Adiabatic Quantum Computing for Binary Clustering
This work addresses binary clustering for machine learning applications, but it is incremental as it adapts an existing quantum paradigm to a specific problem.
The paper tackled binary clustering using adiabatic quantum computing, demonstrating feasibility through numerical simulations that show qubit systems evolving towards solutions.
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical simulations demonstrate the feasibility of our approach and illustrate how systems of qubits adiabatically evolve towards a solution.