A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for
quantum machine learning. Their appeal lies in the rich feature map induced by the …

One of the most natural connections between quantum and classical machine learning has
been established in the context of kernel methods. Kernel methods rely on kernels, which …

Quantum computing is an emerging technology with potentially far-reaching implications for
national prosperity and security. Understanding the timeframes over which economic …

Understanding the capabilities of classical simulation methods is key to identifying where
quantum computers are advantageous. Not only does this ensure that quantum computers …

We study the convergence properties of Variational Quantum Circuits (VQCs) to investigate
how they can differ from their classical counterparts. It is known that a VQC is a linear model …

The vast and complicated large-qubit state space forbids us to comprehensively capture the
dynamics of modern quantum computers via classical simulations or quantum tomography …

A common trait of many machine learning models is that it is often difficult to understand and
explain what caused the model to produce the given output. While the explainability of …

The double descent phenomenon challenges traditional statistical learning theory by
revealing scenarios where larger models do not necessarily lead to reduced performance …

The quest for successful variational quantum machine learning (QML) relies on the design of
suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classical …