324 lines
14 KiB
Plaintext
324 lines
14 KiB
Plaintext
---
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title: QNSPSA
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description: API reference for qiskit.algorithms.optimizers.QNSPSA
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.algorithms.optimizers.QNSPSA
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---
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# QNSPSA
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<Class id="qiskit.algorithms.optimizers.QNSPSA" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.18/qiskit/algorithms/optimizers/qnspsa.py" signature="QNSPSA(fidelity, maxiter=100, blocking=True, allowed_increase=None, learning_rate=None, perturbation=None, last_avg=1, resamplings=1, perturbation_dims=None, regularization=None, hessian_delay=0, lse_solver=None, initial_hessian=None, callback=None)" modifiers="class">
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Bases: `qiskit.algorithms.optimizers.spsa.SPSA`
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The Quantum Natural SPSA (QN-SPSA) optimizer.
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The QN-SPSA optimizer \[1] is a stochastic optimizer that belongs to the family of gradient descent methods. This optimizer is based on SPSA but attempts to improve the convergence by sampling the **natural gradient** instead of the vanilla, first-order gradient. It achieves this by approximating Hessian of the `fidelity` of the ansatz circuit.
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Compared to natural gradients, which require $\mathcal{O}(d^2)$ expectation value evaluations for a circuit with $d$ parameters, QN-SPSA only requires $\mathcal{O}(1)$ and can therefore significantly speed up the natural gradient calculation by sacrificing some accuracy. Compared to SPSA, QN-SPSA requires 4 additional function evaluations of the fidelity.
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The stochastic approximation of the natural gradient can be systematically improved by increasing the number of `resamplings`. This leads to a Monte Carlo-style convergence to the exact, analytic value.
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**Examples**
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This short example runs QN-SPSA for the ground state calculation of the `Z ^ Z` observable where the ansatz is a `PauliTwoDesign` circuit.
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```python
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import numpy as np
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from qiskit.algorithms.optimizers import QNSPSA
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from qiskit.circuit.library import PauliTwoDesign
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from qiskit.opflow import Z, StateFn
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ansatz = PauliTwoDesign(2, reps=1, seed=2)
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observable = Z ^ Z
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initial_point = np.random.random(ansatz.num_parameters)
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def loss(x):
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bound = ansatz.bind_parameters(x)
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return np.real((StateFn(observable, is_measurement=True) @ StateFn(bound)).eval())
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fidelity = QNSPSA.get_fidelity(ansatz)
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qnspsa = QNSPSA(fidelity, maxiter=300)
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result = qnspsa.optimize(ansatz.num_parameters, loss, initial_point=initial_point)
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```
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**References**
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\[1] J. Gacon et al, “Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information”, [arXiv:2103.09232](https://arxiv.org/abs/2103.09232)
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**Parameters**
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* **fidelity** (`Callable`\[\[`ndarray`, `ndarray`], `float`]) – A function to compute the fidelity of the ansatz state with itself for two different sets of parameters.
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* **maxiter** (`int`) – The maximum number of iterations. Note that this is not the maximal number of function evaluations.
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* **blocking** (`bool`) – If True, only accepts updates that improve the loss (up to some allowed increase, see next argument).
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* **allowed\_increase** (`Optional`\[`float`]) – If `blocking` is `True`, this argument determines by how much the loss can increase with the proposed parameters and still be accepted. If `None`, the allowed increases is calibrated automatically to be twice the approximated standard deviation of the loss function.
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* **learning\_rate** (`Union`\[`float`, `Callable`\[\[], `Iterator`], `None`]) – The update step is the learning rate is multiplied with the gradient. If the learning rate is a float, it remains constant over the course of the optimization. It can also be a callable returning an iterator which yields the learning rates for each optimization step. If `learning_rate` is set `perturbation` must also be provided.
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* **perturbation** (`Union`\[`float`, `Callable`\[\[], `Iterator`], `None`]) – Specifies the magnitude of the perturbation for the finite difference approximation of the gradients. Can be either a float or a generator yielding the perturbation magnitudes per step. If `perturbation` is set `learning_rate` must also be provided.
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* **last\_avg** (`int`) – Return the average of the `last_avg` parameters instead of just the last parameter values.
