Quantum reinforcement learning is an emerging field at the intersection of quantum
computing and machine learning. While we intend to provide a broad overview of the …

Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to
their ability to improve performance. However, verifying the success of open QW is …

We develop a deep learning (DL) framework assisted by differentiable programming for
discovery of optimal quantum control protocols under hard constraints. To that end, we use …

The advent of quantum computers has justified the development of quantum machine
learning algorithms, based on the adaptation of the principles of machine learning to the …

We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a
simple, wheeled robot in simulated environments of increasing complexity. For this, we train …

Generative models realized with Machine Learning (ML) techniques are powerful tools to
infer complex and unknown data distributions from a finite number of training samples in …

In the dynamic realm of practical clinical scenarios, Continual Learning (CL) has gained
increasing interest in medical image analysis due to its potential to address major …

Quantum computing holds great potential for advancing the limitations of machine learning
algorithms to handle higher dimensions of data and reduce overall training parameters in …

Quantum computing is a new and exciting field with the potential to solve some of the world's
most challenging problems. Currently, with the rise of quantum computers, the main …

A substantial portion of global quantum computing research has been conducted using
quantum mechanics, which recently has been applied to quantum computers. However, the …