Adiabatic Quantum Support Vector Machines

Machine learning's ability to analyze and predict complex data enables many scientific discoveries and industrial advances. However, state-of-the-art machine learning requires extensive computational resources for training. In machine learning, the training phase is crucial because it enables the model to acquire knowledge from the provided data. The duration of the training process varies significantly, ranging from a few hours to several months. As a result, the computational cost and time constraints hinder researchers from using machine learning. To address this issue, we investigated the possibility of using quantum computers for machine-learning tasks. We compared Date's [1] quantum support vector machine (SVM) approach on a D-Wave Advantage to a classical implementation on AMD Ryzen and Intel Xeon processors. We evaluated both the time-to-solution and the machine-learning performance of the quantum and classical systems. The results of our study demonstrated that SVMs scaled O(N₂) on the quantum computer and O(N₂d) on the classical computer. Here, N represents the number of samples, and d represents the number of features. So, when trained with a data set with eight million features, we found that the quantum annealer outperformed the classical computer by 3.5-4.5x. The quantum approach maintained a comparable accuracy to the classical approach as well. In conclusion, quantum computers offer a computational advantage over classical computers for machine learning.