Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment

• URL: http://arxiv.org/abs/2110.12624v1
• Date: Mon, 25 Oct 2021 03:37:34 GMT
• Title: Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment
• Authors: Samantha Koretsky, Pranav Gokhale, Jonathan M. Baker, Joshua Viszlai, Honghao Zheng, Niroj Gurung, Ryan Burg, Esa Aleksi Paaso, Amin Khodaei, Rozhin Eskandarpour, Frederic T. Chong
• Abstract summary: We formulate and apply a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units.
• Score: 2.8060379263058794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. We also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. Potential quantum advantage would require problem instances at this scale, with several hundred units.

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