Gotowe bibliografie tematyczne / Quantum neural network

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Gotowa bibliografia na temat „Quantum neural network”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Quantum neural network"

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Sineglazov, Victor, and Petro Chynnyk. "Quantum Convolution Neural Network." Electronics and Control Systems 2, no. 76 (2023): 40–45. http://dx.doi.org/10.18372/1990-5548.76.17667.

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In this work, quantum convolutional neural networks are considered in the task of recognizing handwritten digits. A proprietary quantum scheme for the convolutional layer of a quantum convolutional neural network is proposed. A proprietary quantum scheme for the pooling layer of a quantum convolutional neural network is proposed. The results of learning quantum convolutional neural networks are analyzed. The built models were compared and the best one was selected based on the accuracy, recall, precision and f1-score metrics. A comparative analysis was made with the classic convolutional neura
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Bonnell, G., and G. Papini. "Quantum neural network." International Journal of Theoretical Physics 36, no. 12 (1997): 2855–75. http://dx.doi.org/10.1007/bf02435714.

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Zhang, Yulu, and Hua Lu. "Reliability Research on Quantum Neural Networks." Electronics 13, no. 8 (2024): 1514. http://dx.doi.org/10.3390/electronics13081514.

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Quantum neural networks (QNNs) leverage the strengths of both quantum computing and neural networks, offering solutions to challenges that are often beyond the reach of traditional neural networks. QNNs are being used in areas such as computer games, function approximation, and big data processing. Moreover, quantum neural network algorithms are finding utility in social network modeling, associative memory systems, and automatic control mechanisms. Nevertheless, ensuring the reliability of quantum neural networks is crucial as it directly influences network performance and stability. To inves
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Yang, Xin. "Quantum fuzzy neural network based on fuzzy number." Frontiers in Computing and Intelligent Systems 3, no. 2 (2023): 99–105. http://dx.doi.org/10.54097/fcis.v3i2.7524.

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Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, th
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Laokondee, Suraphan, and Prabhas Chongstitvatana. "Quantum Neural Network model for Token allocation for Course Bidding." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 18, no. 1 (2024): 112–18. http://dx.doi.org/10.37936/ecti-cit.2024181.247613.

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Quantum computer has shown the advantage over the classical computer to solve some problems using the laws of quantum mechanics. With a combination of knowledge of machine learning and quantum computing, Quantum neural networks adapted the concept from classical neural networks and apply parameterized quantum gates as neural network weights. In this paper, we present an application of quantum neural networks with real-world data to predict token price used in a course bidding system. The experiments were carried out on the Qiskit quantum simulator. The result shows that quantum neural networks
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Freitas, Nahuel, Giovanna Morigi, and Vedran Dunjko. "Neural network operations and Susuki–Trotter evolution of neural network states." International Journal of Quantum Information 16, no. 08 (2018): 1840008. http://dx.doi.org/10.1142/s0219749918400087.

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It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states of many-body systems [G. Carleo and M. Troyer, Science 355(6325) (2017) 602.]. States represented in this way, called Neural Network States (NNSs), were shown to display interesting properties like the ability to efficiently capture long-range quantum correlations. However, identifying an optimal neural network representation of a given state might be challenging, and so far this problem has been addressed with stöc
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Trahan, Corey, Mark Loveland, and Samuel Dent. "Quantum Physics-Informed Neural Networks." Entropy 26, no. 8 (2024): 649. http://dx.doi.org/10.3390/e26080649.

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In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed, and hybrid configurations are explored. The results show that (1) for some applications, quantum PINNs can obtain comparable accuracy with less neural network parameters than classical PINNs, and (2) adding quantum nodes in classical PINNs can in
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Zhang, Zhisheng, and Wenjie Gong. "Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7910971.

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Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific
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Song, Junyang, Bo Lu, Lu Liu, and Chuan Wang. "Noisy Quantum Channel Characterization Using Quantum Neural Networks." Electronics 12, no. 11 (2023): 2430. http://dx.doi.org/10.3390/electronics12112430.

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Channel noise is considered to be the main obstacle in long-distance quantum communication and distributed quantum networks. Here, employing a quantum neural network, we present an efficient method to study the model and detect the noise of quantum channels. Based on various types of noisy quantum channel models, we construct the architecture of the quantum neural network and the model training process. Finally, we perform experiments to verify the training effectiveness of the scheme, and the results show that the cost function of the quantum neural network could approach above 90% of the cha
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Zhou, Rigui. "Quantum Competitive Neural Network." International Journal of Theoretical Physics 49, no. 1 (2009): 110–19. http://dx.doi.org/10.1007/s10773-009-0183-y.

