Ising-based Consensus Clustering on Specialized Hardware
This work addresses consensus clustering, an important combinatorial optimization problem in data mining, by leveraging quantum-inspired hardware, though it appears incremental as it builds on existing Ising model formulations.
The authors tackled the consensus clustering problem by formulating it as Ising models and solving them on specialized hardware, specifically the Fujitsu Digital Annealer, resulting in performance that outperforms existing techniques.
The emergence of specialized optimization hardware such as CMOS annealers and adiabatic quantum computers carries the promise of solving hard combinatorial optimization problems more efficiently in hardware. Recent work has focused on formulating different combinatorial optimization problems as Ising models, the core mathematical abstraction used by a large number of these hardware platforms, and evaluating the performance of these models when solved on specialized hardware. An interesting area of application is data mining, where combinatorial optimization problems underlie many core tasks. In this work, we focus on consensus clustering (clustering aggregation), an important combinatorial problem that has received much attention over the last two decades. We present two Ising models for consensus clustering and evaluate them using the Fujitsu Digital Annealer, a quantum-inspired CMOS annealer. Our empirical evaluation shows that our approach outperforms existing techniques and is a promising direction for future research.