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he development of elaborate neural networks makes deep learning an accurate and efficient approach to solve some hard physical problems. This article proposes a novel hybrid network that integrates Long Short-Term Memory (LSTM) and residual network (ResNet), enabling the simulation of fast-oscillating quantum dynamics with a high-fidelity. Taking atomic condensate trapped in a double-well potential as an example, the efficiency of ResNet learning for the population dynamics i [Detail] ...
Download coverTwo-dimensional (2D) materials with atomic thickness, non-volatile resistive switching feature and compatibility with the semiconducting technology are naturally a good media of memristors. 2D materials-based memristors with excellent performance, low-power consumption and high integration density can be integrated with other circuit components to implement the complicate logic computing, which will become a key driving force for the development of artificial intelligence.
We theoretically study the band structures and the valley Chern numbers of the AB–AB and AB–BA stacked twisted double bilayer graphene under heterostrain effect. In the absence of heterostrain, due to the constrains by the spatial symmetries, the central two flat bands of the AB–AB are topological trivial bands, while in the AB–BA they have a finite Chern number. The heterostrain breaks all the point group symmetries and the constrains are lifted, hence the topological properties of the two arrangements can be tuned by different strain magnitudes ϵ and directions ϕ. The heterostrain has dissimilar impacts on the Chern numbers of the AB–AB and AB–BA, owing to their different band gaps, and these gaps can be modified by a vertical electric field. Our results show that the topological transitions for both arrangements occur in the ϵ range of 0.1%–0.4%, which can be realized in the graphene-based sample.
Instead of the energy-intensive Haber-Bosch process, electrochemical nitrogen reduction reaction (NRR) is an exciting new carbon neutral technique for ammonia synthesis under ambient conditions. In this work, we investigated K-based electrocatalysts theoretically and demonstrated that K3Sb/graphene performs excellent activity and inhibits hydrogen evolution on alternating reaction pathway. The first hydrogenation step from N2* to NNH* was found to be the most energetic and limiting step (0.61 eV). Graphene substrate plays the critical role to promote electronic conductivity between K3Sb and dinitrogen.
The rapid development of big-data analytics (BDA), internet of things (IoT) and artificial intelligent Technology (AI) demand outstanding electronic devices and systems with faster processing speed, lower power consumption, and smarter computer architecture. Memristor, as a promising Non-Volatile Memory (NVM) device, can effectively mimic biological synapse, and has been widely studied in recent years. The appearance and development of two-dimensional materials (2D material) accelerate and boost the progress of memristor systems owing to a bunch of the particularity of 2D material compared to conventional transition metal oxides (TMOs), therefore, 2D material-based memristors are called as new-generation intelligent memristors. In this review, the memristive (resistive switching) phenomena and the development of new-generation memristors are demonstrated involving grapheme (GR), transition-metal dichalcogenides (TMDs) and hexagonal boron nitride (h-BN) based memristors. Moreover, the related progress of memristive mechanisms is remarked.
The dimensionality of structures allows materials to be classified into zero-, one-, two-, and threedimensional systems. Two-dimensional (2D) systems have attracted a great deal of attention and typically include surfaces, interfaces, and layered materials. Due to their varied properties, 2D systems hold promise for applications such as electronics, optoelectronics, magnetronics, and valleytronics. The design of 2D systems is an area of intensive research because of the rapid development of ab initiostructure-searching methods. In this paper, we highlight recent research progress on accelerating the design of 2D systems using the CALYPSO methodology. Challenges and perspectives for future developments in 2D structure prediction methods are also presented.
