QUANTUM AI · NO-CODE QML PLAYGROUND · LIVE

Quantum-kernel classification, live

Move the controls and watch a quantum-kernel classifier's decision boundary update instantly — angle-encoded feature map, fidelity kernel k(a,b)=∏ cos²((aᵢ−bᵢ)/2). The boundary is drawn in your browser with the exact math the live function uses, and any point can be verified against the live ml.kernel_classify gateway in one click.

Controls

Dataset

Model (quantum-kernel head)

Feature-map scale γ

1.00 ×

γ scales the angle encoding — it changes the kernel bandwidth and how sharp the boundary is.

Training points (per class)

Tip: click anywhere on the board to drop a test point for an instant local prediction, then hit “Verify on live API” to check it against the real gateway.

Decision boundary

class +1 class −1 test point

—

train accuracy

—

local prediction

—

decision score

POST /api/v1/functions/ml.kernel_classify

Drop a test point, then click “Verify on live API” to check the local result against the real quantum-kernel function.

Quantum neural-net regression

A single-qubit data-reuploading QNN fits a 1-D function: ⟨Z⟩(x) is trained to approximate the target curve, drawn live in-browser, and verifiable against the live ml.qnn_regression with R².

test x 1.50

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local R²

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prediction

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live R²

POST /api/v1/functions/ml.qnn_regression