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Rozprawy doktorskie na temat "Quantum neural network"

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Dikme, Altay. "A Quantum Neural Network for Noisy Intermediate Scale Quantum Devices." Thesis, KTH, Tillämpad fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300394.

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Neural networks have helped the field of machine learning grow tremendously in the past decade, and can be used to solve a variety of real world problems such as classification problems. On another front, the field of quantum computing has advanced, with quantum devices publicly available via the cloud. The availability of such systems has led to the creation of a new field of study, Quantum Machine Learning, which attempts to create quantum analogues of classical machine learning techniques. One such method is the Quantum Neural Network (QNN) inspired by classical neural networks. In this the
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SILVA, Adenilton José da. "Artificial neural network architecture selection in a quantum computer." UNIVERSIDADE FEDERAL DE PERNAMBUCO, 2015. https://repositorio.ufpe.br/handle/123456789/15011.

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Submitted by Isaac Francisco de Souza Dias (isaac.souzadias@ufpe.br) on 2016-01-27T17:25:47Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Adenilton José da Silva.pdf: 4885126 bytes, checksum: d2bade12d15d6626962f244aebd5678d (MD5)<br>Made available in DSpace on 2016-01-27T17:25:47Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tese Adenilton José da Silva.pdf: 4885126 bytes, checksum: d2bade12d15d6626962f244aebd5678d (MD5) Previous issue date: 2015-06-26<br>CNPq<br>Miniaturisa
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Gandhi, Vaibhav Sudhir. "Quantum neural network based EEG filtering and adaptive brain-robot interfaces." Thesis, Ulster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573092.

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Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. Dealing with the unknown embedded noise within the raw EEG and the inherent lower bandwidth of BCI are still two of the major challenges in making BC! practical for day-to-day use. The raw EEG signal recorded non-invasively during motor i ~~~y (MI) is intrinsically " ." embedded with non-Gaussian noise while the actual noise-free EEG has so far not been attained. The
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Mori, Yuto. "Path optimization with neural network for sign problem in quantum field theories." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263466.

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Schliebs, Stefan. "Heterogeneous probabilistic models for optimisation and modelling of evolving spiking neural networks." AUT University, 2010. http://hdl.handle.net/10292/963.

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This thesis proposes a novel feature selection and classification method employing evolving spiking neural networks (eSNN) and evolutionary algorithms (EA). The method is named the Quantum-inspired Spiking Neural Network (QiSNN) framework. QiSNN represents an integrated wrapper approach. An evolutionary process evolves appropriate feature subsets for a given classification task and simultaneously optimises the neural and learning-related parameters of the network. Unlike other methods, the connection weights of this network are determined by a fast one-pass learning algorithm which dramaticall
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Czischek, Stefanie [Verfasser], and Thomas [Akademischer Betreuer] Gasenzer. "Simulating Strongly Interacting Quantum Spin Systems–From Critical Dynamics Towards Entanglement Correlations in a Classical Artificial Neural Network / Stefanie Czischek ; Betreuer: Thomas Gasenzer." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/119790431X/34.

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Alzate, Banguero Melissa. "Towards neuromorphic computing on quantum many-body architectures : VO2 transition dynamics." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS021.

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Alors que les exigences en matière d'IA augmentent, de nouveaux paradigmes informatiques deviennent essentiels. Les architectures traditionnelles de von Neumann peinent à répondre aux exigences intensives de l'IA. L'informatique neuromorphique, inspirée par le cerveau, intègre traitement et mémoire pour une computation plus rapide et efficace, idéale pour des applications d'IA comme l'apprentissage profond et la reconnaissance de formes. Les matériaux clés pour l'informatique neuromorphique incluent les synaptors et les neuristors. Les memristors, des mémoires non volatiles fabriquées à partir
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Cîrstea, Bogdan-Ionut. "Contribution à la reconnaissance de l'écriture manuscrite en utilisant des réseaux de neurones profonds et le calcul quantique." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0059.

