--- title: AQGD (v0.46) description: API reference for qiskit.algorithms.optimizers.AQGD in qiskit v0.46 source: https://eu-de.quantum.cloud.ibm.com/docs/en/api/qiskit/0.46/qiskit.algorithms.optimizers.AQGD --- # AQGD *class* `qiskit.algorithms.optimizers.AQGD(maxiter=1000, eta=1.0, tol=1e-06, momentum=0.25, param_tol=1e-06, averaging=10)` [GitHub](https://github.com/qiskit/qiskit/tree/stable/0.46/qiskit/algorithms/optimizers/aqgd.py) Bases: [`Optimizer`](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.optimizer.Optimizer") Analytic Quantum Gradient Descent (AQGD) with Epochs optimizer. Performs gradient descent optimization with a momentum term, analytic gradients, and customized step length schedule for parameterized quantum gates, i.e. Pauli Rotations. See, for example: - K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii. (2018). Quantum circuit learning. Phys. Rev. A 98, 032309. [https://arxiv.org/abs/1803.00745](https://arxiv.org/abs/1803.00745) - Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, Nathan Killoran. (2019). Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99, 032331. [https://arxiv.org/abs/1811.11184](https://arxiv.org/abs/1811.11184) for further details on analytic gradients of parameterized quantum gates. Gradients are computed “analytically” using the quantum circuit when evaluating the objective function. Performs Analytical Quantum Gradient Descent (AQGD) with Epochs. **Parameters** - **maxiter** ([*int*](https://docs.python.org/3/library/functions.html#int) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*int*](https://docs.python.org/3/library/functions.html#int)*]*) – Maximum number of iterations (full gradient steps) - **eta** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous\_param - eta \* deriv - **tol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Tolerance for change in windowed average of objective values. Convergence occurs when either objective tolerance is met OR parameter tolerance is met. - **momentum** ([*float*](https://docs.python.org/3/library/functions.html#float) *|*[*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*]*) – Bias towards the previous gradient momentum in current update. Must be within the bounds: \[0,1) - **param\_tol** ([*float*](https://docs.python.org/3/library/functions.html#float)) – Tolerance for change in norm of parameters. - **averaging** ([*int*](https://docs.python.org/3/library/functions.html#int)) – Length of window over which to average objective values for objective convergence criterion **Raises** [**AlgorithmError**](/docs/api/qiskit/0.46/algorithms#qiskit.algorithms.AlgorithmError "qiskit.algorithms.AlgorithmError") – If the length of `maxiter`, momentum\`, and `eta` is not the same. ## 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 ## Methods ### get\_support\_level `get_support_level()` Support level dictionary **Returns** **gradient, bounds and initial point** support information that is ignored/required. **Return type** Dict\[[str](https://docs.python.org/3/library/stdtypes.html#str), [int](https://docs.python.org/3/library/functions.html#int)] ### 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*](https://docs.python.org/3/library/functions.html#float)) – the epsilon used in the numeric differentiation. - **max\_evals\_grouped** ([*int*](https://docs.python.org/3/library/functions.html#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)` Minimize the scalar function. **Parameters** - **fun** (*Callable\[\[POINT],* [*float*](https://docs.python.org/3/library/functions.html#float)*]*) – The scalar function to minimize. - **x0** (*POINT*) – The initial point for the minimization. - **jac** (*Callable\[\[POINT], POINT] | None*) – The gradient of the scalar function `fun`. - **bounds** ([*list*](https://docs.python.org/3/library/stdtypes.html#list)*\[*[*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple)*\[*[*float*](https://docs.python.org/3/library/functions.html#float)*,* [*float*](https://docs.python.org/3/library/functions.html#float)*]] | None*) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds. **Returns** The result of the optimization, containing e.g. the result as attribute `x`. **Return type** [OptimizerResult](/docs/api/qiskit/0.46/qiskit.algorithms.optimizers.OptimizerResult "qiskit.algorithms.optimizers.OptimizerResult") ### print\_options `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*](https://docs.python.org/3/library/stdtypes.html#dict)) – options, given as name=value. ### 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*](https://docs.python.org/3/library/stdtypes.html#tuple)) – the args to be injected **Returns** wrapper **Return type** function\_wrapper