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SteppableOptimizer

class qiskit.algorithms.optimizers.SteppableOptimizer(maxiter=100)

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Bases: Optimizer

Base class for a steppable optimizer.

This family of optimizers uses the ask and tell interface. When using this interface the user has to call ask() to get information about how to evaluate the fucntion (we are asking the optimizer about how to do the evaluation). This information is typically the next points at which the function is evaluated, but depending on the optimizer it can also determine whether to evaluate the function or its gradient. Once the function has been evaluated, the user calls the method tell() to tell the optimizer what the result of the function evaluation(s) is. The optimizer then updates its state accordingly and the user can decide whether to stop the optimization process or to repeat a step.

This interface is more customizable, and allows the user to have full control over the evaluation of the function.

Examples

An example where the evaluation of the function has a chance of failing. The user, with specific knowledge about his function can catch this errors and handle them before passing the result to the optimizer.

import random
import numpy as np
from qiskit.algorithms.optimizers import GradientDescent
 
def objective(x):
    if random.choice([True, False]):
        return None
    else:
        return (np.linalg.norm(x) - 1) ** 2
 
def grad(x):
    if random.choice([True, False]):
        return None
    else:
        return 2 * (np.linalg.norm(x) - 1) * x / np.linalg.norm(x)
 
 
initial_point = np.random.normal(0, 1, size=(100,))
 
optimizer = GradientDescent(maxiter=20)
optimizer.start(x0=initial_point, fun=objective, jac=grad)
 
while optimizer.continue_condition():
    ask_data = optimizer.ask()
    evaluated_gradient = None
 
    while evaluated_gradient is None:
        evaluated_gradient = grad(ask_data.x_center)
        optimizer.state.njev += 1
 
    optmizer.state.nit += 1
 
     cf  = TellData(eval_jac=evaluated_gradient)
    optimizer.tell(ask_data=ask_data, tell_data=tell_data)
 
result = optimizer.create_result()

Users that aren’t dealing with complicated functions and who are more familiar with step by step optimization algorithms can use the step() method which wraps the ask() and tell() methods. In the same spirit the method minimize() will optimize the function and return the result.

To see other libraries that use this interface one can visit: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/009_ask_and_tell.html

Parameters

maxiter (int) – Number of steps in the optimization process before ending the loop.


Attributes

bounds_support_level

Returns bounds support level

gradient_support_level

Returns gradient support level

initial_point_support_level

Returns initial point support level

is_bounds_ignored

Returns is bounds ignored

is_bounds_required

Returns is bounds required

is_bounds_supported

Returns is bounds supported

is_gradient_ignored

Returns is gradient ignored

is_gradient_required

Returns is gradient required

is_gradient_supported

Returns is gradient supported

is_initial_point_ignored

Returns is initial point ignored

is_initial_point_required

Returns is initial point required

is_initial_point_supported

Returns is initial point supported

setting

Return setting

settings

The optimizer settings in a dictionary format.

The settings can for instance be used for JSON-serialization (if all settings are serializable, which e.g. doesn’t hold per default for callables), such that the optimizer object can be reconstructed as

settings = optimizer.settings
# JSON serialize and send to another server
optimizer = OptimizerClass(**settings)

state

Return the current state of the optimizer.


Methods

ask

ask()

Ask the optimizer for a set of points to evaluate.

This method asks the optimizer which are the next points to evaluate. These points can, e.g., correspond to function values and/or its derivative. It may also correspond to variables that let the user infer which points to evaluate. It is the first method inside of a step() in the optimization process.

Returns

An object containing the data needed to make the funciton evaluation to advance the optimization process.

Return type

AskData

continue_condition

continue_condition()

Condition that indicates the optimization process should continue.

Returns

True if the optimization process should continue, False otherwise.

Return type

bool

create_result

abstract create_result()

Returns the result of the optimization.

All the information needed to create such a result should be stored in the optimizer state and will typically contain the best point found, the function value and gradient at that point, the number of function and gradient evaluation and the number of iterations in the optimization.

Returns

The result of the optimization process.

Return type

OptimizerResult

evaluate

abstract evaluate(ask_data)

Evaluates the function according to the instructions contained in ask_data.

If the user decides to use step() instead of ask() and tell() this function will contain the logic on how to evaluate the function.

Parameters

ask_data (AskData) – Contains the information on how to do the evaluation.

Returns

Data of all relevant information about the function evaluation.

Return type

TellData

get_support_level

abstract get_support_level()

Return support level dictionary

gradient_num_diff

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parameters

  • x_center (ndarray) – point around which we compute the gradient
  • f (func) – the function of which the gradient is to be computed.
  • epsilon (float) – the epsilon used in the numeric differentiation.
  • max_evals_grouped (int) – max evals grouped, defaults to 1 (i.e. no batching).

Returns

the gradient computed

Return type

grad

minimize

minimize(fun, x0, jac=None, bounds=None)

Minimizes the function.

For well behaved functions the user can call this method to minimize a function. If the user wants more control on how to evaluate the function a custom loop can be created using ask() and tell() and evaluating the function manually.

Parameters

  • fun (Callable[[POINT], float]) – Function to minimize.
  • x0 (POINT) – Initial point.
  • jac (Callable[[POINT], POINT] | None) – Function to compute the gradient.
  • bounds (list[tuple[float, float]] | None) – Bounds of the search space.

Returns

Object containing the result of the optimization.

Return type

OptimizerResult

print_options()

Print algorithm-specific options.

set_max_evals_grouped

set_max_evals_grouped(limit)

Set max evals grouped

set_options

set_options(**kwargs)

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parameters

kwargs (dict) – options, given as name=value.

start

abstract start(fun, x0, jac=None, bounds=None)

Populates the state of the optimizer with the data provided and sets all the counters to 0.

Parameters

  • fun (Callable[[POINT], float]) – Function to minimize.
  • x0 (POINT) – Initial point.
  • jac (Callable[[POINT], POINT] | None) – Function to compute the gradient.
  • bounds (list[tuple[float, float]] | None) – Bounds of the search space.

step

step()

Performs one step in the optimization process.

This method composes ask(), evaluate(), and tell() to make a “step” in the optimization process.

tell

tell(ask_data, tell_data)

Updates the optimization state using the results of the function evaluation.

A canonical optimization example using ask() and tell() can be seen in step().

Parameters

  • ask_data (AskData) – Contains the information on how the evaluation was done.
  • tell_data (TellData) – Contains all relevant information about the evaluation of the objective function.

wrap_function

static wrap_function(function, args)

Wrap the function to implicitly inject the args at the call of the function.

Parameters

  • function (func) – the target function
  • args (tuple) – the args to be injected

Returns

wrapper

Return type

function_wrapper

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