Build differentiable quantum circuits across multiple hardware backends.
npx skills add https://github.com/swaruplab/operon --skill pennylane-swaruplab
Please help me install this Agent Skill. Skill: pennylane Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/pennylane Command: npx skills add https://github.com/swaruplab/operon --skill pennylane-swaruplab
PennyLane provides a framework to build and train differentiable quantum circuits across multiple hardware backends, bridging quantum and classical ML workflows.
Install PennyLane, create a device, define a QNode, and run a simple quantum circuit.
High-intent search queries and answers about installing and using this skill.
You can run differentiable quantum circuits across multiple hardware backends by creating a device, defining a QNode, and executing the model, which enables seamless quantum and classical ML workflow integration.
Yes, hybrid quantum-classical models integrate natively with PyTorch, JAX, and TensorFlow, enabling automatic differentiation and backpropagation across both simulated and hardware quantum circuits.
The best way to build hybrid quantum-classical models for VQE or QAOA is using a device-agnostic framework that supports automatic differentiation and integrates directly with standard ML libraries.
Yes, this approach supports variational classifiers and quantum neural networks by providing automatic differentiation of quantum circuits, allowing you to train hybrid models for quantum ML research.
Yes, device-agnostic programming lets you execute the same quantum model on different hardware backends like IBM/Qiskit, Google Cirq, Rigetti, and IonQ without changing your core circuit definitions.
Yes, automatic differentiation is supported for quantum circuits running on actual hardware plugins, enabling backpropagation through quantum operations directly within your chosen ML framework.
SologaDevelop and train quantum machine learning models with automatic differentiation.
tondevrelProvides differentiable quantum circuits for Python ML frameworks like PyTorch, TensorFlow and JAX.
galeepTrain quantum circuits with automatic differentiation and hardware-agnostic quantum ML.
rubenslivDesign, simulate, and optimize quantum circuits with automatic differentiation.
