abstract = "With the emergence of more and more powerful quantum
computers, synthesis of quantum circuits that realize a
given quantum functionality on those devices has become
an important research topic. As quantum algorithms
often contain a substantial Boolean component, many
synthesis approaches focus on reversible circuits.
While some of these methods can be applied on rather
large functions, they often yield circuits that are far
from being optimal. Aiming at better solutions,
evolutionary algorithms can be used as possible
alternatives to above methods. However, while previous
work in this area clearly demonstrated the potential of
this direction, it often focuses on a single
optimization objective and employs cost functions that
are not very well suited for quantum-technological
implementations of the resulting circuits.
In this paper, we propose a framework for
multi-objective synthesis of quantum circuits based on
Genetic Programming that puts a focus on
quantum-specific aspects and can be tuned towards
several relevant/related cost metrics. A preliminary
evaluation indicates that the proposed approach is
competitive to previous ones. In some cases, the
generated circuits even improve over existing results
on all optimization objectives simultaneously, even
though the latter were found by specifically targeting
a single objective.",