  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.
