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 machine learning is often highlighted as one of the most promising practical
applications for which quantum computers could provide a computational advantage …

This review paper examines state-of-the-art algorithms and techniques in quantum machine
learning with potential applications in finance. We discuss QML techniques in supervised …

We introduce LOWESA as a classical algorithm for faithfully simulating quantum systems via
a classically constructed surrogate expectation landscape. After an initial overhead to build …

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 …

Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …

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 …

Quantum machine learning is arguably one of the most explored applications of near-term
quantum devices. Much focus has been put on notions of variational quantum machine …

Quantum machine learning (QML) has become a promising area for real world applications
of quantum computers, but near-term methods and their scalability are still important …

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 …