Machine Learning Kernel Method from a Quantum Generative Model
This work addresses the problem of poor performance in quantum machine learning for practitioners, offering a competitive quantum classifier with intuitive hyper-parameters, though it appears incremental as it builds on existing sampling-based approaches.
The authors tackled the challenge of using Noisy Intermediate Scale Quantum (NISQ) devices for machine learning by proposing a quantum sampling-based classifier that performs at least equally well as top classical methods, leveraging randomized feature maps and random quantum circuits.
Recently the use of Noisy Intermediate Scale Quantum (NISQ) devices for machine learning tasks has been proposed. The propositions often perform poorly due to various restrictions. However, the quantum devices should perform well in sampling tasks. Thus, we recall theory of sampling-based approach to machine learning and propose a quantum sampling based classifier. Namely, we use randomized feature map approach. We propose a method of quantum sampling based on random quantum circuits with parametrized rotations distribution. We obtain simple to use method with intuitive hyper-parameters that performs at least equally well as top out-of-the-box classical methods. In short we obtain a competitive quantum classifier with crucial component being quantum sampling -- a promising task for quantum supremacy.