PennyLane — differentiable quantum machine learning
Train and differentiate quantum circuits with PennyLane: QNodes, parameter-shift/backprop/adjoint gradients, hybrid quantum-classical models with PyTorch/JAX, VQE/QAOA, quantum chemistry, simulators and hardware plugins
- What
- Train and differentiate quantum circuits with PennyLane: QNodes, parameter-shift/backprop/adjoint gradients, hybrid quantum-classical models with PyTorch/JAX, VQE/QAOA, quantum chemistry, simulators and hardware plugins
- Cost
- Free
- Needs
- Python >= 3.11 with uv — pennylane (uv pip install pennylane, v0.43+); optional device plugins (pennylane-qiskit, amazon-braket-pennylane-plugin, pennylane-cirq, pennylane-ionq) for hardware — hardware access is billed and needs provider credentials; PyTorch/JAX for hybrid models
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @alterlab-ieu's alterlab-pennylane skill, listed here with credit to its creator (part of the AlterLab Academic Skills suite): a complete agent reference for quantum machine learning with PennyLane. It covers quantum circuit construction (gates, measurements, state preparation, circuit inspection), quantum machine learning (hybrid quantum-classical models via `qml.qnn.TorchLayer` and JAX, quantum neural networks, variational classifiers, data encoding strategies, transfer learning), quantum chemistry (molecular Hamiltonians, VQE with UCCSD ansatz, geometry optimization), device management (default.qubit and lightning simulators, IBM/Amazon Braket/Cirq/IonQ hardware plugins), optimization (Adam, gradient descent, parameter-shift vs backprop vs adjoint gradients, barren plateaus), and advanced features (templates, transforms, Catalyst JIT, noise models, resource estimation) — with runnable workflows: train a variational classifier, run VQE for molecular ground state, switch between simulator and hardware. Documented version notes for 0.42-0.45 (TensorFlow support gone, shots belong on the QNode, unmaintained Rigetti plugin) and best practices (start on simulators, parameter-shift for hardware, small initializations against barren plateaus). Honest caveats: license listed MIT per the discovery manifest (the skill's own frontmatter reads Apache-2.0 — the manifest makes faith here); local simulation is free, real quantum hardware is billed and needs provider credentials; this is research-grade quantum computing — simulators get exponentially expensive with qubit count. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python >= 3.11 with uv — pennylane (uv pip install pennylane, v0.43+); optional device plugins (pennylane-qiskit, amazon-braket-pennylane-plugin, pennylane-cirq, pennylane-ionq) for hardware — hardware access is billed and needs provider credentials; PyTorch/JAX for hybrid models Install "PennyLane — differentiable quantum machine learning" for me. It gives my agent @alterlab-ieu's PennyLane reference: quantum circuit construction, differentiable QML (hybrid models with PyTorch/JAX, variational classifiers, data encoding), quantum chemistry (VQE/QAOA), device management (simulators and hardware plugins), optimization (parameter-shift, backprop, adjoint gradients), advanced features (templates, Catalyst JIT, noise models), runnable workflows, version notes for 0.42-0.45, and best practices. Part of the AlterLab Academic Skills suite. Listed MIT per the discovery manifest (the skill's frontmatter reads Apache-2.0 — the manifest makes faith). Repository: https://github.com/alterlab-ieu/alterlab-academic-skills/blob/main/skills/domain-specific/alterlab-pennylane/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code — provider credentials only via my own env/vault. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "alterlab-pennylane". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. uv pip install pennylane; test on default.qubit before any hardware; set shots on the QNode, not the device; save hardware credentials in the vault if using them). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.