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

Quantum computing has been moving from a theoretical phase to practical one, presenting
daunting challenges in implementing physical qubits, which are subjected to noises from the …

Quantum reservoir computing is a promising approach for quantum neural networks,
capable of solving hard learning tasks on both classical and quantum input data. However …

Quantum reservoir computing is strongly emerging for sequential and time series data
prediction in quantum machine learning. We make advancements to the quantum noise …

Quantum extreme learning machines (QELMs) aim to efficiently post-process the outcome of
fixed—generally uncalibrated—quantum devices to solve tasks such as the estimation of the …

The practical implementation of many quantum algorithms known today is limited by the
coherence time of the executing quantum hardware and quantum sampling noise. Here we …

Accelerating computation with quantum resources is limited by the challenges of high-fidelity
control of quantum systems. Reservoir computing presents an attractive alternative, as …

Demonstrating a quantum computational advantage will require high-fidelity control and
readout of multiqubit systems. As system size increases, multiplexed qubit readout becomes …

We propose a scheme that can enhance the performance and reduce the computational
cost of quantum reservoir computing. Quantum reservoir computing is a computing …

Two hybrid quantum-classical reservoir computing models are presented to reproduce the
low-order statistical properties of a two-dimensional turbulent Rayleigh-Bénard convection …