Classifying Moons Dataset

This tutorial demonstrates binary classification with VQCClassifier.

Setup

import numpy as np
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from psipose.estimators import VQCClassifier

Generate Data

X, y = make_moons(n_samples=200, noise=0.2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

Train Classifier

clf = VQCClassifier(n_qubits=2, n_iter=100, random_state=42)
clf.fit(X_train, y_train)

Evaluate

y_pred = clf.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2%}")
print(f"Loss history: {clf.loss_history_[:5]}...")