AI & Machine Learning
AlterLab PennyLane
Trains and differentiates quantum circuits with PennyLane, a hardware-agnostic quantum machine-learning framework with automatic differentiation and PyTorch/JAX integration. Use when training quantum circuits via gradients (parameter-shift, backprop, adjoint), building hybrid quantum-classical models or quantum neural networks, or running differentiable variational algorithms (VQE, QAOA). For IBM Quantum hardware and Qiskit Runtime prefer alterlab-qiskit; for Google Quantum AI or NISQ circuits prefer alterlab-cirq; for open-system Lindblad/master-equation dynamics prefer alterlab-qutip. Part of the AlterLab Academic Skills suite.
- Publisher / source
- AlterLab-IEU/AlterLab-Academic-Skills (found on the CSV list)
- Upstream record
- AlterLab-IEU/AlterLab-Academic-Skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
df130980d2ceb3a5e149b900c1834f4f9c89df606655045e6163eccc02433221