Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction.

Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals that are efficiently learned with a single linear output layer. We address the need for quantum reservoir tuning with a novel and generally applicable approach to quantum circuit parameterization, in which tunable noise models are programmed to the quantum reservoir circuit to be fully controlled for effective optimization.

Relativistic electron drift in overdense plasma produced by a superintense femtosecond laser pulse.

The general peculiarities of electron motion in the skin layer at the irradiation of overdense plasma by a superintense linearly polarized laser pulse of femtosecond duration are considered. The quiver electron energy is assumed to be a relativistic quantity. Relativistic electron drift along the propagation of laser radiation produced by a magnetic part of a laser field remains after the end of the laser pulse, unlike the relativistic drift of a free electron in underdense plasma.