相关论文: Perfect coherent transfer in an on-chip reconfigur…

Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully connected. This places a heavy computational and memory…

Bottom-up assembled nanomaterials and nanostructures allow for the studies of rich and unprecedented quantum-related and mesoscopic transport phenomena. However, it can be difficult to quantify the correlations between the geometrical or…

Integrating nano-scale objects, such as single molecules or carbon nanotubes, into impedance transformers and performing radio-frequency measurements allows for high time-resolution transport measurements with improved signal-to-noise…

The aim of this paper is to swap the entanglement between two separate long distant locations. The well-known entangled coherent states as two-mode continuous-variable states are very interesting in quantum teleportation and entanglement…

We propose a mechanism to interface a transmission line resonator (TLR) with a nano-mechanical resonator (NAMR) by commonly coupling them to a charge qubit, a Cooper pair box with a controllable gate voltage. Integrated in this quantum…

Fluid antennas, including those based on liquid, mechanical, and pixel-based technologies, are poised to significantly enhance next-generation wireless systems by adaptively optimizing their radiation characteristics. Many theoretical…

A single-electron tunneling (SET) device with a nanoscale central island that can move with respect to the bulk source- and drain electrodes allows for a nanoelectromechanical (NEM) coupling between the electrical current through the device…

Preparing and observing quantum states of nanoscale particles is a challenging task with great relevance for quantum technologies and tests of fundamental physics. In contrast to atomic systems with discrete transitions, nanoparticles…

This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are…

The primary requirement for achieving spin-selective electron transfer in a nanojunction possessing a magnetic system with zero net magnetization is to break the symmetry between the up and down spin sub-Hamiltonians. Circumventing the…

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus…

Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing…

Spin chains have been proposed as wires to transport information between distributed registers in a quantum information processor. Unfortunately, the challenges in manufacturing linear chains with engineered couplings has hindered…

Perfect quantum state transfer is achievable in different settings, including linear qubit chains, bi-dimensional arrays, ladders, etc. The most studied case contemplates transferring arbitrary one-qubit pure states in systems with…

Perfect transfer of a quantum state through a one-dimensional chain is now well understood, allowing one not only to decide whether a fixed Hamiltonian achieves perfect transfer, but to design a suitable one. We are particularly interested…

We propose a class of qubit networks that admit perfect state transfer of any two-dimensional quantum state in a fixed period of time. We further show that such networks can distribute arbitrary entangled states between two distant parties,…

We study the coherent control of microwave photons propagating in a superconducting waveguide consisting of coupled transmission line resonators, each of which is connected to a tunable charge qubit. While these coupled line resonators form…

Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range dependencies, edge…

Rapid and reproducible assembly of aligned nanostructures on a wafer-scale is a crucial, yet one of the most challenging tasks in the incorporation of nanowires into integrated circuits. We present the synthesis of a periodic nanochannel…

Absorption of electromagnetic energy by a material is a phenomenon that underlies many applied problems, including molecular sensing, photocurrent generation and photodetection. Commonly, the incident energy is delivered to the system…