer, optimizer, new_layer_coef)

inital_params_diff: tuple[list[float], list[float]] property

Returns a list with the mean and standard deviation of the difference between the optimum parameters in the i-th layer and the optimum parameters of the (i+1)-th layer. (We exclude the additional parameters added with the new layer).

Returns:

Type	Description
tuple[list[float], list[float]]	Mean and standard deviation of the parameter differences.

Raises:

Type	Description
ValueError	Parameter difference only supported for new initial and final layers.

__call__(cost, grad_cost, init_params)

Calculate the optimized parameters for each number of layers.

Parameters:

Name	Type	Description	Default
cost	Callable	Cost function to be minimized.	required
grad_cost	Callable	Gradient of the cost function.	required
init_params	NDArray	Initial parameter guess for the cost function; used to initialize the optimizer.	required

Returns:

Type	Description
list[NDArray]	The optimum parameters for each number of layers.

Source code in qubit_approximant/core/optimizer/multilayer_optimizer.py

def __call__(self, cost: Callable, grad_cost: Callable, init_params: NDArray) -> list[NDArray]:
    """Calculate the optimized parameters for each number of layers.

    Parameters
    ----------
    cost : Callable
        Cost function to be minimized.
    grad_cost : Callable
        Gradient of the cost function.
    init_params : NDArray
        Initial parameter guess for the cost function; used to initialize the optimizer.

    Returns
    -------
    list[NDArray]
        The optimum parameters for each number of layers.
    """
    self.params_layer = init_params.size // self.min_layer
    params = init_params
    self.params_list = []

    for layer in range(self.min_layer, self.max_layer + 1):
        params = self.optimizer(cost, grad_cost, params)
        self.params_list.append(params)
        params = self._new_initial_params(params, layer)
    return self.params_list

MultilayerOptimizer(min_layer, max_layer, optimizer, new_layer_coef=0.3)

Bases: ABC

This optimizer uses the parameters of an optimized L layer circuit as input for the optimization of a L+1 layer circuit.

Attributes:

Name	Type	Description
min_layer	int	Starting number of layers to optimize.
max_layer	int	Final number of layers to optimize.
optimizer	Optimizer	The optimizer used to find the optimum parameters.
new_layer_coef	float	The coefficient that multiplies the normal distribution of the new parameters in the additional layer.

Parameters:

Name	Type	Description	Default
min_layer	int	Starting number of layers to optimize.	required
max_layer	int	Final number of layers to optimize.	required
optimizer	Optimizer	The optimizer used to find the optimum parameters.	required
new_layer_coef	float	The coefficient that multiplies the normal distribution of the new parameters in the additional layer.	0.3

Source code in qubit_approximant/core/optimizer/multilayer_optimizer.py

def __init__(self, min_layer, max_layer, optimizer: Optimizer, new_layer_coef: float = 0.3):
    """
    Initialize a black box optimizer.

    Parameters
    ----------
    min_layer : int
        Starting number of layers to optimize.
    max_layer : int
        Final number of layers to optimize.
    optimizer : Optimizer
        The optimizer used to find the optimum parameters.
    new_layer_coef : float
        The coefficient that multiplies the normal distribution of the
        new parameters in the additional layer.
    """
    self.min_layer = min_layer
    self.max_layer = max_layer
    self.optimizer = optimizer
    self.new_layer_coef = new_layer_coef

__call__(cost, grad_cost, init_params) abstractmethod

Calculate the optimized parameters for each number of layers.

Parameters:

Name	Type	Description	Default
cost	Callable	Cost function to be minimized.	required
grad_cost	Callable	Gradient of the cost function.	required
init_params	NDArray	Initial parameter guess for the cost function; used to initialize the optimizer.	required

Returns:

Type	Description
list of NDArray	The optimum parameters for each number of layers.

Source code in qubit_approximant/core/optimizer/multilayer_optimizer.py

@abstractmethod
def __call__(self, cost: Callable, grad_cost: Callable, init_params: NDArray) -> list[NDArray]:
    """
    Calculate the optimized parameters for each number of layers.

    Parameters
    ----------
    cost: Callable
        Cost function to be minimized.
    grad_cost: Callable
        Gradient of the cost function.
    init_params : NDArray
        Initial parameter guess for the cost function; used to initialize the optimizer.

    Returns
    -------
    list of NDArray
        The optimum parameters for each number of layers.
    """
    ...

NonIncrementalOptimizer(min_layer, max_layer, optimizer, new_layer_coef)

Bases: MultilayerOptimizer

This optimizer creates new initial parameters for the optimization of a circuit with an additional layer.

Parameters:

Name	Type	Description	Default
min_layer	int	Starting number of layers to optimize.	required
max_layer	int	Final number of layers to optimize.	required
optimizer	Optimizer	The optimizer used to find the optimum parameters.	required
new_layer_coef	float	The coefficient that multiplies the normal distribution of the new parameters in the additional layer.	required

Source code in qubit_approximant/core/optimizer/multilayer_optimizer.py

def __init__(self, min_layer, max_layer, optimizer: Optimizer, new_layer_coef: float):
    """
    Initialize a black box optimizer.

    Parameters
    ----------
    min_layer : int
        Starting number of layers to optimize.
    max_layer : int
        Final number of layers to optimize.
    optimizer : Optimizer
        The optimizer used to find the optimum parameters.
    new_layer_coef : float
        The coefficient that multiplies the normal distribution of the
        new parameters in the additional layer.
    """
    super().__init__(min_layer, max_layer, optimizer, new_layer_coef)

__call__(cost, grad_cost, init_params)

Calculate the optimized parameters for each number of layers.

Parameters:

Name	Type	Description	Default
cost	Callable	Cost function to be minimized.	required
grad_cost	Callable	Gradient of the cost function.	required
init_params	NDArray	Initial parameter guess for the cost function; used to initialize the optimizer.	required

Returns:

Type	Description
list[NDArray]	The optimum parameters for each number of layers.

Source code in qubit_approximant/core/optimizer/multilayer_optimizer.py

def __call__(self, cost: Callable, grad_cost: Callable, init_params: NDArray) -> list[NDArray]:
    """
    Calculate the optimized parameters for each number of layers.

    Parameters
    ----------
    cost: Callable
        Cost function to be minimized.
    grad_cost: Callable
        Gradient of the cost function.
    init_params : NDArray
        Initial parameter guess for the cost function; used to initialize the optimizer.

    Returns
    -------
    list[NDArray]
        The optimum parameters for each number of layers.
    """
    self.params_layer = init_params.size // self.min_layer
    self.params_list = []
    params = init_params
    rng = np.random.default_rng()

    for layer in range(self.min_layer, self.max_layer + 1):
        params = self.optimizer(cost, grad_cost, params)
        self.params_list.append(params)
        params = self.new_layer_coef * rng.standard_normal((layer + 1) * self.params_layer)
    return self.params_list