ERP EEG decoding with Quantum Classifier.

Decoding applied to EEG data in sensor space using RG. Xdawn spatial filtering is applied on covariances matrices, which are then projected in the tangent space and classified with a quantum SVM classifier. It is compared to the classical SVM on binary classification.

# Author: Gregoire Cattan
# Modified from plot_classify_EEG_tangentspace.py of pyRiemann
# License: BSD (3-clause)

from matplotlib import pyplot as plt
from pyriemann.estimation import XdawnCovariances
from pyriemann.tangentspace import TangentSpace
from sklearn.metrics import (
    ConfusionMatrixDisplay,
    balanced_accuracy_score,
    confusion_matrix,
)
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline

from pyriemann_qiskit.classification import QuanticSVM
from pyriemann_qiskit.utils.dataset import get_mne_sample
from pyriemann_qiskit.utils.filtering import NaiveDimRed

print(__doc__)

X, y = get_mne_sample(n_trials=-1)

# ...skipping the KFold validation parts (for the purpose of the test only)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

Decoding in tangent space with a quantum classifier

# Time complexity of quantum algorithm depends on the number of trials and
# the number of elements inside the covariance matrices
# Thus we reduce elements number by using restrictive spatial filtering
sf = XdawnCovariances(nfilter=1)

# Projecting correlation matrices into the tangent space
# as quantum algorithms take vectors as inputs
# (If not, then matrices will be inlined inside the quantum classifier)
tg = TangentSpace()


# ...and dividing the number of remaining elements by two
dim_red = NaiveDimRed()


# https://stackoverflow.com/questions/61825227/plotting-multiple-confusion-matrix-side-by-side
f, axes = plt.subplots(1, 2, sharey="row")

disp = None

# Results will be computed for QuanticSVM versus SKLearnSVM for comparison
for quantum in [True, False]:
    qsvm = QuanticSVM(verbose=True, quantum=quantum)
    clf = make_pipeline(sf, tg, dim_red, qsvm)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)

    # Printing the results
    acc = balanced_accuracy_score(y_test, y_pred)
    acc_str = "%0.2f" % acc

    names = ["vis left", "vis right"]
    title = ("Quantum (" if quantum else "Classical (") + acc_str + ")"
    axe = axes[0 if quantum else 1]
    cm = confusion_matrix(y_pred, y_test)
    disp = ConfusionMatrixDisplay(cm, display_labels=names)
    disp.plot(ax=axe, xticks_rotation=45)
    disp.ax_.set_title(title)
    disp.im_.colorbar.remove()
    disp.ax_.set_xlabel("")
    if not quantum:
        disp.ax_.set_ylabel("")

if disp:
    f.text(0.4, 0.1, "Predicted label", ha="left")
    plt.subplots_adjust(wspace=0.40, hspace=0.1)
    f.colorbar(disp.im_, ax=axes)
    plt.show()

[QClass]  Initializing Quantum Classifier
[QClass]  Quantum simulation will be performed
GPU optimization disabled. No device found.
[QClass]  Fitting:  (99, 5)
[QClass]  Feature dimension =  5
[QClass]  Quantum backend =  AerSimulator('aer_simulator_statevector')
[QClass]  seed =  580523
[QClass]  SVM initiating algorithm
Computing symbolic Statevector
Dumping to symb_statevectors/ZZFeatureMap-2-5
[QClass]  Training...
n_jobs greater than 1, parallelizing
n_jobs greater than 1, parallelizing
[QClass]  Prediction finished.
[QClass]  Initializing Quantum Classifier
[QClass]  Classical computation will be performed
[QClass]  Fitting:  (99, 5)
[QClass]  Feature dimension =  5
[QClass]  SVM initiating algorithm
[QClass]  Training...
[QClass]  Prediction:  (43, 5)
[QClass]  Prediction finished.
[QClass]  Prediction finished.