Quantum Multiple Kernel Learning
This work addresses the problem of enhancing the expressivity of quantum kernel machines for machine learning practitioners, offering an incremental improvement over existing single quantum kernel methods.
This paper proposes Quantum Multiple Kernel Learning (MKL), a method that combines multiple quantum kernels to enhance the expressivity of kernel machines. The method uses deterministic quantum computing with one qubit (DQC1) to estimate the combined kernel without explicitly computing individual quantum kernels, demonstrating superiority over single quantum kernel machines on synthetic and German credit datasets.
Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity of a machine learning model, referring to the ability of the model to approximate complex functions, has a significant influence on its performance in these tasks. One approach to enhancing the expressivity of kernel machines is to combine multiple individual kernels to arrive at a more expressive combined kernel. This approach is referred to as multiple kernel learning (MKL). In this work, we propose an MKL method we refer to as quantum MKL, which combines multiple quantum kernels. Our method leverages the power of deterministic quantum computing with one qubit (DQC1) to estimate the combined kernel for a set of classically intractable individual quantum kernels. The combined kernel estimation is achieved without explicitly computing each individual kernel, while still allowing for the tuning of individual kernels in order to achieve better expressivity. Our simulations on two binary classification problems---one performed on a synthetic dataset and the other on a German credit dataset---demonstrate the superiority of the quantum MKL method over single quantum kernel machines.