[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ECH09GFX45D59GDMDGGZH7":3,"skill-same:01M6ECH09GFX45D59GDMDGGZH7":121},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":20,"frontMatter":21,"refName":22,"commitSha":23,"skillDirectory":24,"forksCount":25,"repositoryHtmlUrl":26,"repositoryHomepageUrl":27,"ownerHtmlUrl":28,"ownerBlogUrl":6,"ownerDescription":29,"updatedAt":30,"install":31,"resources":44},"01M6ECH09GFX45D59GDMDGGZH7","qiskit",null,"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\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Fqiskit\u002FSKILL.md",[],[5],"2026-11-10 10:12:30",false,"# Qiskit\n\n## Overview\n\nQiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.\n\n**Key Features:**\n- 83x faster transpilation than competitors\n- 29% fewer two-qubit gates in optimized circuits\n- Backend-agnostic execution (local simulators or cloud hardware)\n- Comprehensive algorithm libraries for optimization, chemistry, and ML\n\n## Quick Start\n\n### Installation\n\n```bash\nuv pip install qiskit\nuv pip install \"qiskit[visualization]\" matplotlib\n```\n\n### First Circuit\n\n```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.primitives import StatevectorSampler\n\n# Create Bell state (entangled qubits)\nqc = QuantumCircuit(2)\nqc.h(0)           # Hadamard on qubit 0\nqc.cx(0, 1)       # CNOT from qubit 0 to 1\nqc.measure_all()  # Measure both qubits\n\n# Run locally\nsampler = StatevectorSampler()\nresult = sampler.run([qc], shots=1024).result()\ncounts = result[0].data.meas.get_counts()\nprint(counts)  # {'00': ~512, '11': ~512}\n```\n\n### Visualization\n\n```python\nfrom qiskit.visualization import plot_histogram\n\nqc.draw('mpl')           # Circuit diagram\nplot_histogram(counts)   # Results histogram\n```\n\n## Core Capabilities\n\n### 1. Setup and Installation\nFor detailed installation, authentication, and IBM Quantum account setup:\n- **See `references\u002Fsetup.md`**\n\nTopics covered:\n- Installation with uv\n- Python environment setup\n- IBM Quantum account and API token configuration\n- Local vs. cloud execution\n\n### 2. Building Quantum Circuits\nFor constructing quantum circuits with gates, measurements, and composition:\n- **See `references\u002Fcircuits.md`**\n\nTopics covered:\n- Creating circuits with QuantumCircuit\n- Single-qubit gates (H, X, Y, Z, rotations, phase gates)\n- Multi-qubit gates (CNOT, SWAP, Toffoli)\n- Measurements and barriers\n- Circuit composition and properties\n- Parameterized circuits for variational algorithms\n\n### 3. Primitives (Sampler and Estimator)\nFor executing quantum circuits and computing results:\n- **See `references\u002Fprimitives.md`**\n\nTopics covered:\n- **Sampler**: Get bitstring measurements and probability distributions\n- **Estimator**: Compute expectation values of observables\n- V2 interface (StatevectorSampler, StatevectorEstimator)\n- IBM Quantum Runtime primitives for hardware\n- Sessions and Batch modes\n- Parameter binding\n\n### 4. Transpilation and Optimization\nFor optimizing circuits and preparing for hardware execution:\n- **See `references\u002Ftranspilation.md`**\n\nTopics covered:\n- Why transpilation is necessary\n- Optimization levels (0-3)\n- Six transpilation stages (init, layout, routing, translation, optimization, scheduling)\n- Advanced features (virtual permutation elision, gate cancellation)\n- Common parameters (initial_layout, approximation_degree, seed)\n- Best practices for efficient circuits\n\n### 5. Visualization\nFor displaying circuits, results, and quantum states:\n- **See `references\u002Fvisualization.md`**\n\nTopics covered:\n- Circuit drawings (text, matplotlib, LaTeX)\n- Result histograms\n- Quantum state visualization (Bloch sphere, state city, QSphere)\n- Backend topology and error maps\n- Customization and styling\n- Saving publication-quality figures\n\n### 6. Hardware Backends\nFor running on simulators and real quantum computers:\n- **See `references\u002Fbackends.md`**\n\nTopics covered:\n- IBM Quantum backends and authentication\n- Backend properties and status\n- Running on real hardware with Runtime primitives\n- Job management and queuing\n- Session mode (iterative algorithms)\n- Batch mode (parallel jobs)\n- Local simulators (StatevectorSampler, Aer)\n- Third-party providers (IonQ, Amazon Braket)\n- Error mitigation strategies\n\n### 7. Qiskit Patterns Workflow\nFor implementing the four-step quantum computing workflow:\n- **See `references\u002Fpatterns.md`**\n\nTopics covered:\n- **Map**: Translate problems to quantum circuits\n- **Optimize**: Transpile for hardware\n- **Execute**: Run with primitives\n- **Post-process**: Extract and analyze results\n- Complete VQE example\n- Session vs. Batch execution\n- Common workflow patterns\n\n### 8. Quantum Algorithms and Applications\nFor implementing specific quantum algorithms:\n- **See `references\u002Falgorithms.md`**\n\nTopics