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Relevant bibliographies by topics / Quantum Neural Networks

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Academic literature on the topic 'Quantum Neural Networks'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Quantum Neural Networks"

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Xu, Zenglin. "Tensor Networks Meet Neural Networks." Journal of Physics: Conference Series 2278, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2278/1/012003.

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Abstract As a simulation of the human cognitive system, deep neural networks have achieved great success in many machine learning tasks and are the main driving force of the current development of artificial intelligence. On the other hand, tensor networks as an approximation of quantum many-body systems in quantum physics are applied to quantum physics, statistical physics, quantum chemistry and machine learning. This talk will first give a brief introduction to neural networks and tensor networks, and then discuss the cross-field research between deep neural networks and tensor networks, suc

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Lewenstein, Maciej, and Mariusz Olko. "‘Quantum’ neural networks." Network: Computation in Neural Systems 2, no. 2 (1991): 207–30. http://dx.doi.org/10.1088/0954-898x_2_2_005.

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Gupta, Sanjay, and R. K. P. Zia. "Quantum Neural Networks." Journal of Computer and System Sciences 63, no. 3 (2001): 355–83. http://dx.doi.org/10.1006/jcss.2001.1769.

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Cong, Iris, Soonwon Choi, and Mikhail D. Lukin. "Quantum convolutional neural networks." Nature Physics 15, no. 12 (2019): 1273–78. http://dx.doi.org/10.1038/s41567-019-0648-8.

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Diep, Do Ngoc. "Some Quantum Neural Networks." International Journal of Theoretical Physics 59, no. 4 (2020): 1179–87. http://dx.doi.org/10.1007/s10773-020-04397-1.

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Toth, Geza, Craig S. Lent, P. Douglas Tougaw, et al. "Quantum cellular neural networks." Superlattices and Microstructures 20, no. 4 (1996): 473–78. http://dx.doi.org/10.1006/spmi.1996.0104.

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Chang, Yi-Fang. "Biological Lattice Gauge Theory as Modeling of Quantum Neural Networks." Journal of Modeling and Optimization 10, no. 1 (2018): 23. http://dx.doi.org/10.32732/jmo.2018.10.1.23.

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Based on quantum biology and biological gauge field theory, we propose the biological lattice gauge theory as modeling of quantum neural networks. This method applies completely the same lattice theory in quantum field, but, whose two anomaly problems may just describe the double helical structure of DNA and violated chiral symmetry in biology. Further, we discuss the model of Neural Networks (NN) and the quantum neutral networks, which are related with biological loop quantum theory. Finally, we research some possible developments on described methods of networks by the extensive graph theory

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Dong, Yu-Chao, Xi-Kun Li, Ming Yang, et al. "Quantum state classification via complex-valued neural networks." Laser Physics Letters 21, no. 10 (2024): 105206. http://dx.doi.org/10.1088/1612-202x/ad7246.

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Abstract To efficiently complete quantum information processing tasks, quantum neural networks (QNNs) should be introduced rather than the common classical neural networks, but the QNNs in the current noisy intermediate-scale quantum era cannot perform better than classical neural networks because of scale and the efficiency limits. So if the quantum properties can be introduced into classical neural networks, more efficient classical neural networks may be constructed for tasks in the field of quantum information. Complex numbers play an indispensable role in the standard quantum theory, and

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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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Purushothaman, G., and N. B. Karayiannis. "Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks." IEEE Transactions on Neural Networks 8, no. 3 (1997): 679–93. http://dx.doi.org/10.1109/72.572106.

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Dissertations / Theses on the topic "Quantum Neural Networks"

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Menneer, Tamaryn Stable Ia. "Quantum artificial neural networks." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286530.

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Ahamed, Woakil Uddin. "Quantum recurrent neural networks for filtering." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:2411.

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The essence of stochastic filtering is to compute the time-varying probability densityfunction (pdf) for the measurements of the observed system. In this thesis, a filter isdesigned based on the principles of quantum mechanics where the schrodinger waveequation (SWE) plays the key part. This equation is transformed to fit into the neuralnetwork architecture. Each neuron in the network mediates a spatio-temporal field witha unified quantum activation function that aggregates the pdf information of theobserved signals. The activation function is the result of the solution of the SWE. Theincorpor

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Pesah, Arthur. "Learning quantum state properties with quantum and classical neural networks." Thesis, KTH, Tillämpad fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252693.

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Du, Yuxuan. "The Power of Quantum Neural Networks in The Noisy Intermediate-Scale Quantum Era." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24976.

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Machine learning (ML) has revolutionized the world in recent years. Despite the success, the huge computational overhead required by ML models makes them approach the limits of Moore’s law. Quantum machine learning (QML) is a promising way to conquer this issue, empowered by Google's demonstration of quantum computational supremacy. Meanwhile, another cornerstone in QML is validating that quantum neural networks (QNNs) implemented on the noisy intermediate-scale quantum (NISQ) chips can accomplish classification and image generation tasks. Despite the experimental progress, little is known abo

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Linn, Hanna. "Detecting quantum speedup for random walks with artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289347.

