{"id":"75b757e4-b894-4d0f-83b0-825889ee60b9","shortId":"AZnV2Z","kind":"skill","title":"qiskit","tagline":"Qiskit 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","description":"# Qiskit\n\n## When to Use\n- You are building or optimizing quantum circuits with Qiskit for simulators or real hardware.\n- You need IBM Quantum-style tooling for transpilation, execution, visualization, or algorithm libraries.\n- You want guidance on moving from a simple circuit prototype to backend-aware execution.\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/setup.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/circuits.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/primitives.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/transpilation.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/visualization.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/backends.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/patterns.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/algorithms.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/setup.md`\n- Build a new quantum circuit → `references/circuits.md`\n- Understand gates and circuit operations → `references/circuits.md`\n- Run circuits and get measurements → `references/primitives.md`\n- Compute expectation values → `references/primitives.md`\n- Optimize circuits for hardware → `references/transpilation.md`\n- Visualize circuits or results → `references/visualization.md`\n- Execute on IBM Quantum hardware → `references/backends.md`\n- Connect to third-party providers → `references/backends.md`\n- Implement end-to-end quantum workflow → `references/patterns.md`\n- Build specific algorithm (VQE, QAOA, etc.) → `references/algorithms.md`\n- Solve chemistry or optimization problems → `references/algorithms.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://quantum.ibm.com/docs\n- **Qiskit Textbook**: https://qiskit.org/learn\n- **API Reference**: https://docs.quantum.ibm.com/api/qiskit\n- **Patterns Guide**: https://quantum.cloud.ibm.com/docs/en/guides/intro-to-patterns\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are 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