README.md

metadata

title: QML Classifier Explorer
emoji: 🔬
colorFrom: blue
colorTo: purple
sdk: static
pinned: false

QML Classifier Explorer

Static Hugging Face viewer for HW1 Problem 2 — comparing three quantum machine learning classification methods on two datasets.

What this experiment is about

Three QML approaches are evaluated on binary classification:

Method	Description
Explicit Quantum Model	Encoding circuit S(x) + trainable W(θ) + measurement
Implicit Quantum Kernel	Fixed encoding kernel k(xi, xj) passed to SVM
Data Reuploading	Interleaved encoding and trainable layers (Ref. [4])

Two datasets:

• Circle — concentric ring structure (as used in Ref. [4])
• Moons — sklearn.datasets.make_moons(noise=0.1, n_samples=200)

Viewer contents

1. Decision boundary grid — 3 methods × 2 datasets (6 plots)
2. Training curve — accuracy and loss vs epoch, with step slider
3. Comparison table — test accuracy, trainable parameters, training time

Generating runtime data

Run on gx10:

# training + export
ssh gx10 "cd ~/quantum_computing && GX10_DOCKER_NETWORK=gx10-mlflow ./scripts/gx10_run_py.sh HW1/problem2/train.py --run-name q2-l4-e50 --tracking-uri http://gx10-mlflow-server:5001"

The export populates runtime/viewer_data.json which the viewer picks up automatically. Without a runtime export the viewer shows the template placeholder.