scikit-quant is an aggregator package to improve interoperability between quantum computing software packages. Our first focus in on classical optimizers, making the state-of-the art from the Applied Math community available in Python for use in quantum computing.

Full documentation: https://scikit-quant.readthedocs.io/

Website: http://scikit-quant.org

pip install sckit-quant

Basic example (component interfaces for standard quantum programming frameworks and for SciPy are available as well):

# create a numpy array of bounds, one (low, high) for each parameter
bounds = np.array([[-1, 1], [-1, 1]], dtype=float)

# budget (number of calls, assuming 1 count per call)
budget = 40

# initial values for all parameters
x0 = np.array([0.5, 0.5])

# method can be ImFil, SnobFit, Orbit, or Bobyqa
result, history = \
    minimize(objective_function, x0, bounds, budget, method='imfil')