Solve Max-Cut problems with QAOA circuits using COBYLA optimization.
npx skills add https://github.com/unitarylab/quantum-skills --skill qaoa
Please help me install this Agent Skill. Skill: qaoa Source: https://github.com/unitarylab/quantum-skills/tree/main/algorithms/quantum-machine-learning/qaoa Command: npx skills add https://github.com/unitarylab/quantum-skills --skill qaoa
This skill helps users understand, implement, and apply the Quantum Approximate Optimization Algorithm (QAOA) to Max-Cut problems, bridging theory and runnable code through a structured workflow.
Run the provided script to execute a QAOA Max-Cut example on a 6-node graph.
High-intent search queries and answers about installing and using this skill.
To solve Max-Cut using QAOA, this script constructs a cost Hamiltonian, maps it to a quantum circuit with configurable depth, and runs COBYLA optimization to find the graph partition. It provides an end-to-end workflow from circuit construction to result decoding.
A hybrid quantum-classical workflow for graph optimization builds parameterized quantum circuits to evaluate cost functions while a classical optimizer updates parameters. This approach applies QAOA to find approximate solutions for graph-based problems like Max-Cut.
Yes, you can configure the circuit depth parameter p and select backends for QAOA optimization. The implementation allows tuning the gate sequence construction and running the COBYLA optimizer across different simulation environments.
Yes, you need the UnitaryLab QAOAAlgorithm implementation and Python installed to run this skill. It provides the core circuit construction, COBYLA optimizer integration, and circuit export functions required for the workflow.
This QAOA implementation is designed for educational learning and prototyping on small graphs, such as a 6-node example. It is not intended for large-scale graph optimization tasks due to the computational limits of simulating deep quantum circuits.
The COBYLA optimizer works with QAOA by classically tuning the parameterized gate angles in the quantum circuit to minimize the cost Hamiltonian. This hybrid loop repeats until the algorithm converges on an approximate Max-Cut solution.
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