Agent skill · research science · k-dense-ai
qiskit
Build, simulate, transpile, and execute quantum circuits with Qiskit and IBM Quantum Runtime. Use for Qiskit 2.x circuits and operators, V2 Sampler or Estimator primitives, target-aware transpilation, local or noisy simulation, IBM QPU execution, Runtime sessions or batches, error mitigation, and Qiskit ecosystem packages.
Why this skill is useful
Provides domain-specific knowledge on Qiskit APIs and workflows for quantum circuit execution that the AI wouldn't reliably generate on its own.
What it needs
Requires qiskit, qiskit-aer, qiskit-ibm-runtime installed locally. Requires ibm account access. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Qiskit Use current Qiskit 2.x APIs to build circuits, prepare hardware-compatible instruction set architecture (ISA) circuits, and execute them through V2 primitives. This skill was verified on 2026-07-23 against the PyPI releases qiskit==2.5.0, qiskit-ibm-runtime==0.48.0, and qiskit-aer==0.17.2. Check references/sources.md before changing pins or documenting newly released behavior. Choose the Right Path Goal Recommended interface --- --- Exact local sampling qiskit.primitives.StatevectorSampler Exact local expectation values qiskit.primitives.StatevectorEstimator High-performance or noisy simulation Qiskit Aer IBM QPU sampling qiskitibmruntime.SamplerV2 IBM QPU expectation values and mitigation qiskitibmruntime.EstimatorV2 Backend without native primitives BackendSamplerV2 or BackendEstimatorV2 Open-system or master-equation dynamics Prefer QuTiP Differentiable quantum machine learning Prefer PennyLane unless Qiskit integration is required Installation Create an isolated environment and install only the components needed: Do not install qiskit-terra; it was superseded by the qiskit distribution. Qiskit Runtime, Aer, Nature, Machine Learning, Optimization, and Algorithms are separate distributions. For IBM account setup, CI-safe credential handling, optional packages, and environment repair, read references/setup.md. Core Workflow Follow this sequence for every hardware-oriented workload: 1. Map the problem to a circuit and, for Estimator, one or more observables. 2. Optimize the parameterized circuit once for the selected backend. 3. Apply the layout to every observable. 4. Execute ISA circuits through a V2 primitive using Primitive Unified Blocs (PUBs). 5. Analyze register-aware results, metadata, uncertainty, and resource usage. Do not bind and retranspile a parameterized circuit inside every optimizer iteration. Transpile the parameterized circuit once, then pass parameter arrays in PUBs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/qiskit