Abstract Machine learning has become a ubiquitous and effective technique for data
processing and classification. Furthermore, due to the superiority and progress of quantum …

Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

Data representation is crucial for the success of machine-learning models. In the context of
quantum machine learning with near-term quantum computers, equally important …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

Abstract Machine learning algorithms are becoming increasingly resource-intensive. In
contrast to classical computing, quantum computing holds the potential with exponential …

Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence
and temporal dependency data modeling and its effectiveness has been extensively …

Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale
quantum computers. Due to the structure of conventional parametrized quantum gates, the …

Neural networks are computing models that have been leading progress in Machine
Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale …

This paper presents a quantum convolutional neural network (QCNN) for the classification of
high energy physics events. The proposed model is tested using a simulated dataset from …