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

Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

Quantum neuromorphic computing physically implements neural networks in brain-inspired
quantum hardware to speed up their computation. In this perspective article, we show that …

The biggest challenge that quantum computing and quantum machine learning are currently
facing is the presence of noise in quantum devices. As a result, big efforts have been put into …

Realizing the promise of quantum information processing remains a daunting task given the
omnipresence of noise and error. Adapting noise-resilient classical computing modalities to …

Closed quantum systems exhibit different dynamical regimes, like many-body localization or
thermalization, which determine the mechanisms of spread and processing of information …

Loss-induced transparency (LIT), featuring the revival of optical intensity by adding loss, has
been demonstrated in classical optics. However, a fundamental question has remained …

Quantum computing promises to speed up machine-learning algorithms. However, noisy
intermediate-scale quantum (NISQ) devices pose engineering challenges to realizing …

Time-series processing is a major challenge in machine learning with enormous progress in
the last years in tasks such as speech recognition and chaotic series prediction. A promising …