  The design of quantum circuits is often still done manually, for instance by
following certain patterns or rule of thumb. While this approach may work well
for some problems, it can be a tedious task and present quite the challenge in
other situations. Designing the architecture of a circuit for a simple
classification problem may be relatively straightforward task, however,
creating circuits for more complex problems or that are resilient to certain
known problems (e.g. barren plateaus, trainability, etc.) is a different issue.
Moreover, efficient state preparation or circuits with low depth are important
for virtually most algorithms. In attempts to automate the process of designing
circuits, different approaches have been suggested over the years, including
genetic algorithms and reinforcement learning. We propose our GA4QCO framework
that applies a genetic algorithm to automatically search for quantum circuits
that exhibit user-defined properties. With our framework, the user specifies
through a fitness function what type of circuit should be created, for instance
circuits that prepare a specific target state while keeping depth at a minimum
and maximizing fidelity. Our framework is designed in such a way that the user
can easily integrate a custom designed fitness function. In this paper, we
introduce our framework and run experiments to show the validity of the
approach.
