Agent skill · research science · aiskillstore
qiskit
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
Why this skill is useful
Provides domain-specific knowledge for building and optimizing quantum circuits on IBM Quantum hardware that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, qiskit installed locally. Requires ibm quantum account access. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Qiskit Overview 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 other providers. Key Features: 83x faster transpilation than competitors 29% fewer two-qubit gates in optimized circuits Backend-agnostic execution (local simulators or cloud hardware) Comprehensive algorithm libraries for optimization, chemistry, and ML Quick Start Installation First Circuit Visualization Core Capabilities 1. Setup and Installation For detailed installation, authentication, and IBM Quantum account setup: See references/setup.md Topics covered: Installation with uv Python environment setup IBM Quantum account and API token configuration Local vs. cloud execution 2. Building Quantum Circuits For constructing quantum circuits with gates, measurements, and composition: See references/circuits.md Topics covered: Creating circuits with QuantumCircuit Single-qubit gates (H, X, Y, Z, rotations, phase gates) Multi-qubit gates (CNOT, SWAP, Toffoli) Measurements and barriers Circuit composition and properties Parameterized circuits for variational algorithms 3. Primitives (Sampler and Estimator) For executing quantum circuits and computing results: See references/primitives.md Topics covered: Sampler: Get bitstring measurements and probability distributions Estimator: Compute expectation values of observables V2 interface (StatevectorSampler, StatevectorEstimator) IBM Quantum Runtime primitives for hardware Sessions and Batch modes Parameter binding 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/qiskit--7bd267