Can I use this skill to run experiments on real quantum hardware?

Yes, the Qiskit skill provides guidance for authenticating and executing circuits on IBM Quantum hardware as well as providers like IonQ and Amazon Braket.

Is this suitable for beginners in quantum computing?

Yes, it includes a 'Quick Start' guide and a 'Workflow Decision Guide' to help developers navigate quantum development from installation to execution.

Does it support the latest Qiskit V2 primitives?

Absolutely, the skill includes comprehensive implementation patterns for the V2 Sampler and Estimator interfaces for robust job execution.

Can I visualize quantum circuits within my development environment?

Yes, it provides patterns for generating circuit diagrams, Bloch spheres, and result histograms using Matplotlib and other visualization libraries.

Qiskit Quantum Computing

Name: Qiskit Quantum Computing
Author: Activer007

byActiver007

Data Science & ML

Builds, optimizes, and executes quantum circuits and algorithms on real hardware and high-performance simulators.

About

Qiskit is a comprehensive open-source framework for quantum computing development, enabling users to design complex quantum circuits, optimize them for specific hardware backends, and execute jobs on real IBM Quantum processors or local simulators. It bridges the gap between high-level algorithm development and low-level pulse control, making it indispensable for research in quantum chemistry, machine learning, and optimization tasks within the Claude Code environment.

Key Features

High-performance transpilation and hardware-specific circuit optimization
0 GitHub stars
Advanced circuit construction with standard and custom quantum gates
Comprehensive visualization tools for circuits, state vectors, and histograms
Native integration with IBM Quantum Runtime and third-party hardware providers
Support for variational algorithms including VQE, QAOA, and Grover

Use Cases

Simulating molecular ground states for quantum chemistry and materials science
Solving complex optimization problems using variational quantum algorithms
Developing and training quantum machine learning models and neural networks

About

Key Features

High-performance transpilation and hardware-specific circuit optimization
0 GitHub stars
Advanced circuit construction with standard and custom quantum gates
Comprehensive visualization tools for circuits, state vectors, and histograms
Native integration with IBM Quantum Runtime and third-party hardware providers
Support for variational algorithms including VQE, QAOA, and Grover

Use Cases

Simulating molecular ground states for quantum chemistry and materials science
Solving complex optimization problems using variational quantum algorithms
Developing and training quantum machine learning models and neural networks