What is the primary purpose of the Qiskit skill?

It enables Claude to help developers design, optimize, and execute quantum circuits using the industry-standard Qiskit framework, covering everything from basic gates to complex variational algorithms.

Does this skill support quantum machine learning?

Absolutely. It provides patterns for Qiskit Machine Learning, including quantum kernels, variational quantum classifiers (VQC), and quantum neural networks.

Can I run circuits on real quantum hardware with this skill?

Yes, the skill includes guidance for connecting to and executing jobs on real hardware providers like IBM Quantum, IonQ, and Amazon Braket using Qiskit Runtime.

How does this skill help with circuit optimization?

It offers best practices for transpilation, helping you reduce gate depth and optimize qubit layouts to maximize fidelity on NISQ-era quantum processors.

Qiskit Quantum Computing

Name: Qiskit Quantum Computing
Author: Microck

byMicrock

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Data Science & ML

Builds, optimizes, and executes quantum circuits and algorithms across various hardware backends and simulators.

About

This skill empowers Claude to assist with full-stack quantum computing development using the Qiskit framework. It provides expert guidance on constructing quantum circuits with gates and measurements, transpiling circuits for hardware optimization, and executing jobs on IBM Quantum or third-party providers. Whether you are implementing variational algorithms like VQE and QAOA, exploring quantum chemistry, or visualizing complex quantum states, this skill provides the patterns and best practices needed for efficient quantum research and development.

Key Features

Advanced quantum algorithm implementation for chemistry, optimization, and ML
Comprehensive visualization for circuits, results, and quantum states like Bloch spheres
Build and compose complex quantum circuits with single and multi-qubit gates
High-performance transpilation to optimize circuits for specific hardware constraints
Execution via V2 Primitives (Sampler and Estimator) for probability distributions and expectation values
81 GitHub stars

Use Cases

Automating the execution and analysis of quantum experiments on IBM Quantum or Amazon Braket
Optimizing quantum circuits to minimize two-qubit gate counts and reduce error rates on noisy hardware
Developing variational algorithms like VQE for molecular ground state energy estimation

About

Key Features

Advanced quantum algorithm implementation for chemistry, optimization, and ML
Comprehensive visualization for circuits, results, and quantum states like Bloch spheres
Build and compose complex quantum circuits with single and multi-qubit gates
High-performance transpilation to optimize circuits for specific hardware constraints
Execution via V2 Primitives (Sampler and Estimator) for probability distributions and expectation values
81 GitHub stars

Use Cases

Automating the execution and analysis of quantum experiments on IBM Quantum or Amazon Braket
Optimizing quantum circuits to minimize two-qubit gate counts and reduce error rates on noisy hardware
Developing variational algorithms like VQE for molecular ground state energy estimation