[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ERDB10J9DMZZCAN0Y7ADST":3,"skill-same:01M6ERDB10J9DMZZCAN0Y7ADST":105},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":55,"updatedAt":56,"install":57,"resources":70},"01M6ERDB10J9DMZZCAN0Y7ADST","pennylane",null,"Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch\u002FJAX\u002FTensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fdavila7\u002Fpennylane\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"ibm","quantum algorithms","differentiation","amazon braket","platform","molecular","across","algorithms","networks","google","Optimization","library","tasks","training","pytorch","neural networks","workflows","machine learning","tensorflow","2026-11-10 10:12:30",false,"---\nname: pennylane\ndescription: Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch\u002FJAX\u002FTensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.\n---\n\n# PennyLane\n\n## Overview\n\nPennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv pip install pennylane\n```\n\nFor quantum hardware access, install device plugins:\n\n```bash\n# IBM Quantum\nuv pip install pennylane-qiskit\n\n# Amazon Braket\nuv pip install amazon-braket-pennylane-plugin\n\n# Google Cirq\nuv pip install pennylane-cirq\n\n# Rigetti Forest\nuv pip install pennylane-rigetti\n\n# IonQ\nuv pip install pennylane-ionq\n```\n\n## Quick Start\n\nBuild a quantum circuit and optimize its parameters:\n\n```python\nimport pennylane as qml\nfrom pennylane import numpy as np\n\n# Create device\ndev = qml.device('default.qubit', wires=2)\n\n# Define quantum circuit\n@qml.qnode(dev)\ndef circuit(params):\n    qml.RX(params[0], wires=0)\n    qml.RY(params[1], wires=1)\n    qml.CNOT(wires=[0, 1])\n    return qml.expval(qml.PauliZ(0))\n\n# Optimize parameters\nopt = qml.GradientDescentOptimizer(stepsize=0.1)\nparams = np.array([0.1, 0.2], requires_grad=True)\n\nfor i in range(100):\n    params = opt.step(circuit, params)\n```\n\n## Core Capabilities\n\n### 1. Quantum Circuit Construction\n\nBuild circuits with gates, measurements, and state preparation. See `references\u002Fquantum_circuits.md` for:\n- Single and multi-qubit gates\n- Controlled operations and conditional logic\n- Mid-circuit measurements and adaptive circuits\n- Various measurement types (expectation, probability, samples)\n- Circuit inspection and debugging\n\n### 2. Quantum Machine Learning\n\nCreate hybrid quantum-classical models. See `references\u002Fquantum_ml.md` for:\n- Integration with PyTorch, JAX, TensorFlow\n- Quantum neural networks and variational classifiers\n- Data encoding strategies (angle, amplitude, basis, IQP)\n- Training hybrid models with backpropagation\n- Transfer learning with quantum circuits\n\n### 3. Quantum Chemistry\n\nSimulate molecules and compute ground state energies. See `references\u002Fquantum_chemistry.md` for:\n- Molecular Hamiltonian generation\n- Variational Quantum Eigensolver (VQE)\n- UCCSD ansatz for chemistry\n- Geometry optimization and dissociation curves\n- Molecular property calculations\n\n### 4. Device Management\n\nExecute on simulators or quantum hardware. See `references\u002Fdevices_backends.md` for:\n- Built-in simulators (default.qubit, lightning.qubit, default.mixed)\n- Hardware plugins (IBM, Amazon Braket, Google, Rigetti, IonQ)\n- Device selection and configuration\n- Performance optimization and caching\n- GPU acceleration and JIT compilation\n\n### 5. Optimization\n\nTrain quantum circuits with various optimizers. See `references\u002Foptimization.md` for:\n- Built-in optimizers (Adam, gradient descent, momentum, RMSProp)\n- Gradient computation methods (backprop, parameter-shift, adjoint)\n- Variational algorithms (VQE, QAOA)\n- Training strategies (learning rate schedules, mini-batches)\n- Handling barren plateaus and local minima\n\n### 6. Advanced Features\n\nLeverage templates, transforms, and compilation. See `references\u002Fadvanced_features.md` for:\n- Circuit templates and layers\n- Transforms and circuit optimization\n- Pulse-level programming\n- Catalyst JIT compilation\n- Noise models and error mitigation\n- Resource estimation\n\n## Common Workflows\n\n### Train a Variational Classifier\n\n```python\n# 1. Define ansatz\n@qml.qnode(dev)\ndef classifier(x, weights):\n    # Encode data\n    qml.AngleEmbedding(x, wires=range(4))\n\n    # Variational layers\n    qml.StronglyEntanglingLayers(weights, wires=range(4))\n\n    return qml.expval(qml.PauliZ(0))\n\n# 2. Train\nopt = qml.AdamOptimizer(stepsize=0.01)\nweights = np.random.random((3, 4, 3))  # 3 layers, 4 wires\n\nfor epoch in range(100):\n    for x, y in zip(X_train, y_train):\n        weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)\n```\n\n### Run