Related papers: Reservoir-assisted energy migration through multip…

We show how Cooper-pair-assisted transport, which describes the stimulated transport of electrons in the presence of Cooper-pairs, can be engineered and controlled with cold atoms, in regimes that are difficult to access for condensed…

The concept of energetic particle reservoirs, essentially based on the assumption of the presence of outer reflecting boundaries/magnetic mirrors or diffusion barriers (deterministic) rather than on the effect of particle diffusive…

We study the fundamental problem of distributed network formation among mobile agents of limited computational power that aim to achieve energy balance by wirelessly transmitting and receiving energy in a peer-to-peer manner. Specifically,…

Physical reservoir computing is a computational framework that offers an energy- and computation-efficient alternative to conventional training of neural networks. In reservoir computing, input signals are mapped into the high-dimensional…

Spintronics is the generic term that describes magnetic systems coupled to an electric generator, taking into account the spin attached to the charge carriers. For this topical review of {\it Spin Caloritronics}, we focus our attention on…

Spin transfer torque and spin pumping are central reciprocal phenomena in spintronics. These phenomena occur in hybrid systems of normal metals and magnets. Spin transfer is the conversion of spin currents in metals to a torque on the…

Reservoir computing (RC) represents a class of state-space models (SSMs) characterized by a fixed state transition mechanism (the reservoir) and a flexible readout layer that maps from the state space. It is a paradigm of computational…

The mechanisms of energy and spin transfer in quantum dot pairs coupled via the Coulomb interaction are studied. Exciton transfer can be resonant or phonon-assisted. In both cases, the transfer rates strongly depend on the resonance…

The energy cascade in turbulence, first statistically described by Richardson (1922) and Kolmogorov (1941), lacked connection to the underlying fluid dynamics. Recent numerical studies of Goto et al. (2017) and Yoneda et al. (2022) revealed…

We study energy transport in XXZ spin chains driven to nonequilibrium configurations by thermal reservoirs of different temperatures at the boundaries. We discuss the transition between diffusive and subdiffusive transport regimes in…

Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to…

The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum…

In the present work we propose that a one-dimensional quantum heterostructure composed of magnetic and non-magnetic atomic sites can be utilized as a spin filter for a wide range of applied bias voltage. A simple tight-binding framework is…

We propose a simple model quantum network consisting of diamond-shaped plaquettes with deterministic distribution of magnetic and non-magnetic atoms in presence of a uniform external magnetic flux in each plaquette and predict that such a…

Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology and fundamental tests. One of the key challenges for implementing a quantum network is to…

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER…

The rate of energy transfer in donor-acceptor systems can be manipulated via the common interaction with the confined electromagnetic modes of a micro-cavity. We analyze the competition between the near-field short range dipole-dipole…

Quantum reservoir computing is a machine-learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided…

We study the effect of system reservoir coupling on currents flowing through quantum junctions. We consider two simple double-quantum dot configurations coupled to two external fermionic reservoirs and study the net current flowing between…

This paper considers a two-hop network consisting of a source, two parallel half-duplex relay nodes, and two destinations. While the destinations have an adequate power supply, the source and relay nodes rely on harvested energy for data…