  In recent years, the development of quantum annealers has enabled
experimental demonstrations and has increased research interest in applications
of quantum annealing, such as in quantum machine learning and in particular for
the popular quantum SVM. Several versions of the quantum SVM have been
proposed, and quantum annealing has been shown to be effective in them.
Extensions to multiclass problems have also been made, which consist of an
ensemble of multiple binary classifiers. This work proposes a novel quantum SVM
formulation for direct multiclass classification based on quantum annealing,
called Quantum Multiclass SVM (QMSVM). The multiclass classification problem is
formulated as a single Quadratic Unconstrained Binary Optimization (QUBO)
problem solved with quantum annealing. The main objective of this work is to
evaluate the feasibility, accuracy, and time performance of this approach.
Experiments have been performed on the D-Wave Advantage quantum annealer for a
classification problem on remote sensing data. The results indicate that,
despite the memory demands of the quantum annealer, QMSVM can achieve accuracy
that is comparable to standard SVM methods and, more importantly, it scales
much more efficiently with the number of training examples, resulting in nearly
constant time. This work shows an approach for bringing together classical and
quantum computation, solving practical problems in remote sensing with current
hardware.