VQE for Molecular Ground State\n\n```python\nfrom pennylane import qchem\n\n# 1. Build Hamiltonian\nsymbols = ['H', 'H']\ncoords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])\nH, n_qubits = qchem.molecular_hamiltonian(symbols, coords)\n\n# 2. Define ansatz\n@qml.qnode(dev)\ndef vqe_circuit(params):\n    qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))\n    qml.UCCSD(params, wires=range(n_qubits))\n    return qml.expval(H)\n\n# 3. Optimize\nopt = qml.AdamOptimizer(stepsize=0.1)\nparams = np.zeros(10, requires_grad=True)\n\nfor i in range(100):\n    params, energy = opt.step_and_cost(vqe_circuit, params)\n    print(f\"Step {i}: Energy = {energy:.6f} Ha\")\n```\n\n### Switch Between Devices\n\n```python\n# Same circuit, different backends\ncircuit_def = lambda dev: qml.qnode(dev)(circuit_function)\n\n# Test on simulator\ndev_sim = qml.device('default.qubit', wires=4)\nresult_sim = circuit_def(dev_sim)(params)\n\n# Run on quantum hardware\ndev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')\nresult_hw = circuit_def(dev_hw)(params)\n```\n\n## Detailed Documentation\n\nFor comprehensive coverage of specific topics, consult the reference files:\n\n- **Getting started**: `references\u002Fgetting_started.md` - Installation, basic concepts, first steps\n- **Quantum circuits**: `references\u002Fquantum_circuits.md` - Gates, measurements, circuit patterns\n- **Quantum ML**: `references\u002Fquantum_ml.md` - Hybrid models, framework integration, QNNs\n- **Quantum chemistry**: `references\u002Fquantum_chemistry.md` - VQE, molecular Hamiltonians, chemistry workflows\n- **Devices**: `references\u002Fdevices_backends.md` - Simulators, hardware plugins, device configuration\n- **Optimization**: `references\u002Foptimization.md` - Optimizers, gradients, variational algorithms\n- **Advanced**: `references\u002Fadvanced_features.md` - Templates, transforms, JIT compilation, noise\n\n## Best Practices\n\n1. **Start with simulators** - Test on `default.qubit` before deploying to hardware\n2. **Use parameter-shift for hardware** - Backpropagation only works on simulators\n3. **Choose appropriate encodings** - Match data encoding to problem structure\n4. **Initialize carefully** - Use small random values to avoid barren plateaus\n5. **Monitor gradients** - Check for vanishing gradients in deep circuits\n6. **Cache devices** - Reuse device objects to reduce initialization overhead\n7. **Profile circuits** - Use `qml.specs()` to analyze circuit complexity\n8. **Test locally** - Validate on simulators before submitting to hardware\n9. **Use templates** - Leverage built-in templates for common circuit patterns\n10. **Compile when possible** - Use Catalyst JIT for performance-critical code\n\n## Resources\n\n- Official documentation: https:\u002F\u002Fdocs.pennylane.ai\n- Codebook (tutorials): https:\u002F\u002Fpennylane.ai\u002Fcodebook\n- QML demonstrations: https:\u002F\u002Fpennylane.ai\u002Fqml\u002Fdemonstrations\n- Community forum: https:\u002F\u002Fdiscuss.pennylane.ai\n- GitHub: https:\u002F\u002Fgithub.com\u002FPennyLaneAI\u002Fpennylane\n","# PennyLane\n\n## Overview\n\nPennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv pip install pennylane\n```\n\nFor quantum hardware access, install device plugins:\n\n```bash\n# IBM Quantum\nuv pip install pennylane-qiskit\n\n# Amazon Braket\nuv pip install amazon-braket-pennylane-plugin\n\n# Google Cirq\nuv pip install pennylane-cirq\n\n# Rigetti Forest\nuv pip install pennylane-rigetti\n\n# IonQ\nuv pip install pennylane-ionq\n```\n\n## Quick Start\n\nBuild a quantum circuit and optimize its parameters:\n\n```python\nimport pennylane as qml\nfrom pennylane import numpy as np\n\n# Create device\ndev = qml.device('default.qubit', wires=2)\n\n# Define quantum circuit\n@qml.qnode(dev)\ndef circuit(params):\n    qml.RX(params[0], wires=0)\n    qml.RY(params[1], wires=1)\n    qml.CNOT(wires=[0, 1])\n    return qml.expval(qml.PauliZ(0))\n\n# Optimize parameters\nopt = qml.GradientDescentOptimizer(stepsize=0.1)\nparams = np.array([0.1, 0.2], requires_grad=True)\n\nfor i in range(100):\n    params = opt.step(circuit, params)\n```\n\n## Core Capabilities\n\n### 1. Quantum Circuit Construction\n\nBuild circuits with gates, measurements, and state preparation. See `references\u002Fquantum_circuits.md` for:\n- Single and multi-qubit gates\n- Controlled operations and conditional logic\n- Mid-circuit measurements and adaptive circuits\n- Various measurement types (expectation, probability, samples)\n- Circuit inspection and debugging\n\n### 2. Quantum Machine Learning\n\nCreate hybrid quantum-classical models. See `references\u002Fquantum_ml.md` for:\n- Integration with