How does this skill assist with circuit optimization?

It guides you through the transpilation process, helping you select the right optimization levels (0-3) to reduce two-qubit gate counts and improve fidelity on noisy hardware.

What is the difference between Sampler and Estimator primitives?

The Sampler is used to obtain bitstrings and probability distributions, while the Estimator is designed to compute the expectation values of observables, which is essential for algorithms like VQE.

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

Yes, it provides specific implementation patterns for IBM Quantum Runtime, including session management, backend selection, and hardware-aware transpilation.

Does it support quantum machine learning applications?

Absolutely. It covers Qiskit Machine Learning libraries for creating quantum kernels, variational classifiers (VQC), and quantum neural networks (QNN).

Qiskit Quantum Computing

Name: Qiskit Quantum Computing
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

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

About

The Qiskit skill empowers Claude to serve as an expert assistant for quantum information science, providing deep integration with the world's most popular open-source quantum SDK. It enables the design of complex quantum circuits, implementation of variational algorithms like VQE and QAOA, and advanced hardware-specific transpilation to minimize gate errors. Whether you are conducting quantum chemistry research, developing quantum machine learning models, or executing jobs on IBM Quantum hardware, this skill provides the patterns and best practices necessary to bridge abstract quantum theory with physical execution.

Key Features

Rich visualization for circuits, Bloch spheres, and result histograms
Hardware-specific transpilation and multi-level circuit optimization
Circuit design with comprehensive single and multi-qubit gate support
Implementation of VQE, QAOA, and Grover's quantum algorithms
Execution on IBM Quantum Runtime, IonQ, and Amazon Braket
3 GitHub stars

Use Cases

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

About

Key Features

Rich visualization for circuits, Bloch spheres, and result histograms
Hardware-specific transpilation and multi-level circuit optimization
Circuit design with comprehensive single and multi-qubit gate support
Implementation of VQE, QAOA, and Grover's quantum algorithms
Execution on IBM Quantum Runtime, IonQ, and Amazon Braket
3 GitHub stars

Use Cases

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