Does it provide visualization support?

Yes, it assists with generating circuit diagrams, Bloch sphere representations, and result histograms using Matplotlib and other visualization backends.

Can this skill help me run circuits on real quantum hardware?

Yes, the skill provides patterns for connecting to IBM Quantum, managing API tokens, and using Qiskit Runtime primitives to execute jobs on real hardware backends.

How does it handle circuit optimization?

It includes detailed strategies for transpilation, allowing you to optimize circuits for specific backend topologies, reduce gate counts, and apply error mitigation.

Can I use this for quantum machine learning or chemistry?

Yes, it covers specialized libraries like Qiskit Nature and Qiskit ML, providing patterns for molecular Hamiltonians, quantum kernels, and neural networks.

Does it support the latest Qiskit V2 primitives?

Absolutely. It includes comprehensive guidance and code examples for the V2 Sampler and Estimator interfaces, including Session and Batch execution modes.

Qiskit Quantum Computing

Name: Qiskit Quantum Computing
Author: K-Dense-AI

byK-Dense-AI

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2,188

Data Science & ML

Builds, optimizes, and executes quantum circuits and algorithms on simulators or real hardware using the Qiskit framework.

About

The Qiskit skill empowers Claude to assist with the entire quantum computing lifecycle, from initial circuit design and gate application to advanced hardware optimization. It provides specific implementation patterns for the Qiskit V2 primitives (Sampler and Estimator), circuit transpilation for hardware-specific constraints, and the execution of complex algorithms like VQE and QAOA. Whether you are performing quantum chemistry simulations, exploring quantum machine learning, or managing jobs on IBM Quantum hardware, this skill ensures best practices in circuit depth reduction and error mitigation are followed.

Key Features

Hardware-aware transpilation and multi-level circuit optimization
2,188 GitHub stars
Quantum circuit construction with multi-qubit gates and measurements
Implementation of variational algorithms like VQE, QAOA, and Grover's
Execution on IBM Quantum backends, local simulators, and third-party providers
Advanced visualization of circuits, histograms, and quantum states

Use Cases

Simulating molecular ground states for quantum chemistry research
Optimizing logistics or financial problems using variational algorithms
Developing and training quantum machine learning (QML) models

About

Key Features

Hardware-aware transpilation and multi-level circuit optimization
2,188 GitHub stars
Quantum circuit construction with multi-qubit gates and measurements
Implementation of variational algorithms like VQE, QAOA, and Grover's
Execution on IBM Quantum backends, local simulators, and third-party providers
Advanced visualization of circuits, histograms, and quantum states

Use Cases

Simulating molecular ground states for quantum chemistry research
Optimizing logistics or financial problems using variational algorithms
Developing and training quantum machine learning (QML) models