Responsible Classic/Quantum AI Technologies for Industrial Applications

Topic Information

Dear Colleagues,

The rapid evolution of artificial intelligence has entered a new phase with the convergence of classical machine learning and emerging quantum computing techniques. While classical AI has already demonstrated transformative potential in diverse industrial domains such as manufacturing, energy systems, healthcare, logistics, and finance, quantum AI promises to address complex optimization and learning problems that are computationally intractable with conventional methods. However, alongside these opportunities arise pressing concerns of responsibility, transparency, and trustworthiness, particularly as AI technologies are deployed in safety-critical and high-stakes industrial environments. This multidisciplinary topic seeks to provide a timely platform for advancing responsible AI by bridging classical and quantum paradigms, with an emphasis on practical industrial applications. Contributions will explore not only the design of hybrid classical–quantum models and algorithms but also frameworks that ensure fairness, accountability, and ethical use. Topics include but are not limited to responsible AI governance, privacy-preserving learning, robust optimization with quantum resources, interpretable machine learning models for industrial processes, and evaluation metrics for trustworthy deployments. By uniting theoretical advances with real-world case studies, this multidisciplinary topic aims to shape a roadmap toward the responsible adoption of AI, both classical and quantum, for sustainable and ethical industrial innovation. 

Dr. Youyang Qu
Dr. Khandakar Ahmed
Dr. Zhiyi Tian
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	23.1 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Entropy entropy	2.0	5.2	1999	21.5 Days	CHF 2600	Submit
Quantum Reports quantumrep	1.3	3.0	2019	19.8 Days	CHF 1400	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Sensors Electronics Technologies AI Entropy Quantum Reports BDCC

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

21 pages, 6282 KB  

Open AccessArticle

Comparative Evaluation of Deep Learning Object Detectors for Embedded Weed Detection on Resource-Constrained Platforms

by Nurtay Albanbay, Yerik Nugman, Mukhagali Sagyntay, Azamat Mustafa, Ramona Blanes, Algazy Zhauyt, Rustem Kaiyrov and Nurgali Nurgozhayev

Technologies 2026, 14(5), 265; https://doi.org/10.3390/technologies14050265 - 27 Apr 2026

Viewed by 285

Abstract

Computer vision–based weed detection plays a critical role in agricultural robotics, enabling accurate, selective weeding. These systems operate on resource-constrained embedded platforms, which introduces a significant trade-off between accuracy and efficiency. This study presents a comparative evaluation of six detection models (YOLOv11n, YOLOv11s, [...] Read more.

Computer vision–based weed detection plays a critical role in agricultural robotics, enabling accurate, selective weeding. These systems operate on resource-constrained embedded platforms, which introduces a significant trade-off between accuracy and efficiency. This study presents a comparative evaluation of six detection models (YOLOv11n, YOLOv11s, SSD-Lite, NanoDet, Faster R-CNN, RT-DETR) for agro-robotic applications, measuring precision, recall, mAP@0.5, and runtime on low-power hard-ware. NanoDet achieved the highest detection accuracy (precision 98.6%, recall 94.2%, mAP@0.5 97.7%). YOLOv11s demonstrated similar performance (mAP@0.5: 96.1%) but required more computation. YOLOv11n provides the most favourable balance between accuracy and throughput (mAP@0.5: 94.6%, 207 FPS on a workstation). On Raspberry Pi 5, light models achieved 3–5 FPS. RT-DETR and Faster R-CNN exhibited high latency (3112–6500 ms/frame), which prevents real-time operation. NanoDet excelled in detection, while YOLOv11n provides the best balance between accuracy and efficiency for limited devices. Full article

(This article belongs to the Topic Responsible Classic/Quantum AI Technologies for Industrial Applications)

►▼ Show Figures

Figure 1

15 pages, 378 KB  

Open AccessArticle

SparsePool: A Graph Pooling Framework via Sparse Representation for Graph Classification

by Zehan Li, Xuemeng Zhai, Hangyu Hu, Jiandong Liang and Guangmin Hu

Sensors 2026, 26(9), 2627; https://doi.org/10.3390/s26092627 - 23 Apr 2026

Viewed by 962

Abstract

Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effectively leverage global structural information, leading [...] Read more.

Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effectively leverage global structural information, leading to loss of critical substructures and limited interpretability—key limitations in molecular analysis and social network mining. To address these issues, we propose SparsePool, a graph pooling method that integrates node features and structural patterns through atomic decomposition. By dynamically decomposing graphs into interpretable atomic units via Boolean matrix factorization, SparsePool preserves semantically meaningful substructures while providing transparent evidence of retained patterns. We further introduce an Atomic Pooling Neural Network (APNN) for graph representation learning. Extensive experiments on relevant benchmarks including biochemical and social network datasets demonstrate that SparsePool outperforms state-of-the-art pooling methods, achieving an average classification accuracy improvement of 1.03% over baseline models while reducing structural information loss. We also discuss its compatibility with emerging quantum computing paradigms, such as quantum-accelerated sparse decomposition, as a promising direction for scaling graph processing in industrial contexts. Full article

(This article belongs to the Topic Responsible Classic/Quantum AI Technologies for Industrial Applications)

►▼ Show Figures

Figure 1

11 pages, 1747 KB  

Open AccessCommunication

A New Mathematical Framework for CMOS Si Photomultiplier Detection Rates in Quantum Cryptography

by Tal Gofman and Yael Nemirovsky

Sensors 2026, 26(4), 1386; https://doi.org/10.3390/s26041386 - 22 Feb 2026

Viewed by 464

Abstract

The deployment of Discrete Variable Quantum Key Distribution (DV-QKD) in high-traffic, short-reach environments, such as intra-data center networks, is currently constrained by the saturation of single-photon detectors. While CMOS Single-Photon Avalanche Diodes (SPADs) offer a cost-effective solution, their Secure Key Rate (SKR) is [...] Read more.

The deployment of Discrete Variable Quantum Key Distribution (DV-QKD) in high-traffic, short-reach environments, such as intra-data center networks, is currently constrained by the saturation of single-photon detectors. While CMOS Single-Photon Avalanche Diodes (SPADs) offer a cost-effective solution, their Secure Key Rate (SKR) is limited by detector dead time. To the best of the authors’ knowledge, this work is the first to derive a generalized detection rate model for SiPMs that addresses the dead-time bottlenecks of gigahertz-rate quantum cryptography. While methods for managing deadtime via active optical switching have been proposed, our model quantifies the benefits of passive spatial multiplexing inherent in standard SiPM arrays. Furthermore, contrasting with models designed to optimize energy resolution or characterize nonlinear charge response to light pulses, our work focuses on maximizing the detection count rate. We derive exact detection rate models for both analog (paralyzable) and digital (non-paralyzable) SiPM architectures, incorporating correlated noise sources such as optical crosstalk and afterpulsing. Simulation results indicate that SiPMs can increase detection rates by over an order of magnitude compared to single SPADs. Full article

(This article belongs to the Topic Responsible Classic/Quantum AI Technologies for Industrial Applications)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3