We studied the quantum correlations of a three-body Unruh−DeWitt detector system using genuine tripartite entanglement (GTE) and geometric quantum discord (GQD). We considered two representative three-body initial entangled states, namely the GHZ state and the W state. We demonstrated that the quantum correlations of the tripartite system are completely destroyed at the limit of infinite acceleration. In particular, it is found that the GQD of the two initial states exhibits “sudden change” behavior with increasing acceleration. It is shown that the quantum correlations of the W state are more sensitive than those of the GHZ state under the effect of Unruh thermal noise. The GQD is a more robust quantum resource than the GTE, and we can achieve robustness in discord-type quantum correlations by selecting the smaller energy gap in the detector. These findings provide guidance for selecting appropriate quantum states and resources for quantum information processing tasks in a relativistic setting.
Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.
MXenes, a novel class of 2D transition metal carbides and nitrides, have recently emerged as a promising candidate in the quest for efficient catalysts for the hydrogen evolution reaction. To enhance the performance of 2D MXenes with modest or poor catalytic efficiency, a particularly prosperous strategy involves doping with transition and noble metal atoms. Taking the Nb4C3O2 monolayer as a model, we explore substitutional metallic impurities, which serve as single-atom catalysts embedded within the Nb4C3O2 surface. Our findings demonstrate the ability to finely tune the atomic H binding energy within a 0.6 eV range, showing the potential for precise control in catalytic applications. Across different transition and noble metals, the single atoms integrated into Nb4C3O2 effectively adjust the free energy of H adsorption at nearby O atoms, achieving values comparable to or superior to Pt catalysts. A comprehensive examination of the electronic properties around the impurities reveals a correlation between changes in local reactivity and charge transfer to neighboring O atoms, where H atoms bind.
In order to fulfill the urgent requirements of functional products, circuit integration of different functional devices are commonly utilized. Thus, issues including production cycle, cost, and circuit crosstalk will get serious. Neuromorphic computing aims to break through the bottle neck of von Neumann architectures. Electronic devices with multi-operation modes, especially neuromorphic devices with multi-mode cognitive activities, would provide interesting solutions. Here, pectin/chitosan hybrid electrolyte gated oxide neuromorphic transistor was fabricated. With extremely strong proton related interfacial electric-double-layer coupling, the device can operate at low voltage of below 1 V. The device can also operate at multi-operation mode, including bottom gate mode, coplanar gate and pseudo-diode mode. Interestingly, the artificial synapse can work at low voltage of only 1 mV, exhibiting extremely low energy consumption of ~7.8 fJ, good signal-to-noise ratio of ~229.6 and sensitivity of ~23.6 dB. Both inhibitory and excitatory synaptic responses were mimicked on the pseudo-diode, demonstrating spike rate dependent plasticity activities. Remarkably, a linear classifier is proposed on the oxide neuromorphic transistor under synaptic metaplasticity mechanism. These results suggest great potentials of the oxide neuromorphic devices with multi-mode cognitive activities in neuromorphic platform.
High-quality antiferromagnetic Mott insulator thin films of LaTiO3 (LTO) were epitaxially grown onto SrTiO3 (STO) (110) substrates using the pulsed laser deposition. The LTO/STO heterostructures are not only highly conducting and ferromagnetic, but also show Kondo effect, Shubnikov‒de Haas (SdH) oscillations with a nonzero Berry phase of
Dielectric engineering plays a crucial role in the process of device miniaturization. Herein we investigate the electrical properties of bilayer GaSe metal-oxide-semiconductor field-effect transistors (MOSFETs), considering hetero-gate-dielectric construction, dielectric materials and GaSe stacking pattern. The results show that device performance strongly depends on the dielectric constants and locations of insulators. When high-k dielectric is placed close to the drain, it behaves with a larger on-state current (Ion) of 5052 μA/μm when the channel is 5 nm. Additionally, when the channel is 5 nm and insulator is HfO2, the largest Ion is 5134 μA/μm for devices with AC stacking GaSe channel. In particular, when the gate length is 2 nm, it still meets the HP requirements of ITRS 2028 for the device with AA stacking when high-k dielectric is used. Hence, the work provides guidance to regulate the performance of the two-dimensional nanodevices by dielectric engineering.
