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 …

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 …

Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …

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 article presents a first end-to-end application of a quantum support vector machine
(QSVM) algorithm for a classification problem in the financial payment industry using the IBM …

In the past decade, remarkable progress has been achieved in deep learning related
systems and applications. In the post Moore's Law era, however, the limit of semiconductor …

Quantum machine learning (QML) can complement the growing trend of using learned
models for a myriad of classification tasks, from image recognition to natural speech …

Tremendous progress has been witnessed in artificial intelligence where neural network
backed deep learning systems have been used, with applications in almost every domain …

Visualizations have played a crucial role in helping quantum computing users explore
quantum states in various quantum computing applications. Among them, Bloch Sphere is …

The high energy physics (HEP) community has a long history of dealing with large-scale
datasets. To manage such voluminous data, classical machine learning and deep learning …