Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …

Abstract Kernel methods in Quantum Machine Learning (QML) have recently gained
significant attention as a potential candidate for achieving a quantum advantage in data …

Kernel methods in Quantum Machine Learning (QML) have recently gained significant
attention as a potential candidate for achieving a quantum advantage in data analysis …

The widespread success of deep neural networks has revealed a surprise in classical
machine learning: very complex models often generalize well while simultaneously …

Quantum machine learning is the field that aims to integrate machine learning with quantum
computation. In recent years, the field has emerged as an active research area with the …

Generalisation in machine learning often relies on the ability to encode structures present in
data into an inductive bias of the model class. To understand the power of quantum machine …

Despite the mounting anticipation for the quantum revolution, the success of quantum
machine learning (QML) in the noisy intermediate-scale quantum (NISQ) era hinges on a …

Learning problems involve settings in which an algorithm has to make decisions based on
data, and possibly side information such as expert knowledge. This study has two main …

Quantum machine learning is a rapidly growing field at the intersection of quantum
computing and machine learning. In this work, we examine our quantum machine learning …

Quantum kernel methods exploit quantum computers to calculate quantum kernels (QKs) for
the use of kernel-based learning models. Despite a potential quantum advantage of the …