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Dans cette thèse, nous fournissons plusieurs contributions des domaines de l’apprentissage profond et du calcul quantique à la reconnaissance de l’écriture manuscrite. Nous commençons par intégrer certaines des techniques d’apprentissage profond les plus récentes(comme dropout, batch normalization et différentes fonctions d’activation) dans les réseaux de neurones à convolution et obtenons des meilleures performances sur le fameux jeu de données MNIST. Nous proposons ensuite des réseaux TSTN (Tied Spatial Transformer Networks), une variante des réseaux STN (Spatial Transformer Networks) avec p
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Hutin, Hector. "Mesure et contrôle assisté par réseau de neurones de modes micro-ondes quantiques avec des circuits supraconducteurs." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0092.

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L'association d'un bit quantique et d'un mode harmonique couplé dispersivement est l'un des systèmes les plus étudiés dans le domaine de l'électrodynamique quantique de circuits supraconducteurs. Il offre un des moyens actuels les plus fiables pour mesurer l'état d'un bit quantique de manière non-destructive, et a permis nombre d'expériences d'information quantique étudiant l'information obtenue via cette mesure et la dynamique du qubit lorsqu'il y est soumis. Il offre également la possibilité de préparer et de manipuler l'état quantique du mode harmonique grâce au qubit, ce qui permet de stoc
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Wu, Jiaxin. "Topics in Cold Atoms Related to Quantum Information Processing and A Machine Learning Approach to Condensed Matter Physics." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156320039156199.

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Książki na temat "Quantum neural network"

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Czischek, Stefanie. Neural-Network Simulation of Strongly Correlated Quantum Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52715-0.

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1919-, Pribram Karl H., and Eccles, John C. Sir, 1903-, eds. Rethinking neural networks: Quantum fields and biological data. Erlbaum, 1993.

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1919-, Pribram Karl H., and Eccles, John C. Sir, 1903-, eds. Rethinking neural networks: Quantum fields and biological data : proceedings of the First Appalachian Conference on Behavioral Neurodynamics. Erlbaum, 1993.

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1994, Altherr Tanguy d., Aurenche P, Veneziano G, and Sorba P, eds. From thermal field theory to neural networks: A day to remember Tanguy Altherr, Cern, 4 November 1994. World Scientific, 1996.

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Perus, Mitja. Biological and quantum computing for human vision: Holonomic models and applications. Medical Information Science Reference, 2011.

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1921-, Caianiello Eduardo R., Marinaro M, Scarpetta G, Università degli studi di Salerno. Dipartimento di fisica teorica, Istituto italiano per gli studi filosofici, and International Institute for Advanced Scientific Studies, eds. Structure: From physics to general systems : festschrift volume in honour of E.R. Caianiello on his seventieth birthday : Amalfi, Salerno, Italy, 20-24 October 1991. World Scientific, 1992.

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Kiong, Loo Chu. Biological and quantum computing for human vision: Holonomic models and applications. Medical Information Science Reference, 2011.

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Czischek, Stefanie. Neural-Network Simulation of Strongly Correlated Quantum Systems. Springer International Publishing AG, 2020.

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Czischek, Stefanie. Neural-Network Simulation of Strongly Correlated Quantum Systems. Springer International Publishing AG, 2021.

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Conti, Claudio. Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing. Springer International Publishing AG, 2023.

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Części książek na temat "Quantum neural network"

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Zhou, Rigui, Ling Qin, and Nan Jiang. "Quantum Perceptron Network." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_68.

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Kommadi, Bhagvan. "Quantum Neural Network Algorithms." In Quantum Computing Solutions. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6516-1_8.

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Meng, Qinxue, Jiarun Zhang, Zhao Li, Ming Li, and Lin Cui. "Entangled Quantum Neural Network." In Studies in Computational Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9530-9_14.

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Kinjo, Mitsunaga, Shigeo Sato, and Koji Nakajima. "Quantum Adiabatic Evolution Algorithm for a Quantum Neural Network." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_113.

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Yu, Shao-Bo, Shou-Ren Hu, and Jun-Yong Yan. "Quantum Learning Algorithm for Multilayered Neural Network." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_79.

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Hdaib, Moe, Sutharshan Rajasegarar, and Lei Pan. "Quantum Autoencoder Frameworks for Network Anomaly Detection." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8073-4_6.

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Anh, Tu Tran, and The Dung Luong. "Malwares Classification Using Quantum Neural Network." In Advances in Information and Communication Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49073-1_37.

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Aher, Rani Nandkishor, and Nandkishor Daulat Aher. "Audio Compression Using Quantum Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0838-7_20.

