  Planning energy production is a challenging task due to its cost-sensitivity,
fast-moving energy markets, uncertainties in demand, and technical constraints
of power plants. Thus, more complex models of this so-called \emph{unit
commitment problem (UCP)} have to be solved more rapidly, a task that probably
can be solved more efficiently via quantum computing. In this article, we model
a UCP with minimum running and idle times as a quadratic unconstrained
optimization problem to solve it on quantum computing hardware. First
experiments confirm the advantages of our formulation in terms of qubit usage
and connectivity and most importantly solution quality.
