Results 41 to 50 of about 1,190 (203)

QuDASH: Quantum-Inspired Rate Adaptation Approach for DASH Video Streaming

IEEE Access, 2023
Internet traffic is dramatically increasing with the development of network technologies and video streaming traffic accounts for large amount within the total traffic, which reveals the importance to guarantee the quality of content delivery service ...
Bo Wei, Hang Song, Makoto Nakamura, Koichi Kimura, Nozomu Togawa, Jiro Katto   +5 more
doaj   +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

Toward Solution‐Time Advantage With Error‐Mitigated Quantum Annealing for Combinatorial Optimization

Advanced Physics Research, EarlyView.
This paper presents a novel error mitigation technique to address the qubit errors that occur when solving combinatorial optimization problems with quantum annealing. The approach significantly speeds up the computation to reach the global optimum solution for a correlated 3D image segmentation model for material microstructures, demonstrating a ...
Yushuang Sam Yang, Peter Tyson, Clement Chu, Anthony Bruce Murphy   +3 more
wiley   +1 more source

End‐to‐End Portfolio Optimization with Hybrid Quantum Annealing

Advanced Quantum Technologies, EarlyView.
This works presents a hybrid quantum‐classical framework for portfolio optimization that combines quantum assisted asset selection and rebalancing with classical weight allocation. The approach processes real market data, embeds it into Quadratic Unconstrained Binary Optimization formulations, and evaluates performance within a unified workflow ...
Sai Nandan Morapakula, Sangram Deshpande, Rakesh Yata, Rushikesh Ubale, Uday Wad, Kazuki Ikeda   +5 more
wiley   +1 more source

Computing canonical averages with quantum and classical optimizers: Thermodynamic reweighting for QUBO models of physical systems

Physical Review Research
We present a general method to compute canonical averages for physical models sampled via quantum or classical quadratic unconstrained binary optimization (QUBO).
Francesco Slongo, Cristian Micheletti
doaj   +1 more source

A Resource Efficient Ising Model‐Based Quantum Sudoku Solver

Software: Practice and Experience, EarlyView.
ABSTRACT Background Quantum algorithms exploit superposition and parallelism to address complex combinatorial problems, many of which fall into the non‐polynomial (NP) class. Sudoku, a widely known logic‐based puzzle, is proven to be NP‐complete and thus presents a suitable testbed for exploring quantum optimization approaches.
Wen‐Li Wang, Mei‐Huei Tang, Kevin Wang, Muhammad Abdul Basit, Yuan Tian, Md. Sanaul Haque   +5 more
wiley   +1 more source

Reinforcement Learning-Based Formulations With Hamiltonian-Inspired Loss Functions for Combinatorial Optimization Over Graphs

IEEE Access
Quadratic Unconstrained Binary Optimization (QUBO) is a versatile approach used to represent a wide range of NP-hard Combinatorial Optimization (CO) problems through binary variables. The transformation of QUBO to an Ising Hamiltonian is recognized as an
Redwan Ahmed Rizvee, Raheeb Hassan, Md. Mosaddek Khan   +2 more
doaj   +1 more source

Technical Specification for Hemocompatibility Assessment of Human Mesenchymal Stem Cells

Cell Proliferation, EarlyView.
A comprehensive evaluation of MSC haemocompatibility is a critical prerequisite for ensuring the safety of systemic delivery. A haemocompatibility level of ‘poor’ indicates a high probability of adverse blood reactions when MSCs contact with blood. ABSTRACT ‘Technical specification for haemocompatibility assessment of human mesenchymal stem cells’ is ...
Jialing Liu, Weiyang Liang, Wenjie Xia, Ping Wang, Jie Hao, Tao Na, Sunxing Yan, Yu Zhang, Ka Li, Qiyuan Li, Guangjin Pan, Jun Wei, Qubo Chen, Jiani Cao, Peijun Zhai, Boqiang Fu, Hengjun Gao, Yong Zhang, Lei Wang, Meimei Guo, Shijun Hu, Lijun Zhu, Yang Zhao, Weiqi Zhang, Zhihong Wu, Yifang Ping, Hongling Zhao, Shuaishuai Niu, Tongbiao Zhao, Aijin Ma, Andy Peng Xiang, Xiaoyong Chen   +31 more
wiley   +1 more source

PearSAN: A Machine Learning Method for Inverse Design Using Pearson Correlated Surrogate Annealing

Advanced Optical Materials, Volume 14, Issue 10, 13 March 2026.
A machine learning–assisted inverse design framework is introduced to overcome the curse of dimensionality in complex nanophotonic design problems. By leveraging Pearson‐correlated surrogate annealing (PearSAN) method within a generative latent space, rapid convergence toward optimal thermophotovoltaic metasurface designs is achieved, enabling precise ...
Michael Bezick, Blake A. Wilson, Vaishnavi Iyer, Yuheng Chen, Vladimir M. Shalaev, Sabre Kais, Alexander V. Kildishev, Brad Lackey, Alexandra Boltasseva   +8 more
wiley   +1 more source