Ok, the source code for the estimator is here:

  
      github.com
  
  
    leockl/helstrom-quantum-centroid-classifier/blob/master/hqc/hqc.py 9
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import check_classification_targets
from sklearn.preprocessing import normalize
from joblib import Parallel, delayed

class HQC(BaseEstimator, ClassifierMixin):
    """The Helstrom Quantum Centroid (HQC) classifier is a quantum-inspired supervised 
    classification approach for data with binary classes (ie. data with 2 classes only).
                         
    Parameters
    ----------
    rescale : int or float, default = 1
        The dataset rescaling factor. A parameter used for rescaling the dataset. 
    encoding : str, default = 'amplit'
        The encoding method used to encode vectors into quantum densities. Possible values:
        'amplit', 'stereo'. 'amplit' means using the amplitude encoding method. 'stereo' means 
        using the inverse of the standard stereographic projection encoding method. Default set 
        to 'amplit'.


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It has already been parallelized for multiple CPUs but it’s still slow when the hyperparameter n_copies is increased. This hyperparameter controls the number of times a Kronecker tensor product is performed, which will result in multiplication of very large matrices when it is increased, therefore slowing down the speed performance when this hyperparameter is large.