Train a variational quantum classifier on tabular data with parameter-shift gradients.
npx skills add https://github.com/unitarylab/quantum-skills --skill vqc
Please help me install this Agent Skill. Skill: vqc Source: https://github.com/unitarylab/quantum-skills/tree/main/algorithms/quantum-machine-learning/vqc Command: npx skills add https://github.com/unitarylab/quantum-skills --skill vqc
Classifies tabular data by leveraging a Variational Quantum Classifier (VQC) trained with data re-uploading and the Parameter Shift Rule to produce interpretable class logits from a small quantum circuit.
Run the included script to train the VQC on Iris data and report the final test accuracy.
High-intent search queries and answers about installing and using this skill.
Data re-uploading encodes 4 features onto 4 qubits per layer, using trainable Ry rotations and a CNOT ladder to preserve input information and increase circuit depth for classification tasks.
The parameter shift rule provides exact gradient estimation for the quantum circuit, enabling gradient-based optimization with Adam to update trainable rotations during the classification training workflow.
This variational quantum classifier targets Iris-like datasets or small benchmarks with 4 features and 3 classes, supporting multi-layer architectures for demonstrations of training, evaluation, and circuit export.
Quantum classification training requires a Python scientific stack including Torch, NumPy, and scikit-learn for data handling and optimization, plus a quantum circuit backend to execute gates.
A 4-qubit variational quantum classifier is limited to small benchmarks like the Iris dataset, as 4 qubits restrict scalability for large tabular datasets while focusing on circuit expressiveness and exact gradient estimation.
echo-iceGenerate executable Python modeling code from validated method plans and cleaned data.
tonone-aiAutomate end-to-end ML pipelines from data ingestion to serving endpoints.
unitarylabTrain a quantum circuit Born machine on Bars-and-Stripes distributions with KL-divergence and parameter-shift gradients.
hxk622Create, simulate, and train quantum circuits with PennyLane.
