The meeting between Natural Language Processing (NLP) and Quantum Computing has
been very successful in recent years, leading to the development of several approaches of …

Jonas Landman: jonas. landman@ qcware. com we trained on these small-scale datasets
require fewer parameters compared to standard classical benchmarks. While this …

Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …

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 investigate whether large language models can perform the creative hypothesis
generation that human researchers regularly do. While the error rate is high, generative AI …

The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with
challenges in delivering timely and accurate information to clients, particularly concerning …

An emerging direction of quantum computing is to establish meaningful quantum
applications in various fields of artificial intelligence, including natural language processing …

Automatic machine learning-based (ML-based) medical report generation systems for retinal
images suffer from a relative lack of interpretability. Hence, such ML-based systems are still …

The barren plateau problem in quantum neural networks (QNNs) is a significant challenge
that hinders the practical success of QNNs. In this paper, we introduce residual quantum …

Evaluating the quality of automatically generated image descriptions is challenging,
requiring metrics that capture various aspects such as grammaticality, coverage …