PyTorch, JAX, TensorFlow\n- Quantum neural networks and variational classifiers\n- Data encoding strategies (angle, amplitude, basis, IQP)\n- Training hybrid models with backpropagation\n- Transfer learning with quantum circuits\n\n### 3. Quantum Chemistry\n\nSimulate molecules and compute ground state energies. See `references\u002Fquantum_chemistry.md` for:\n- Molecular Hamiltonian generation\n- Variational Quantum Eigensolver (VQE)\n- UCCSD ansatz for chemistry\n- Geometry optimization and dissociation curves\n- Molecular property calculations\n\n### 4. Device Management\n\nExecute on simulators or quantum hardware. See `references\u002Fdevices_backends.md` for:\n- Built-in simulators (default.qubit, lightning.qubit, default.mixed)\n- Hardware plugins (IBM, Amazon Braket, Google, Rigetti, IonQ)\n- Device selection and configuration\n- Performance optimization and caching\n- GPU acceleration and JIT compilation\n\n### 5. Optimization\n\nTrain quantum circuits with various optimizers. See `references\u002Foptimization.md` for:\n- Built-in optimizers (Adam, gradient descent, momentum, RMSProp)\n- Gradient computation methods (backprop, parameter-shift, adjoint)\n- Variational algorithms (VQE, QAOA)\n- Training strategies (learning rate schedules, mini-batches)\n- Handling barren plateaus and local minima\n\n### 6. Advanced Features\n\nLeverage templates, transforms, and compilation. See `references\u002Fadvanced_features.md` for:\n- Circuit templates and layers\n- Transforms and circuit optimization\n- Pulse-level programming\n- Catalyst JIT compilation\n- Noise models and error mitigation\n- Resource estimation\n\n## Common Workflows\n\n### Train a Variational Classifier\n\n```python\n# 1. Define ansatz\n@qml.qnode(dev)\ndef classifier(x, weights):\n    # Encode data\n    qml.AngleEmbedding(x, wires=range(4))\n\n    # Variational layers\n    qml.StronglyEntanglingLayers(weights, wires=range(4))\n\n    return qml.expval(qml.PauliZ(0))\n\n# 2. Train\nopt = qml.AdamOptimizer(stepsize=0.01)\nweights = np.random.random((3, 4, 3))  # 3 layers, 4 wires\n\nfor epoch in range(100):\n    for x, y in zip(X_train, y_train):\n        weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)\n```\n\n### Run VQE for Molecular Ground State\n\n```python\nfrom pennylane import qchem\n\n# 1. Build Hamiltonian\nsymbols = ['H', 'H']\ncoords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])\nH, n_qubits = qchem.molecular_hamiltonian(symbols, coords)\n\n# 2. Define ansatz\n@qml.qnode(dev)\ndef vqe_circuit(params):\n    qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))\n    qml.UCCSD(params, wires=range(n_qubits))\n    return qml.expval(H)\n\n# 3. Optimize\nopt = qml.AdamOptimizer(stepsize=0.1)\nparams = np.zeros(10, requires_grad=True)\n\nfor i in range(100):\n    params, energy = opt.step_and_cost(vqe_circuit, params)\n    print(f\"Step {i}: Energy = {energy:.6f} Ha\")\n```\n\n### Switch Between Devices\n\n```python\n# Same circuit, different backends\ncircuit_def = lambda dev: qml.qnode(dev)(circuit_function)\n\n# Test on simulator\ndev_sim = qml.device('default.qubit', wires=4)\nresult_sim = circuit_def(dev_sim)(params)\n\n# Run on quantum hardware\ndev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')\nresult_hw = circuit_def(dev_hw)(params)\n```\n\n## Detailed Documentation\n\nFor comprehensive coverage of specific topics, consult the reference files:\n\n- **Getting started**: `references\u002Fgetting_started.md` - Installation, basic concepts, first steps\n- **Quantum circuits**: `references\u002Fquantum_circuits.md` - Gates, measurements, circuit patterns\n- **Quantum ML**: `references\u002Fquantum_ml.md` - Hybrid models, framework integration, QNNs\n- **Quantum chemistry**: `references\u002Fquantum_chemistry.md` - VQE, molecular Hamiltonians, chemistry workflows\n- **Devices**: `references\u002Fdevices_backends.md` - Simulators, hardware plugins, device configuration\n- **Optimization**: `references\u002Foptimization.md` - Optimizers, gradients, variational algorithms\n- **Advanced**: `references\u002Fadvanced_features.md` - Templates, transforms, JIT compilation, noise\n\n## Best Practices\n\n1. **Start with simulators** - Test on `default.qubit` before deploying to hardware\n2. **Use parameter-shift for hardware** - Backpropagation only works on simulators\n3. **Choose appropriate encodings** - Match data encoding to problem structure\n4. **Initialize carefully** - Use small random values to avoid barren plateaus\n5. **Monitor gradients** - Check for vanishing gradients in deep circuits\n6. **Cache devices** - Reuse device objects to reduce initialization overhead\n7. **Profile 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