Quantum Data Re-Uploading Classifier

This simulation trains a real single-qubit quantum machine learning model — the "data re-uploading" classifier — on a 2D dataset, live in the browser. A classical feature (x, y) is repeatedly encoded onto a qubit's Bloch sphere and interleaved with trainable rotations across configurable layers; gradient descent uses the exact quantum parameter-shift rule to update those rotation angles every epoch, sharpening the decision boundary shown on the 2D panel while the 3D Bloch sphere animates the exact rotation trajectory a probed point takes through the circuit. Switch between a circular and an XOR-shaped target concept, add re-upload layers to increase the qubit's expressivity, and watch loss and accuracy converge as the single qubit learns to classify data it was never explicitly programmed to separate.