# Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

@article{Koretsky2021AdaptingQA, title={Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment}, author={Samantha Koretsky and Pranav Gokhale and Jonathan M. Baker and Joshua Viszlai and Honghao Zheng and Niroj Gurung and Ryan Burg and Esa Aleksi Paaso and Amin Khodaei and Rozhin Eskandarpour and Frederic T. Chong}, journal={2021 IEEE International Conference on Quantum Computing and Engineering (QCE)}, year={2021}, pages={181-187} }

In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization…

## 2 Citations

### Quantum computing for software engineering: prospects

- Computer Science, PhysicsQP4SE@ESEC/SIGSOFT FSE
- 2022

Eight groups of quantum algorithms that may accelerate Software Engineering (SE) tasks across the different phases of SE and sketch potential opportunities and challenges are examined.

### Data-Driven Quantum Approximate Optimization Algorithm for Cyber-Physical Power Systems

- Computer ScienceArXiv
- 2022

A data-driven QAOA is presented, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density, and is comparable with the Goemans-Williamson algorithm without parameter optimization.

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