  Recent advances in specialized hardware for solving optimization problems
such quantum computers, quantum annealers, and CMOS annealers give rise to new
ways for solving real-word complex problems. However, given current and
near-term hardware limitations, the number of variables required to express a
large real-world problem easily exceeds the hardware capabilities, thus hybrid
methods are usually developed in order to utilize the hardware. In this work,
we advocate for the development of hybrid methods that are built on top of the
frameworks of existing state-of-art heuristics, thereby improving these
methods. We demonstrate this by building on the so called Louvain method, which
is one of the most popular algorithms for the Community detection problem and
develop and Ising-based Louvain method. The proposed method outperforms two
state-of-the-art community detection algorithms in clustering several small to
large-scale graphs. The results show promise in adapting the same optimization
approach to other unsupervised learning heuristics to improve their
performance.
