A general quantum minimum searching algorithm with high success rate and its implementation

Finding a minimum is a fundamental calculation in many quantum algorithms.However,challenges are faced in demonstrating it effectively in real quantum computers.In practice,the number of solutions is unknown,and there is no universal encoding method.Besides that,current quantum computers have limited resources.To alleviate these problems,this paper proposes a general quantum minimum searching algorithm.An adaptive estimation method is adopted to calculate the number of solutions,and a quantum encoding circuit for arbitrary databases is presented for the first time,which improves the universality of the algorithm and helps it achieve a nearly 100%success rate in a series of random databases.Moreover,gate complexity is reduced by our simplified Oracle,and the realizability of the algorithm is verified on a superconducting quantum computer.Our algorithm can serve as a subroutine for various quantum algorithms to promote their implementation in the Noisy IntermediateScale Quantum era.

Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure

The tight-binding(TB)method is an ideal candidate for determining electronic and transport properties for a large-scale system.It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters,leading to substantially lower computational costs than the ab-initio methods.Since the whole system is defined by the parameterization scheme,the choice of the TB parameters decides the reliability of the TB calculations.The typical empirical TB method uses the TB parameters directly from the existing parameter sets,which hardly reproduces the desired electronic structures quantitatively without specific optimizations.It is thus not suitable for quantitative studies like the transport property calculations.The ab-initio TB method derives the TB parameters from the ab-initio results through the transformation of basis functions,which achieves much higher numerical accuracy.However,it assumes prior knowledge of the basis and may encompass truncation error.Here,a machine learning method for TB Hamiltonian parameterization is proposed,within which a neural network(NN)is introduced with its neurons acting as the TB matrix elements.This method can construct the empirical TB model that reproduces the given ab-initio energy bands with predefined accuracy,which provides a fast and convenient way for TB model construction and gives insights into machine learning applications in physical problems.

A novel multiscale simulation framework for low-dimensional memristors

In recent years,the memristor has been widely considered an emerging device,but it has rarely been simulated.An obstacle is the change in the intrinsic atomic level when it works.Using the density functional theory(DFT),this atomic level change in structure cannot be demonstrated.Using molecular dynamics(MD),memristor electronic transport properties cannot be calculated.In this study,we propose a novel multiscale simulation framework merging MD,DFT,and the nonequilibrium Green’s function method,which can reveal not only a memristor’s basic working mechanism but also its transport character.To verify our framework’s availability in guiding innovative memristor design,a new type of memristor,a planar monolayer MoS_(2)-based memristor,is simulated for the first time.The popped S atoms’effect on its carrier transport is revealed,which clarifies the working mechanism of the planar monolayer MoS_(2)-based memory device.We hope that this framework can shed light on the analysis and design of low-dimensional memristors.

Graph representation-based machine learning framework for predicting electronic band structures of quantum-confined nanostructures

The application of machine learning(ML)to electronic structure theory enables electronic property prediction with ab initio accuracy.However,most previous ML models predict one or several properties of intrinsic materials.The prediction of electronic band structure,which embeds all the main electronic information,has yet to be deeply studied.This is a challenging task due to the highly variable inputs and outputs;the input materials may have different sizes and compositions,and the output band structures may have varying band numbers and k-point samplings.This task becomes even more difficult when quantum-confined nanostructures are considered,whose band structures are sensitive to the confinements applied.This paper presents an ML framework for predicting band structures of quantum-confined nanostructures from their geometries.Our framework introduces a graph convolutional network applicable to materials with varying compositions and geometries to extract their atoms’local environment information.A learnable real-space Hamiltonian construction process then enables the utilization of the information to predict the electronic structure at any arbitrary k-point;the theoretical foundations introduced in this process help to capture and incorporate minor changes in quantum confinements into band structures,and endow the framework with the ability of few-shot learning.Taking an example of graphene nanoribbons,typical quantum-confined nanostructures,we show how the framework is constructed and its excellent performance on band structure prediction with a tiny data set.Our framework may not only provide a rapid yet reliable method for electronic structure determination but also enlighten the applications of graph representation to ML in related fields.

Modulation of High-Latitude Tropical Cyclone Recurvature by Solar Radiation

In this study,idealized simulations are conducted to investigate potential influences of solar radiation on the tropical cyclone(TC) recurvature at higher latitudes.Results indicate that TC track is sensitive to the seasonal variation of radiative forcing at higher latitudes.In the absence of a background flow,TCs at higher latitudes tend to recurve(remain northwestward) in the cold(warm) season.This feature is an additional aspect of the so-called intrinsic recurvature property of TC movement at high latitude.Physically,the greater meridional gradient of temperature in the cold season due to solar radiative forcing would induce a larger thermal wind,which affects the upper-level anticyclonic circulation and associated outflow.The structure changes of TC,mainly at upper-levels,modulate the steering flow for TC,leading to a higher probability of TCs at higher latitudes to recurve in the cold season than in the warm season.