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* **resamplings** (`Union`\[`int`, `Dict`\[`int`, `int`]]) – The number of times the gradient (and Hessian) is sampled using a random direction to construct a gradient estimate. Per default the gradient is estimated using only one random direction. If an integer, all iterations use the same number of resamplings. If a dictionary, this is interpreted as `{iteration: number of resamplings per iteration}`.
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* **perturbation\_dims** (`Optional`\[`int`]) – The number of perturbed dimensions. Per default, all dimensions are perturbed, but a smaller, fixed number can be perturbed. If set, the perturbed dimensions are chosen uniformly at random.
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* **regularization** (`Optional`\[`float`]) – To ensure the preconditioner is symmetric and positive definite, the identity times a small coefficient is added to it. This generator yields that coefficient.
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* **hessian\_delay** (`int`) – Start multiplying the gradient with the inverse Hessian only after a certain number of iterations. The Hessian is still evaluated and therefore this argument can be useful to first get a stable average over the last iterations before using it as preconditioner.
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* **lse\_solver** (`Optional`\[`Callable`\[\[`ndarray`, `ndarray`], `ndarray`]]) – The method to solve for the inverse of the Hessian. Per default an exact LSE solver is used, but can e.g. be overwritten by a minimization routine.
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* **initial\_hessian** (`Optional`\[`ndarray`]) – The initial guess for the Hessian. By default the identity matrix is used.
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* **callback** (`Optional`\[`Callable`\[\[`ndarray`, `float`, `float`, `int`, `bool`], `None`]]) – A callback function passed information in each iteration step. The information is, in this order: the parameters, the function value, the number of function evaluations, the stepsize, whether the step was accepted.
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## Methods
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### calibrate
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<Function id="qiskit.algorithms.optimizers.QNSPSA.calibrate" signature="QNSPSA.calibrate(loss, initial_point, c=0.2, stability_constant=0, target_magnitude=None, alpha=0.602, gamma=0.101, modelspace=False)" modifiers="static">
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Calibrate SPSA parameters with a powerseries as learning rate and perturbation coeffs.
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The powerseries are:
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$$
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a_k = \frac{a}{(A + k + 1)^\alpha}, c_k = \frac{c}{(k + 1)^\gamma}
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$$
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**Parameters**
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* **loss** (`Callable`\[\[`ndarray`], `float`]) – The loss function.
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* **initial\_point** (`ndarray`) – The initial guess of the iteration.
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* **c** (`float`) – The initial perturbation magnitude.
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* **stability\_constant** (`float`) – The value of A.
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* **target\_magnitude** (`Optional`\[`float`]) – The target magnitude for the first update step, defaults to $2\pi / 10$.
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* **alpha** (`float`) – The exponent of the learning rate powerseries.
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* **gamma** (`float`) – The exponent of the perturbation powerseries.
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* **modelspace** (`bool`) – Whether the target magnitude is the difference of parameter values or function values (= model space).
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**Returns**
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**A tuple of powerseries generators, the first one for the**
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learning rate and the second one for the perturbation.
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**Return type**
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tuple(generator, generator)
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</Function>
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### estimate\_stddev
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<Function id="qiskit.algorithms.optimizers.QNSPSA.estimate_stddev" signature="QNSPSA.estimate_stddev(loss, initial_point, avg=25)" modifiers="static">
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Estimate the standard deviation of the loss function.
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**Return type**
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`float`
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</Function>
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### get\_fidelity
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<Function id="qiskit.algorithms.optimizers.QNSPSA.get_fidelity" signature="QNSPSA.get_fidelity(circuit, backend=None, expectation=None)" modifiers="static">
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Get a function to compute the fidelity of `circuit` with itself.
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Let `circuit` be a parameterized quantum circuit performing the operation $U(\theta)$ given a set of parameters $\theta$. Then this method returns a function to evaluate
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$$
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F(\theta, \phi) = \big|\langle 0 | U^\dagger(\theta) U(\phi) |0\rangle \big|^2.
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$$
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The output of this function can be used as input for the `fidelity` to the :class:\~\`qiskit.algorithms.optimizers.QNSPSA\` optimizer.