covered:\n- **Optimization**: VQE, QAOA, Grover's algorithm\n- **Chemistry**: Molecular ground states, excited states, Hamiltonians\n- **Machine Learning**: Quantum kernels, VQC, QNN\n- **Algorithm libraries**: Qiskit Nature, Qiskit ML, Qiskit Optimization\n- Physics simulations and benchmarking\n\n## Workflow Decision Guide\n\n**If you need to:**\n\n- Install Qiskit or set up IBM Quantum account → `references\u002Fsetup.md`\n- Build a new quantum circuit → `references\u002Fcircuits.md`\n- Understand gates and circuit operations → `references\u002Fcircuits.md`\n- Run circuits and get measurements → `references\u002Fprimitives.md`\n- Compute expectation values → `references\u002Fprimitives.md`\n- Optimize circuits for hardware → `references\u002Ftranspilation.md`\n- Visualize circuits or results → `references\u002Fvisualization.md`\n- Execute on IBM Quantum hardware → `references\u002Fbackends.md`\n- Connect to third-party providers → `references\u002Fbackends.md`\n- Implement end-to-end quantum workflow → `references\u002Fpatterns.md`\n- Build specific algorithm (VQE, QAOA, etc.) → `references\u002Falgorithms.md`\n- Solve chemistry or optimization problems → `references\u002Falgorithms.md`\n\n## Best Practices\n\n### Development Workflow\n\n1. **Start with simulators**: Test locally before using hardware\n   ```python\n   from qiskit.primitives import StatevectorSampler\n   sampler = StatevectorSampler()\n   ```\n\n2. **Always transpile**: Optimize circuits before execution\n   ```python\n   from qiskit import transpile\n   qc_optimized = transpile(qc, backend=backend, optimization_level=3)\n   ```\n\n3. **Use appropriate primitives**:\n   - Sampler for bitstrings (optimization algorithms)\n   - Estimator for expectation values (chemistry, physics)\n\n4. **Choose execution mode**:\n   - Session: Iterative algorithms (VQE, QAOA)\n   - Batch: Independent parallel jobs\n   - Single job: One-off experiments\n\n### Performance Optimization\n\n- Use optimization_level=3 for production\n- Minimize two-qubit gates (major error source)\n- Test with noisy simulators before hardware\n- Save and reuse transpiled circuits\n- Monitor convergence in variational algorithms\n\n### Hardware Execution\n\n- Check backend status before submitting\n- Use least_busy() for testing\n- Save job IDs for later retrieval\n- Apply error mitigation (resilience_level)\n- Start with fewer shots, increase for final runs\n\n## Common Patterns\n\n### Pattern 1: Simple Circuit Execution\n\n```python\nfrom qiskit import QuantumCircuit, transpile\nfrom qiskit.primitives import StatevectorSampler\n\nqc = QuantumCircuit(2)\nqc.h(0)\nqc.cx(0, 1)\nqc.measure_all()\n\nsampler = StatevectorSampler()\nresult = sampler.run([qc], shots=1024).result()\ncounts = result[0].data.meas.get_counts()\n```\n\n### Pattern 2: Hardware Execution with Transpilation\n\n```python\nfrom qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\nfrom qiskit import transpile\n\nservice = QiskitRuntimeService()\nbackend = service.backend(\"ibm_brisbane\")\n\nqc_optimized = transpile(qc, backend=backend, optimization_level=3)\n\nsampler = Sampler(backend)\njob = sampler.run([qc_optimized], shots=1024)\nresult = job.result()\n```\n\n### Pattern 3: Variational Algorithm (VQE)\n\n```python\nfrom qiskit_ibm_runtime import Session, EstimatorV2 as Estimator\nfrom scipy.optimize import minimize\n\nwith Session(backend=backend) as session:\n    estimator = Estimator(session=session)\n\n    def cost_function(params):\n        bound_qc = ansatz.assign_parameters(params)\n        qc_isa = transpile(bound_qc, backend=backend)\n        result = estimator.run([(qc_isa, hamiltonian)]).result()\n        return result[0].data.evs\n\n    result = minimize(cost_function, initial_params, method='COBYLA')\n```\n\n## Additional Resources\n\n- **Official Docs**: https:\u002F\u002Fquantum.ibm.com\u002Fdocs\n- **Qiskit Textbook**: https:\u002F\u002Fqiskit.org\u002Flearn\n- **API Reference**: https:\u002F\u002Fdocs.quantum.ibm.com\u002Fapi\u002Fqiskit\n- **Patterns Guide**: https:\u002F\u002Fquantum.cloud.ibm.com\u002Fdocs\u002Fen\u002Fguides\u002Fintro-to-patterns\n",{},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Fqiskit",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":7,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill qiskit",{"tool":37,"command":38},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill qiskit",{"tool":40,"command":41},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill