Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application p...Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.展开更多
Quantum search algorithm,which can search an unsorted database quadratically faster than any known classical algorithms,has become one of the most impressive showcases of quantum computation.It has been implemented us...Quantum search algorithm,which can search an unsorted database quadratically faster than any known classical algorithms,has become one of the most impressive showcases of quantum computation.It has been implemented using various quantum schemes.Here,we demonstrate both theoretically and experimentally that such a fast search algorithm can also be realized using classical electric circuits.The classical circuit networks to perform such a fast search have been designed.It has been shown that the evolution of electric signals in the circuit networks is analogies of quantum particles randomly walking on graphs described by quantum theory.The searching efficiencies in our designed classical circuits are the same to the quantum schemes.Because classical circuit networks possess good scalability and stability,the present scheme is expected to avoid some problems faced by the quantum schemes.Thus,our findings are advantageous for information processing in the era of big data.展开更多
基金supported by the National Key R and D Program of China(No.2022YFF0604801)the National Natural Science Foundation of China(Nos.62271056,62171186,62201037)+3 种基金the Technology Innovation Center of Infrared Remote Sensing Metrology Technology of State Administration for Market Regulation(No.AKYKF2423)the Beijing Natural Science Foundation of China-Haidian Original Innovation Joint Fund(No.L222042)the Open Research Fund of State Key Laboratory of Millimeter Waves(No.K202326)the 111 Project of China(No.B14010).
文摘Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.
基金supported by the National Key R&D Pro-gram of China under Grant no.2017YFA0303800the National Natural Science Foundation of China(91850205).
文摘Quantum search algorithm,which can search an unsorted database quadratically faster than any known classical algorithms,has become one of the most impressive showcases of quantum computation.It has been implemented using various quantum schemes.Here,we demonstrate both theoretically and experimentally that such a fast search algorithm can also be realized using classical electric circuits.The classical circuit networks to perform such a fast search have been designed.It has been shown that the evolution of electric signals in the circuit networks is analogies of quantum particles randomly walking on graphs described by quantum theory.The searching efficiencies in our designed classical circuits are the same to the quantum schemes.Because classical circuit networks possess good scalability and stability,the present scheme is expected to avoid some problems faced by the quantum schemes.Thus,our findings are advantageous for information processing in the era of big data.
