The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Quantum information processing relies on the precise control of non-classical states in the
presence of many uncontrolled environmental degrees of freedom. The interactions …

Neural networks have enabled applications in artificial intelligence through machine
learning, and neuromorphic computing. Software implementations of neural networks on …

In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

The state-of-the-art machine learning approaches are based on classical von Neumann
computing architectures and have been widely used in many industrial and academic …

Recurrent neural networks are the foundation of many sequence-to-sequence models in
machine learning, such as machine translation and speech synthesis. With applied quantum …

Discoveries in quantum materials, which are characterized by the strongly quantum-
mechanical nature of electrons and atoms, have revealed exotic properties that arise from …

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 …

Model bias is an inherent limitation of the current dominant approach to optimal quantum
control, which relies on a system simulation for optimization of control policies. To overcome …

Quantum reservoir computing and quantum extreme learning machines are two emerging
approaches that have demonstrated their potential both in classical and quantum machine …