Results 51 to 60 of about 43,906 (221)

Random Time‐Space Coding Metasurfaces for Spatial Control of the Temporal Statistics of Electromagnetic Fields

Advanced Science, EarlyView.
A probabilistic framework based on random time‐space coding metasurfaces enables control of the spatial distribution of electromagnetic fields temporal statistics. By tailoring the marginal and joint distributions of random codes, electromagnetic fields with desired mean and variance patterns are realized, enabling simultaneous transmission and jamming.
Jia Cheng Li, Jiang Han Bao, Che Liu, Tie Jun Cui   +3 more
wiley   +1 more source

Triple‐Mode Ferroelectric Thin‐Film Transistor for Hybrid Electrical–Optical Reservoir Computing

Advanced Science, EarlyView.
A triple‐mode ferroelectric thin‐film transistor is developed by integrating Si3N4/HZO/IGZO layers to realize three independent memory modes: electric long‐term, electric short‐term, and optical short‐term. This single‐device architecture functions as both a reservoir and readout layer, achieving 92.43% MNIST accuracy. It offers a fully hardware‐based,
Hyeonho Lee, Seungjun Lee, Tae‐Hyeon Kim, Yonghee Jeong, Gwangmin An, Minsu Ko, Junsu Yu, Heung Soo Kim, Yang Chai, Sungjun Kim   +9 more
wiley   +1 more source

Speedup of high-order unconstrained binary optimization using quantum $${{\mathbb{Z}}}_{2}$$ Z 2 lattice gauge theory

Communications Physics
An important and difficult problem in optimization is the high-order unconstrained binary optimization, which can represent many optimization problems more efficiently than quadratic unconstrained binary optimization, but how to quickly solve it has ...
Bi-Ying Wang, Xiaopeng Cui, Qingguo Zeng, Yemin Zhan, Man-Hong Yung, Yu Shi   +5 more
doaj   +1 more source

Qubit Reduction and Quantum Speedup for Wireless Channel Assignment Problem

IEEE Transactions on Quantum Engineering, 2023
In this article, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive search (GAS) is used to provide a quadratic speedup for solving
Yuki Sano, Masaya Norimoto, Naoki Ishikawa   +2 more
doaj   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

MM Algorithms for Geometric and Signomial Programming

, 2010
This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean
A Ruszczynski, DR Hunter, EL Peterson, H Zhou, H Zhou, H Zhou, H Zhou, Hua Zhou, J Nocedal, JG Ecker, JM Borwein, JM Steele, K Hoffman, K Lange, K Lange, Kenneth Lange, M Mar Hershenson del, M Mazumdar, PD Feigin, S Boyd, S Boyd, U Passy, Y Wang   +22 more
core   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Distributed Interior-point Method for Loosely Coupled Problems

, 2013
In this paper, we put forth distributed algorithms for solving loosely coupled unconstrained and constrained optimization problems. Such problems are usually solved using algorithms that are based on a combination of decomposition and first order methods.
Andersen, Martin S., Hansson, Anders, Pakazad, Sina Khoshfetrat   +2 more
core   +1 more source

Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction

Advanced Intelligent Systems, EarlyView.
This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin, Ren Cao, Zhongxue Gan, Siyang Leng   +3 more
wiley   +1 more source