DephasingAssisted Macrospin Transport Transport phenomena are ubiquitous in physics, and it is generally understood that the environmental disorder and noise deteriorates the transfer of excitations. There are, however, cases in which transport can be enhanced by fluctuations. In the present work, we show, by means of micromagnetics simulations, that transport efficiency in a chain of classical macrospins can be greatly increased by an optimal level of dephasing noise. We also demonstrate the same effect in a simplified model, the dissipative Discrete Nonlinear Schrodinger equation, subject to phase noise. Our results point towards the realization of a large class of magnonics and spintronics devices, where disorder and noise can be used to enhance spindependent transport efficiency.

Quantum Learning with Noise and Decoherence: A Robust Quantum Neural Network Noise and decoherence are two major obstacles to  :8,of largescale quantum computing. Because :8,of the  :16,which says we cannot make an exact copy :8,of an arbitrary quantum  :29,redundancy will not work in a quantum context, and unwanted  the :40,environment can destroy coherence and thus the quantum nature :8,of  Because :8,of the :52,parallel and distributed nature :8,of classical  :62,they  have long  :67,used to deal with incomplete or damaged data. In this   :78,show that our model :8,of a  :86,network (QNN) is similarly robust to noise, and that, in addition, it  to :99,decoherence. Moreover, robustness to noise and decoherence is  :109,maintained but improved as the size :8,of the system is increased. Noise  :122,may even be :8,of advantage in training, as it helps correct  :134,We demonstrate the robustness using entanglement as a means  :144,storage in a qubit array. Our results provide evidence that  :155,approaches can obviate otherwise recalcitrant problems in