Build and train a Quantum Neural Network for supervised classification with angle-encoded PQCs.
npx skills add https://github.com/unitarylab/quantum-skills --skill qnn
Please help me install this Agent Skill. Skill: qnn Source: https://github.com/unitarylab/quantum-skills/tree/main/algorithms/quantum-machine-learning/qnn Command: npx skills add https://github.com/unitarylab/quantum-skills --skill qnn
Enables building and training a Quantum Neural Network (QNN) for supervised classification tasks using parameterized quantum circuits.
Run the provided script to train the QNN on a synthetic binary classification dataset.
High-intent search queries and answers about installing and using this skill.
To train a quantum neural network for supervised classification, use this Skill to build a parameterized quantum circuit with angle-encoded features and per-qubit rotations, then run the provided NumPy-based training loop on labeled data.
Angle encoding in parameterized quantum circuits is the process of mapping classical features to quantum states via rotations, which this Skill uses alongside variational layers with CNOT entanglement to learn complex decision boundaries.
This quantum neural network Skill is applicable to small-to-moderate datasets, as it uses a lightweight training scaffold designed to explore angle-encoded PQCs and variational layers rather than scaling to massive data.
Variational layers in a QNN apply per-qubit Rx, Ry, and Rz rotations combined with CNOT entanglement to learn complex decision boundaries, allowing the parameterized circuit to adapt during the supervised training process.
Yes, you need NumPy to run the quantum neural network training scaffold, because the workflow relies on a NumPy-based implementation compatible with UnitaryLab's QNNAlgorithm to execute the variational circuits.
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