qiskit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Fqiskit\u002FSKILL.md",true,[45,51,55,59,65,71,75,81,85,89,93,97,101,105,109,113,117],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"agents\u002Fanalyzer.md","REFERENCE","参考文档",10376,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fagents\u002Fanalyzer.md",{"path":52,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":53,"sourceHtmlUrl":54},"agents\u002Fcomparator.md",7287,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fagents\u002Fcomparator.md",{"path":56,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":57,"sourceHtmlUrl":58},"agents\u002Fgrader.md",9049,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fagents\u002Fgrader.md",{"path":60,"resourceType":61,"resourceTypeDesc":62,"sizeBytes":63,"sourceHtmlUrl":64},"assets\u002Feval_review.html","ASSET","资产文件",7058,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fassets\u002Feval_review.html",{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"eval-viewer\u002Fgenerate_review.py","SCRIPT","脚本",16365,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Feval-viewer\u002Fgenerate_review.py",{"path":72,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":73,"sourceHtmlUrl":74},"eval-viewer\u002Fviewer.html",44998,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Feval-viewer\u002Fviewer.html",{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"LICENSE.txt","LICENSE","许可证",11345,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002FLICENSE.txt",{"path":82,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fschemas.md",12061,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Freferences\u002Fschemas.md",{"path":86,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002F__init__.py",0,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002F__init__.py",{"path":90,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":91,"sourceHtmlUrl":92},"scripts\u002Faggregate_benchmark.py",14386,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Faggregate_benchmark.py",{"path":94,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":95,"sourceHtmlUrl":96},"scripts\u002Fgenerate_report.py",12847,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Fgenerate_report.py",{"path":98,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":99,"sourceHtmlUrl":100},"scripts\u002Fimprove_description.py",11116,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Fimprove_description.py",{"path":102,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":103,"sourceHtmlUrl":104},"scripts\u002Fpackage_skill.py",4234,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Fpackage_skill.py",{"path":106,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":107,"sourceHtmlUrl":108},"scripts\u002Fquick_validate.py",3972,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Fquick_validate.py",{"path":110,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":111,"sourceHtmlUrl":112},"scripts\u002Frun_eval.py",11464,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Frun_eval.py",{"path":114,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":115,"sourceHtmlUrl":116},"scripts\u002Frun_loop.py",13605,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Frun_loop.py",{"path":118,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":119,"sourceHtmlUrl":120},"scripts\u002Futils.py",1661,"https:\u002F\u002Fgithub.com\u002Fa1627418541\u002F-\u002Fblob\u002Fmain\u002F.rovodev\u002Fskills\u002Fskill-creator\u002Fscripts\u002Futils.py",[122,136,146,155,161,168],{"skillNo":123,"name":124,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":132,"publishedAt":18},"01M6FKJN13GMG4KY57J3QD8GY5","model-hierarchy-skill","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzscole\u002Fmodel-hierarchy-skill\u002Fmodel-hierarchy-skill\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":130,"taxonomyTypeDesc":131},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[133,134,135],"model","model hierarchy","model hierarchy skill",{"skillNo":137,"name":138,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":18},"01M6FKJJJ6GCAMR4PPPRD12M5X","zoom-mcp","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-mcp\u002FSKILL.md",[141],{"categoryNo":128,"categoryName":129,"taxonomyType":130,"taxonomyTypeDesc":131},[143,144,145],"MCP","zoom","zoom mcp",{"skillNo":147,"name":148,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":18},"01M6FKJE3KYT3KPEZMEEHYKA6K","zoom-apps-sdk","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[152,153,154,144],"sdk","zoom apps sdk","apps",{"skillNo":156,"name":157,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":18},"01M6FKJ9EDH9Q5VWDF8HAJRSP0","windows","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwindows\u002FSKILL.md",[],[157],{"skillNo":162,"name":163,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":167,"publishedAt":18},"01M6FKJ666BEEFC3AE9GJ72Q93","whiteboard","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwhiteboard\u002FSKILL.md",[166],{"categoryNo":128,"categoryName":129,"taxonomyType":130,"taxonomyTypeDesc":131},[163],{"skillNo":169,"name":170,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":18},"01M6FKJ21WFCDGTJQ2Q91T9DVW","websockets","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwebsockets\u002FSKILL.md",[],[170]]