Real‐Time Self‐Optimization of Quantum Dot Laser Emissions During Machine Learning‐Assisted Epitaxy by Chao Shen, Wenkang Zhan, Shujie Pan, Hongyue Hao, Ning Zhuo, Kaiyao Xin, Hui Cong, Chi Xu, Bo Xu, Tien Khee Ng, Siming Chen, Chunlai Xue, Zhanguo Wang, Chao Zhao

“…In this work, in situ reflection high‐energy electron diffraction (RHEED) is integrated with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. …”
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Quantum Neural Network Based Machine Translator for Hindi to English by Ravi Narayan, V. P. Singh, S. Chakraverty

“…The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. …”
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Machine learning assisted zero-area pulse design in an open quantum system by Yi-Ye Li, Lian-Ao Wu, Zhao-Ming Wang

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Retentive neural quantum states: efficient ansätze for ab initio quantum chemistry by Oliver Knitter, Dan Zhao, James Stokes, Martin Ganahl, Stefan Leichenauer, Shravan Veerapaneni

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The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

“…Abstract This study aims to comprehensively analyze and classify the badminton players’ swing actions by combining the theoretical frameworks of quantum mechanics and machine learning. A badminton stroke recognition method based on Quantum Convolutional Neural Network (QCNN) is proposed. …”
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Federated learning with tensor networks: a quantum AI framework for healthcare by Amandeep Singh Bhatia, David E Bernal Neira

“…In this work, we propose a federated learning framework based on quantum tensor networks (QTNs) that takes advantage of the principles of many-body quantum physics. …”
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Quantum Vulnerability Analysis to Guide Robust Quantum Computing System Design by Fang Qi, Kaitlin N. Smith, Travis LeCompte, Nian-feng Tzeng, Xu Yuan, Frederic T. Chong, Lu Peng

“…While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. …”
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Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation by Matvei Anoshin, Asel Sagingalieva, Christopher Mansell, Dmitry Zhiganov, Vishal Shete, Markus Pflitsch, Alexey Melnikov

“…This work develops an application of hybrid quantum generative models based on the integration of parameterized quantum circuits into known molecular generative adversarial networks and proposes quantum cycle architectures that improve model performance and stability during training. …”
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Enhanced natural parameterized quantum circuit by Yuquan Chen, Yanjun Hou, Zeyuan Wang, Tianyun Wang, Ze Wu, Zhaokai Li, Xinhua Peng

“…This work provides a powerful tool for the classical-quantum interface, paving the way for quantum big data processing during the noisy intermediate-scale quantum era.…”
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Combined use of long short‐term memory neural network and quantum computation for hierarchical forecasting of locational marginal prices by Xin Huang, Guozhong Liu, Jiajia Huan, Shuxin Luo, Jing Qiu, Feiyan Qin, Yunxia Xu

“…The middle layer is a QGWO‐optimised support vector machine (SVM) for classifing whether LMPs are price spikes. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…Abstract Quantum machine learning is among the most exciting potential applications of quantum computing. …”
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A Quantum Q-Learning Fault Diagnosis Method for Intelligent Manufacturing Equipment by Yi Chen, Kai Deng, Xuelin Du, Zichao Chang, Tong Wan

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Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions by J. Quetzalcóatl Toledo-Marín, Sebastian Gonzalez, Hao Jia, Ian Lu, Deniz Sogutlu, Abhishek Abhishek, Colin Gay, Eric Paquet, Roger G. Melko, Geoffrey C. Fox, Maximilian Swiatlowski, Wojciech Fedorko

“…To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). …”
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Quantum and complex-valued hybrid networks for multi-principal element alloys phase prediction by Shaochun Li, Yutong Sun, Lu Xiao, Weimin Long, Gang Wang, Junzhi Cui, Jingli Ren

“…Complex-valued neural networks process data in the complex domain, significantly reducing information loss during transitions. The experimental results show that the hybrid model achieves a phase classification accuracy of 94.93%, outperforming the best machine learning model by 2.27% and the quantum model by 8.67%. …”
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45th Winter School on Wave and Quantum Acoustics and XLIVth Winter School on Environmental Acoustics and Vibroacoustics by 45th Winter School on WQA; XLIVth Winter School on EA&V

“…They will present more than 40 lectures and reports. 45th Winter School on Wave and Quantum Acoustics constitutes a platform for sharing the results and achievements obtained in different branches of physical acoustics (e.g. molecular acoustics, quantum acoustics, acousto-optics, magnetoacoustics, photoacoustics, acoustics of solid state etc.). …”
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Improving the Detection Accuracy of Subsurface Damage in Optical Materials by Exploiting the Fluorescence Polarization Properties of Quantum Dots by Yana Cui, Xuelian Liu, Bo Xiao, Yajie Wu, Chunyang Wang

“…Optical materials are widely used in large optical systems such as lithography machines and astronomical telescopes. However, optical materials inevitably produce subsurface damage (SSD) during lapping and polishing processes, degrading the laser damage threshold and impacting the service life of the optical system. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…This review critically examines the integration of machine learning (ML) with conventional water quality monitoring and treatment methods, presenting a systematic comparison of their capabilities, limitations, and synergies. …”
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51st Winter School on Wave and Quantum Acoustics Lth Winter School on Environmental Acoustics and Vibroacoustics Szczyrk, Poland, February 27 – March 3, 2023 by 51st Winter School on Wave and Quantum Acoustics Lth Winter School on Environmental Acoustics and Vibroacoustics

“…Many persons well-known from previous editions of the Conference joined us, either on-site or on-line. During the Conference, a seminar on “Industrial and Impulsive Noise” was organized in association with SVANTEK, which attracted 30 participants. 51st Winter School on Wave and Quantum Acoustics constituted platforms for sharing the results and achievements obtained in different branches of physical acoustics, as molecular acoustics, quantum acoustics, acousto-optics, magnetoacoustics, photoacoustics, acoustics of solid state, acoustic emission, and others. …”
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49th Winter School on Wave and Quantum Acoustics XLVIIIth Winter School on Environmental Acoustics and Vibroacoustics Szczyrk, Poland, February 24–27, 2020 by Dariusz Bismor

“…49th Winter School on Wave and Quantum Acoustics constituted platforms for sharing the results and achievements obtained in different branches of physical acoustics, as molecular acoustics, quantum acoustics, acousto-optics, magnetoacoustics, photoacoustics, acoustics of solid state, acoustic emission, and others. …”
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Machine-Learning Classification of Port Wine Stain With Quantitative Features of Optical Coherence Tomography Image by Shengnan Ai, Chengming Wang, Wenxin Zhang, Wenchao Liao, Juicheng Hsieh, Zhenyu Chen, Bin He, Xiao Zhang, Ning Zhang, Ying Gu, Ping Xue

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