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Quantum Kernel Classifier

Compare a classical linear classifier against a quantum-kernel classifier on classically-inseparable 2D data (concentric circles, interleaved spirals). Watch classical data get mapped onto multi-qubit quantum states — an exponentially larger Hilbert space — and see the quantum kernel's decision boundary curve correctly where a straight line cannot.

Quantum Computing3DAdvanced60 FPS

Some 2D datasets — concentric rings, interleaved spirals — have no straight line that separates their two classes. A quantum kernel sidesteps that limit by encoding each point onto several qubits' worth of rotations, landing it in an exponentially larger Hilbert space where overlaps between encoded states reveal structure a 2D line can't see. This simulation trains a real logistic-regression line and a real kernel-perceptron classifier built on a simulated multi-qubit quantum feature map, side by side on the same data, so the gap between "classically inseparable" and "quantum-kernel separable" is visible rather than asserted.

⚙ Under the hood

Compare a classical linear classifier against a quantum-kernel classifier on classically-inseparable data (concentric circles, interleaved spirals): watch points encode onto several qubits' quantum states, an exponentially larger Hilbert space, and see the quantum decision boundary curve correctly where the straight line fails, with live test-accuracy readouts for both.

Quantum Machine LearningQuantum KernelClassificationHilbert SpaceBloch SphereThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install