A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

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

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

Memristive devices are a class of physical systems with history-dependent dynamics
characterized by signature hysteresis loops in their input–output relations. In the past few …

Reservoir computing is a relatively recent computational paradigm that originates from a
recurrent neural network and is known for its wide range of implementations using different …

Quantum computing crucially relies on the ability to efficiently characterize the quantum
states output by quantum hardware. Conventional methods which probe these states …

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

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

The quest for realizing novel fundamental physical effects and practical applications in
ambient conditions has led to tremendous interest in microcavity exciton polaritons working …

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 …