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Hu, Shiyan. "Quantum Neural Network for Image Watermarking." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28648-6_107.

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Gastegger, Michael, and Philipp Marquetand. "Molecular Dynamics with Neural Network Potentials." In Machine Learning Meets Quantum Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7_12.

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Streszczenia konferencji na temat "Quantum neural network"

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Pang, Xin, Yanan Li, and Zhimin Wang. "Quantum neural network quantum state." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3059735.

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Muscalagiu, Anca-Ioana. "Quantum Neural Network Design via Quantum Deep Reinforcement Learning." In 16th International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012997500003837.

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Li, Chuang, Zhaolin Liu, and Shibin Zhang. "Multiclassification quantum neural network based on variational quantum circuits." In Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), edited by Feng Yin and Zehui Zhan. SPIE, 2024. http://dx.doi.org/10.1117/12.3045429.

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Zheng, FangCong, ZhaoLin Liu, and ShiBin Zhang. "Quantum Convolutional Neural Network based on Variational Quantum Fuzzy Inference." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667586.

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Zhou, Shuo, Yihang Zhou, Congcong Liu, et al. "Quantum Neural Network for Accelerated Magnetic Resonance Imaging." In 2024 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2024. https://doi.org/10.1109/ist63414.2024.10759235.

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Liu, Shilong, and Yongjian Gu. "Quantum spiking neural networks for image classification." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3059865.

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Mittal, Sparsh, Yash Chand, and Neel Kanth Kundu. "Hybrid Quantum Neural Network Based Indoor User Localization Using Cloud Quantum Computing." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752291.

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Bosco, Daniele Lizzio, Riccardo Romanello, Giuseppe Serra, and Carla Piazza. "Softer is Better: Tweaking Quantum Dropout to Enhance Quantum Neural Network Trainability." In 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025. https://doi.org/10.1109/qcnc64685.2025.00076.

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Kumar, S. Vinod, Anurag Shrivastava, Aravinda K, Lavish Kansal, Ravi Kalra, and Hawraa Ali Sabah. "Leveraging Graph Neural Networks for Complex Network Traffic Signal Processing." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545165.

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Anusha, Mareddy, R. Thangam, and Sanjay Dubey. "Quantum Neural Network for Image Feature Extraction using MNIST." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714726.

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Raporty organizacyjne na temat "Quantum neural network"

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. W
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Valdez, Luis, and Alexander Heifetz. Preliminary Assessment of Qiskit Quantum Simulator Capabilities for Development of Quantum Hopfield Neural Network for Anomaly Detection Applications (Q4 Report). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1832153.

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Valdez, Luis, Miltos Alamaniotis, and Alexander Heifetz. Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1894587.

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Perdigão, Rui A. P. Neuro-Quantum Cyber-Physical Intelligence (NQCPI). Synergistic Manifolds, 2024. http://dx.doi.org/10.46337/241024.

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Neuro-Quantum Cyber-Physical Intelligence (NQCPI) is hereby introduced, entailing a novel framework for nonlinear natural-based neural post-quantum information physics, along with novel advances in far-from-equilibrium thermodynamics and evolutionary cognition in post-quantum neurobiochemistry for next-generation information physical systems intelligence. NQCPI harnesses and operates with the higher-order nonlinear nature of previously elusive quantum behaviour, including in open chaotic dissipative systems in thermodynamically and magneto-electrodynamically disruptive conditions, such as in n
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Raychev, Nikolay. Precision modeling of applied quantum neural networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i1.25.

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Ortiz Marrero, Carlos, Nathan Wiebe, James Furches, and Michael Ragone. Quantum Neural Networks: Issues, Training, and Applications. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2337965.

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Lin, Youzuo. Intelligent Quantum Sensing with Quantum Neural Networks: an Application to Earthquake Detection. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1890966.

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Pasupuleti, Murali Krishna. Quantum Semiconductors for Scalable and Fault-Tolerant Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr825.

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Abstract: Quantum semiconductors are revolutionizing computing by enabling scalable, fault-tolerant quantum processors that overcome the limitations of classical computing. As quantum technologies advance, superconducting qubits, silicon spin qubits, topological qubits, and hybrid quantum-classical architectures are emerging as key solutions for achieving high-fidelity quantum operations and long-term coherence. This research explores the materials, device engineering, and fabrication challenges associated with quantum semiconductors, focusing on quantum error correction, cryogenic control sys
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