Quantum-Assisted Machine Learning by Means of Adiabatic Quantum Computing

Motivated by the potential performance gains offered by quantum computing (QC), a recent research focus — known as quantum-assisted machine learning (QAML) — aims to develop machine learning algorithms tailored for execution on quantum computers while processing data that originate from a classical (i.e. non-quantum) phenomenon. QAML has been studied in various quantum computing (QC) paradigms. This paper, in particular, offers a brief overview of QAML within the context of adiabatic quantum computing (AQC). The major challenge in this case is to reformulate the machine learning task as a quadratic unconstrained binary optimization (QUBO) problem, which lies at the core of existing AQC physical implementations. We provide examples of machine learning techniques tailored to AQC, along with applications in remote sensing.