Bump qiskit-machine-learning from 0.5.0 to 0.6.0

[dependabot]

Bumps qiskit-machine-learning from 0.5.0 to 0.6.0.

Release notes

Sourced from qiskit-machine-learning's releases.

Qiskit Machine Learning 0.6.0

Changelog

New Features

• Allow callable as an optimizer in NeuralNetworkClassifier, VQC, NeuralNetworkRegressor, VQR, as well as in QuantumKernelTrainer.

  Now, the optimizer can either be one of Qiskit’s optimizers, such as SPSA or a callable with the following signature:

      from qiskit.algorithms.optimizers import OptimizerResult
  def my_optimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
      # Args:
      #     fun (callable): the function to minimize
      #     x0 (np.ndarray): the initial point for the optimization
      #     jac (callable, optional): the gradient of the objective function
      #     bounds (list, optional): a list of tuples specifying the parameter bounds
      result = OptimizerResult()
      result.x = # optimal parameters
      result.fun = # optimal function value
      return result

      The above signature also allows to directly pass any SciPy minimizer, for instance as

      from functools import partial
      from scipy.optimize import minimize
      optimizer = partial(minimize, method="L-BFGS-B")

• Added a new FidelityStatevectorKernel class that is optimized to use only statevector-implemented feature maps. Therefore, computational complexity is reduced from $O(N^2)$ to $O(N)$.

  Computed statevector arrays are also cached to further increase efficiency. This cache is cleared when the evaluate method is called, unless auto_clear_cache is False. The cache is unbounded by default, but its size can be set by the user, i.e., limited to the number of samples in the worst case.

  By default the Terra reference Statevector is used, however, the type can be specified via the statevector_type argument.

  Shot noise emulation can also be added. If shots is None, the exact fidelity is used. Otherwise, the mean is taken of samples drawn from a binomial distribution with probability equal to the exact fidelity.

  With the addition of shot noise, the kernel matrix may no longer be positive semi-definite (PSD). With enforce_psd set to True this condition is enforced.

  An example of using this class is as follows:

    from sklearn.datasets import make_blobs
    from sklearn.svm import SVC
from qiskit.circuit.library import ZZFeatureMap
from qiskit.quantum_info import Statevector

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Commits

• 6b3c65a Prepare 0.6 Release (#586)
• 2c771cc Enable sparse support in TorchConnector and other minor updates (#571)
• 9596dc4 Fix renos (#584)
• 55baa9d deploy documentation in qiskit.org/ecosystem (#581)
• ffe2b6f Fix docstrings in VQR (#578)
• a8621a1 Update deploy docs workflow and codeowners (#575)
• 7050517 Improving a sentence in getting_started.rst (#580)
• 3a869ae Remove unnecessary html_theme_path option (#573)
• 30ea030 Fix spelling in deprecated functions/classes (#574)
• 5abda6c Update slack invite link (#568)
• Additional commits viewable in compare view

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