Micro/nano-porous polymeric material is considered a unique industrial material due to its extremely low thermal conductivity, low density, and high surface area. Therefore, it is necessary to establish an accurate thermal conductivity prediction model suiting their applicable conditions and provide a theoretical basis for expanding their applications. In this work, the development of the calculation model of equivalent thermal conductivity of micro/nano-porous polymeric materials in recent years is summarized. Firstly, it reviews the process of establishing the overall equivalent thermal conductivity calculation model for micro/nanoporous polymers. Then, the predicted calculation models of thermal conductivity are introduced separately according to the conductive and radiative thermal conductivity models. In addition, the thermal conduction part is divided into the gaseous thermal conductivity model, solid thermal conductivity model and gas–solid coupling model. Finally, it is concluded that, compared with other porous materials, there are few studies on heat transfer of micro/ nanoporous polymers, especially on the particular heat transfer mechanisms such as scale effects at the micro/nanoscale. In particular, the following aspects of porous polymers still need to be further studied: micro scaled thermal radiation, heat transfer characteristics of particular morphologies at the nanoscales, heat transfer mechanism and impact factors of micro/nanoporous polymers. Such studies would provide a more accurate prediction of thermal conductivity and a broader application in energy conversion and storage systems.
Heat dissipation is one of the most serious problems in modern integrated electronics with the continuously decreasing devices size. Large portion of the consumed power is inevitably dissipated in the form of waste heat which not only restricts the device energy-efficiency performance itself, but also leads to severe environment problems and energy crisis. Thermoelectric Seebeck effect is a green energy-recycling method, while thermoelectric Peltier effect can be employed for heat management by actively cooling overheated devices, where passive cooling by heat conduction is not sufficiently enough. However, the technological applications of thermoelectricity are limited so far by their very low conversion efficiencies and lack of deep understanding of thermoelectricity in microscopic levels. Probing and managing the thermoelectricity is therefore fundamentally important particularly in nanoscale. In this short review, we will first briefly introduce the microscopic techniques for studying nanoscale thermoelectricity, focusing mainly on scanning thermal microscopy (SThM). SThM is a powerful tool for mapping the lattice heat with nanometer spatial resolution and hence detecting the nanoscale thermal transport and dissipation processes. Then we will review recent experiments utilizing these techniques to investigate thermoelectricity in various nanomaterial systems including both (two-material) heterojunctions and (single-material) homojunctions with tailored Seebeck coefficients, and also spin Seebeck and Peltier effects in magnetic materials. Next, we will provide a perspective on the promising applications of our recently developed Scanning Noise Microscope (SNoiM) for directly probing the non-equilibrium transporting hot charges (instead of lattice heat) in thermoelectric devices. SNoiM together with SThM are expected to be able to provide more complete and comprehensive understanding to the microscopic mechanisms in thermoelectrics. Finally, we make a conclusion and outlook on the future development of microscopic studies in thermoelectrics.
External-cavity diode laser (ECDL) has important applications in many fundamental and applied researches. Here we report a method to fast and widely tune the frequency of a stabilized ECDL. The beat frequency between the ECDL and a frequency-locked reference laser is identified by the voltage-controlled oscillator contained in a phase detector, whose output voltage is subtracted from the flexibly controlled PC signal to generate an error signal for stabilizing the ECDL. The output frequency of the stabilized ECDL can be shifted at a short characteristic time of ~ 150 μs within a range of ~ 620 MHz. The wide and fast-frequency tuning achieved by our method is compared with other previous works. We demonstrated the performance of our method by the efficient sub-Doppler cooling of Cs atoms with the temperature as low as 6 μK.
Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences. In the present work, we propose a novel learning method based on a hybrid network integrating two different kinds of neural networks: Long Short-Term Memory (LSTM) and Deep Residual Network (ResNet), in order to overcome the difficulty met in numerically simulating strongly-oscillating dynamical evolutions of physical systems. By taking the dynamics of Bose–Einstein condensates in a double-well potential as an example, we show that our new method makes a highly efficient pre-learning and a high-fidelity prediction about the whole dynamics. This benefits from the advantage of the combination of the LSTM and the ResNet and is impossibly achieved with a single network in the case of direct learning. Our method can be applied for simulating complex cooperative dynamics in a system with fast multiplefrequency oscillations with the aid of auxiliary spectrum analysis.
