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Research Paper#Quantum Computing, Traveling Salesman Problem, Ising Model, VQE🔬 ResearchAnalyzed: Jan 3, 2026 15:39

Quantum Computing for Traveling Salesman Problem

Published:Dec 30, 2025 16:04
1 min read
ArXiv

Analysis

This paper explores the application of quantum computing, specifically using the Ising model and Variational Quantum Eigensolver (VQE), to tackle the Traveling Salesman Problem (TSP). It highlights the challenges of translating the TSP into an Ising model and discusses the use of VQE as a SAT-solver, qubit efficiency, and the potential of Discrete Quantum Exhaustive Search to improve VQE. The work is relevant to the Noisy Intermediate Scale Quantum (NISQ) era and suggests broader applicability to other NP-complete and even QMA problems.
Reference

“The paper discusses the use of VQE as a novel SAT-solver and the importance of qubit efficiency in the Noisy Intermediate Scale Quantum-era.”

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Research Paper#Array Signal Processing, Sparse Arrays, Robustness🔬 ResearchAnalyzed: Jan 3, 2026 16:47

Optimal Robust Sparse Arrays: Discovery and Closed-Form Expressions

Published:Dec 30, 2025 11:39
1 min read
ArXiv

Analysis

This paper addresses the critical issue of sensor failure robustness in sparse arrays, which are crucial for applications like radar and sonar. It extends the known optimal configurations of Robust Minimum Redundancy Arrays (RMRAs) and provides a new family of sub-optimal RMRAs with closed-form expressions (CFEs), making them easier to design and implement. The exhaustive search method and the derivation of CFEs are significant contributions.
Reference

“The novelty of this work is two-fold: extending the catalogue of known optimal RMRAs and formulating a sub-optimal RMRA that abides by CFEs.”

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Research Paper#Additive Manufacturing, Machine Learning, Composite Materials🔬 ResearchAnalyzed: Jan 3, 2026 20:06

SE-WDNN for CFRC-AM Property Prediction

Published:Dec 26, 2025 22:27
1 min read
ArXiv

Analysis

This paper addresses the challenge of predicting multiple properties of additively manufactured fiber-reinforced composites (CFRC-AM) using a data-efficient approach. The authors combine Latin Hypercube Sampling (LHS) for experimental design with a Squeeze-and-Excitation Wide and Deep Neural Network (SE-WDNN). This is significant because CFRC-AM performance is highly sensitive to manufacturing parameters, making exhaustive experimentation costly. The SE-WDNN model outperforms other machine learning models, demonstrating improved accuracy and interpretability. The use of SHAP analysis to identify the influence of reinforcement strategy is also a key contribution.
Reference

“The SE-WDNN model achieved the lowest overall test error (MAPE = 12.33%) and showed statistically significant improvements over the baseline wide and deep neural network.”

PermalinkArXiv