pennylane-hybrid-executor

# PennyLane Hybrid Executor ## Purpose Provides expert guidance on hybrid quantum-classical workflows using PennyLane, enabling seamless integration of quantum circuits with classical machine learning frameworks. ## Capabilities - Quantum node (QNode) definition and execution - Automatic differentiation for quantum circuits - Device-agnostic circuit execution - Integration with ML frameworks (PyTorch, TensorFlow, JAX) - Variational algorithm optimization - Parameter shift rule gradients - Shot-based and analytic differentiation - Multi-device workflow orchestration ## Usage Guidelines 1. **QNode Definition**: Create differentiable quantum functions with device specification 2. **Gradient Computation**: Select appropriate differentiation method for the use case 3. **Framework Integration**: Seamlessly combine with PyTorch, TensorFlow, or JAX models 4. **Optimization**: Use classical optimizers to train variational circuits 5. **Device Switching**: Test on simulators before deploying to hardware ## Tools/Libraries - PennyLane - PennyLane-Lightning - PennyLane-Qiskit - PennyLane-Cirq - PennyLane-SF (Strawberry Fields)