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**Parameters**
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* **circuit** (`QuantumCircuit`) – The circuit preparing the parameterized ansatz.
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* **backend** (`Union`\[`Backend`, `QuantumInstance`, `None`]) – A backend of quantum instance to evaluate the circuits. If None, plain matrix multiplication will be used.
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* **expectation** (`Optional`\[`ExpectationBase`]) – An expectation converter to specify how the expected value is computed. If a shot-based readout is used this should be set to `PauliExpectation`.
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**Return type**
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`Callable`\[\[`ndarray`, `ndarray`], `float`]
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**Returns**
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A handle to the function $F$.
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</Function>
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.QNSPSA.get_support_level" signature="QNSPSA.get_support_level()">
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Get the support level dictionary.
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</Function>
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.QNSPSA.gradient_num_diff" signature="QNSPSA.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" modifiers="static">
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We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center.
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**Parameters**
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* **x\_center** (*ndarray*) – point around which we compute the gradient
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* **f** (*func*) – the function of which the gradient is to be computed.
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* **epsilon** (*float*) – the epsilon used in the numeric differentiation.
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* **max\_evals\_grouped** (*int*) – max evals grouped
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**Returns**
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the gradient computed
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**Return type**
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grad
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</Function>
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### optimize
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<Function id="qiskit.algorithms.optimizers.QNSPSA.optimize" signature="QNSPSA.optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
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Perform optimization.
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**Parameters**
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* **num\_vars** (*int*) – Number of parameters to be optimized.
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* **objective\_function** (*callable*) – A function that computes the objective function.
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* **gradient\_function** (*callable*) – A function that computes the gradient of the objective function, or None if not available.
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* **variable\_bounds** (*list\[(float, float)]*) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
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* **initial\_point** (*numpy.ndarray\[float]*) – Initial point.
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**Returns**
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**point, value, nfev**
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point: is a 1D numpy.ndarray\[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None
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**Raises**
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**ValueError** – invalid input
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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.QNSPSA.print_options" signature="QNSPSA.print_options()">
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Print algorithm-specific options.
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</Function>
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.QNSPSA.set_max_evals_grouped" signature="QNSPSA.set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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### set\_options
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<Function id="qiskit.algorithms.optimizers.QNSPSA.set_options" signature="QNSPSA.set_options(**kwargs)">
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Sets or updates values in the options dictionary.
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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.
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**Parameters**
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**kwargs** (*dict*) – options, given as name=value.
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</Function>
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.QNSPSA.wrap_function" signature="QNSPSA.wrap_function(function, args)" modifiers="static">
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Wrap the function to implicitly inject the args at the call of the function.
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**Parameters**
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* **function** (*func*) – the target function
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* **args** (*tuple*) – the args to be injected
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**Returns**
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wrapper
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**Return type**
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function\_wrapper
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</Function>
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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.gradient_support_level">
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Returns gradient support level
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</Attribute>
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### initial\_point\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.initial_point_support_level">
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Returns initial point support level
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</Attribute>
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### is\_bounds\_ignored
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_bounds_ignored">
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Returns is bounds ignored
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</Attribute>
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### is\_bounds\_required
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_bounds_required">
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Returns is bounds required
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</Attribute>
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### is\_bounds\_supported
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_bounds_supported">
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Returns is bounds supported
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</Attribute>
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### is\_gradient\_ignored
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_gradient_ignored">
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Returns is gradient ignored
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</Attribute>
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### is\_gradient\_required
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_gradient_required">
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Returns is gradient required
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</Attribute>
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### is\_gradient\_supported
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_gradient_supported">
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Returns is gradient supported
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</Attribute>
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### is\_initial\_point\_ignored
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_initial_point_ignored">
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Returns is initial point ignored
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</Attribute>
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### is\_initial\_point\_required
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_initial_point_required">
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Returns is initial point required
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</Attribute>
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### is\_initial\_point\_supported
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.setting">
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Return setting
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</Attribute>
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### settings
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<Attribute id="qiskit.algorithms.optimizers.QNSPSA.settings">
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The optimizer settings in a dictionary format.
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**Return type**
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`Dict`\[`str`, `Any`]
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</Attribute>
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</Class>
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