pennylane
Category: physical-sciences · License: Apache-2.0 license · Version: 1.2
Tags: quantum machine learning, quantum computing, variational algorithms, physics
When to use it
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.Example requests
Ask your agent in plain language, for example:- “How do I get gradients of a quantum circuit’s expectation value w.r.t. its parameters and train it with Adam?”
- “I have a 4-qubit angle-embedding classifier in qml.qnode with a torch.nn.Linear head, for iris_features.csv (4 features, 3 classes). Training with the torch interface gives ‘RuntimeError: element 0 of tensors does not require grad’ and the loss stays flat. Can you fix it so it trains end to end and then run it on a hardware-style device?”
- “I want to find the ground-state energy of an H2 molecule at several bond lengths with a variational quantum approach, and I’d like the same code to run on different vendors’ backends by changing one line. Can you set that up?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole physical-sciences category with /plugin install physical-sciences@research-agent-skills.