In this article we report on the experimental investigation of light storage for several types of diffractionfree beams (Bessel and Airy beams) and quasi-diffraction-free beams by utilizing electromagnetically induced transparency (EIT) technique in a hot atomic gas cell. The experimental results show that the diffraction-free and quasi-diffraction-free beams have better storage performances when compared with ordinary images possessing similar spatial profiles. Meanwhile, the Bessel beams and the quasidiffraction-free images are able to maintain their spatial profiles with a long storage time while the sidelobes of the Airy beam are gradually depleted with the increment of the storage time. We quantitatively analyze the storage results and give physical explanations behind these phenomena. Furthermore, the self-healing of the retrieved diffraction-free beams is verified, signifying that their characteristics preserve well after storage.
We present an improved version of the superatom (SA) model to examine the slow-light dynamics of a few-photons signal field in cold Rydberg atoms with van der Waals (vdW) interactions. A main feature of this version is that it promises consistent estimations on total Rydberg excitations based on dynamic equations of SAs or atoms. We consider two specific cases in which the incident signal field contains more photons with a smaller detuning or less photons with a larger detuning so as to realize the single-photon-level light storage. It is found that vdW interactions play a significant role even for the slow-light dynamics of a single-photon signal field as distributed Rydberg excitations are inevitable in the picture of dark-state polariton. Moreover, the stored (retrieved) signal field exhibits a clearly asymmetric (more symmetric) profile because its leading and trailing edges undergo different (identical) traveling journeys, and higher storage/retrieval efficiencies with well preserved profiles apply only to weaker and well detuned signal fields. These findings are crucial to understand the nontrivial interplay of single-photon-level light storage and distributed Rydberg excitations.
A dynamics regime of Rydberg atoms, unselective ground-state blockade (UGSB), is proposed in the context of Rydberg antiblockade (RAB), where the evolution of two atoms is suppressed when they populate in an identical ground state. UGSB is used to implement a SWAP gate in one step without individual addressing of atoms. Aiming at circumventing common issues in RAB-based gates including atomic decay, Doppler dephasing, and fluctuations in the interatomic coupling strength, we modify the RAB condition to achieve a dynamical SWAP gate whose robustness is much greater than that of the nonadiabatic holonomic one in the conventional RAB regime. In addition, on the basis of the proposed SWAP gates, we further investigate the implementation of a three-atom Fredkin gate by combining Rydberg blockade and RAB. The present work may facilitate to implement the RAB-based gates of strongly coupled atoms in experiment.
Multiparty quantum communication is an important branch of quantum networks. It enables private information transmission with information-theoretic security among legitimate parties. We propose a sender-controlled measurement-device-independent multiparty quantum communication protocol. The sender Alice divides a private message into several parts and delivers them to different receivers for secret sharing with imperfect measurement devices and untrusted ancillary nodes. Furthermore, Alice acts as an active controller and checks the security of quantum channels and the reliability of each receiver before she encodes her private message for secret sharing, which makes the protocol convenient for multiparity quantum communication.
In this paper, we propose a scheme for implementing the nonadiabatic holonomic quantum computation (NHQC+) of two Rydberg atoms by using invariant-based reverse engineering (IBRE). The scheme is based on Förster resonance induced by strong dipole–dipole interaction between two Rydberg atoms, which provides a selective coupling mechanism to simply the dynamics of system. Moreover, for improving the fidelity of the scheme, the optimal control method is introduced to enhance the gate robustness against systematic errors. Numerical simulations show the scheme is robust against the random noise in control fields, the deviation of dipole–dipole interaction, the Förster defect, and the spontaneous emission of atoms. Therefore, the scheme may provide some useful perspectives for the realization of quantum computation with Rydberg atoms.
The entangled coherent states (ECSs) have been widely used to realize quantum information processing tasks. However, the ECSs may suffer from photon loss and decoherence due to the inherent noise in quantum channel, which may degrade the fidelity of ECSs. To overcome these obstacles, we present a measurement-based entanglement purification protocol (MBEPP) for ECSs to distill some highquality ECSs from a large number of low-quality copies. We first show the principle of this MBEPP without considering the photon loss. After that, we prove that this MBEPP is feasible to correct the error resulted from the photon loss. Additionally, this MBEPP only requires to operate the Bell state measurement without performing local two-qubit gates on the noisy pairs and the purified high-quality ECSs can be preserved for other applications. This MBEPP may have application potential in the implementation of long-distance quantum communication.