Spin−rotation coupling (SRC) is a fundamental interaction that connects electronic spins with the rotational motion of a medium. We elucidate the Einstein−de Haas (EdH) effect and its inverse with SRC as the microscopic mechanism using the dynamic spin−lattice equations derived by elasticity theory and Lagrangian formalism. By applying the coupling equations to an iron disk in a magnetic field, we exhibit the transfer of angular momentum and energy between spins and lattice, with or without damping. The timescale of the angular momentum transfer from spins to the entire lattice is estimated by our theory to be on the order of 0.01 ns, for the disk with a radius of 100 nm. Moreover, we discover a linear relationship between the magnetic field strength and the rotation frequency, which is also enhanced by a higher ratio of Young’s modulus to Poisson’s coefficient. In the presence of damping, we notice that the spin−lattice relaxation time is nearly inversely proportional to the magnetic field. Our explorations will contribute to a better understanding of the EdH effect and provide valuable insights for magneto-mechanical manufacturing.
Orbital angular momentums (OAMs) greatly enhance the channel capacity in free-space optical communication. However, demodulation of superposed OAM to recognize them separately is always difficult, especially upon multiplexing more OAMs. In this work, we report a directly recognition of multiplexed fractional OAM modes, without separating them, at a resolution of 0.1 with high accuracy, using a multi-task deep learning (MTDL) model, which has not been reported before. Namely, two-mode, four-mode, and eight-mode superposed OAM beams, experimentally generated with a hologram carrying both phase and amplitude information, are well recognized by the suitable MTDL model. Two applications in information transmission are presented: the first is for 256-ary OAM shift keying via multiplexed fractional OAMs; the second is for OAM division multiplexed information transmission in an eightfold speed. The encouraging results will expand the capacity in future free-space optical communication.
We investigated 1-μm multimode fiber laser based on carbon nanotubes, where multiple typical pulse states were observed, including Q-switched, Q-switched mode-locked, and spatiotemporal mode-locked pulses. Particularly, stable spatiotemporal mode-locking was realized with a low threshold, where the pulse duration was 37 ps and the wavelength was centred at 1060.5 nm. Moreover, both the high signal to noise and long-term operation stability proved the reliability of the mode-locked laser. Furthermore, the evolution of the spatiotemporal mode-locked pulses in the cavity was also simulated and discussed. This work exhibits the flexible outputs of spatiotemporal phenomena in multimode lasers based on nanomaterials, providing more possibilities for the development of high-dimensional nonlinear dynamics.
We show that the manifold of quantum states is endowed with a rich and nontrivial geometric structure. We derive the Fubini−Study metric of the projective Hilbert space of a multi-qubit quantum system, endowing it with a Riemannian metric structure, and investigate its deep link with the entanglement of the states of this space. As a measure, we adopt the entanglement distance E preliminary proposed in Phys. Rev. A 101, 042129 (2020). Our analysis shows that entanglement has a geometric interpretation:
We study mathematical, physical and computational aspects of the stabilizer formalism arising in quantum information and quantum computation. The measurement process of Pauli observables with its algorithm is given. It is shown that to detect genuine entanglement we need a full set of stabilizer generators and the stabilizer witness is coarser than the GHZ (Greenberger–Horne–Zeilinger) witness. We discuss stabilizer codes and construct a stabilizer code from a given linear code. We also discuss quantum error correction, error recovery criteria and syndrome extraction. The symplectic structure of the stabilizer formalism is established and it is shown that any stabilizer code is unitarily equivalent to a trivial code. The structure of graph codes as stabilizer codes is identified by obtaining the respective stabilizer generators. The distance of embeddable stabilizer codes in lattices is obtained. We discuss the Knill−Gottesman theorem, tableau representation and frame representation. The runtime of simulating stabilizer gates is obtained by applying stabilizer matrices. Furthermore, an algorithm for updating global phases is given. Resolution of quantum channels into stabilizer channels is shown. We discuss capacity achieving codes to obtain the capacity of the quantum erasure channel. Finally, we discuss the shadow tomography problem and an algorithm for constructing classical shadow is given.
Quantum secure direct communication (QSDC) can transmit secret messages without keys, making it an important branch of quantum communication. We present a hybrid entanglement-based quantum secure direct communication (HE-QSDC) protocol with simple linear optical elements, combining the benefits of both continuous variables (CV) and discrete variables (DV) encoding. We analyze the security and find that the QSDC protocol has a positive security capacity when the bit error rate is less than 0.073. Compared with previous DV QSDC protocols, our protocol has higher communication efficiency due to performing nearly deterministic Bell-state measurement. On the other hand, compared with CV QSDC protocol, this protocol has higher fidelity with large