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Random walks on graphs are an essential base for crucial algorithms for solving problems, like the boolean satisfiability problem. A speedup of random walks could improve these algorithms. The quantum version of the random walk, quantum walk, is faster than random walks in specific cases, e.g., on some linear graphs. An analysis of when the quantum walk is faster than the random walk can be accomplished analytically or by simulating both the walks on the graph. The problem arises when the graphs grow in size and connectivity. There are no known general rules for what an arbitrary graph not hav

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Hu, Zhizhai, University of Western Sydney, of Science Technology and Environment College, and School of Computing and Information Technology. "Quantum computation via neural networks applied to image processing and pattern recognition." THESIS_CSTE_CIT_Hu_Z.xml, 2001. http://handle.uws.edu.au:8081/1959.7/136.

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This thesis explores moving information processing by means of quantum computation technology via neural networks. A new quantum computation algorithm achieves a double-accurate outcome on measuring optical flows in a video. A set of neural networks act as experimental tools that manipulate the applied data. Attempts have been made to calculate a pixel's location, velocity and grey scale value of moving images but the location and velocity could not be simultaneously measured precisely enough in accordance with both classical and quantum uncertainty principles. The error in measurement produce

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Hu, Zhizhai. "Quantum computation via neural networks applied to image processing and pattern recognition." Thesis, View thesis, 2001. http://handle.uws.edu.au:8081/1959.7/136.

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This thesis explores moving information processing by means of quantum computation technology via neural networks. A new quantum computation algorithm achieves a double-accurate outcome on measuring optical flows in a video. A set of neural networks act as experimental tools that manipulate the applied data. Attempts have been made to calculate a pixel's location, velocity and grey scale value of moving images but the location and velocity could not be simultaneously measured precisely enough in accordance with both classical and quantum uncertainty principles. The error in measurement produce

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Hu, Zhizhai. "Quantum computation via neural networks applied to image processing and pattern recognition /." View thesis, 2001. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030731.105740/index.html.

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Thesis (PhD) -- University of Western Sdyney, 2001.<br>"A thesis submitted for the Degree of Doctor of Philosophy of the University of Western Sydney, Australia. School of Computing and Information Technology." "August 2001" Bibliography: leaves 136 - 143.

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Ellis, Mathys. "Regularised feed forward neural networks for streamed data classification problems." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/75804.

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Streamed data classiﬁcation problems (SDCPs) require classifiers with the ability to learn and to adjust to the underlying relationships in data streams, in real-time. This requirement poses a challenge to classifiers, because the learning task is no longer just to ﬁnd the optimal decision boundaries, but also to track changes in the decision boundaries as new training data is received. The challenge is due to concept drift, i.e. the changing of decision boundaries over time. Changes include disappearing, appearing, or shifting decision boundaries. This thesis proposes an online learning appro

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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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Books on the topic "Quantum Neural Networks"

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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, 2020. http://dx.doi.org/10.1007/978-3-030-52715-0.

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Quantum Neural Computation. Springer, 2009.

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Ivancevic, Vladimir G., and Tijana T. Ivancevic. Quantum Neural Computation. Springer, 2010.

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Ivancevic, Vladimir G., and Tijana T. Ivancevic. Quantum Neural Computation. Springer, 2016.

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Book chapters on the topic "Quantum Neural Networks"

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Subbiah, Priyanga, M. Kandan, N. Krishnaraj, and Shaji K. A. Theodore. "Quantum Neural Networks." In Quantum Computing. Auerbach Publications, 2025. https://doi.org/10.1201/9781003499459-8.

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Hardy, Yorick, and Willi-Hans Steeb. "Neural Networks." In Classical and Quantum Computing. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8366-5_14.

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Pastorello, Davide. "Quantum Neural Networks." In Concise Guide to Quantum Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6_9.

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Ezhov, Alexandr A., and Dan Ventura. "Quantum Neural Networks." In Future Directions for Intelligent Systems and Information Sciences. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1856-7_11.

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Ramakrishnan, Akshay Bhuvaneswari, Pranav Manikandan, and Karthikeyan Saminathan. "Artificial Neural Networks." In Quantum Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003429654-7.

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Ganguly, Santanu. "Neural Networks." In Quantum Machine Learning: An Applied Approach. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_3.

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Ivancevic, Vladimir G., and Tijana T. Ivancevic. "Brain and Classical Neural Networks." In Quantum Neural Computation. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3350-5_2.

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Nicolay, Delphine, and Timoteo Carletti. "Quantum Neural Networks Achieving Quantum Algorithms." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78658-2_1.

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Altaisky, M. V., and N. E. Kaputkina. "Quantum Neural Networks and Quantum Intelligence." In Rhythmic Oscillations in Proteins to Human Cognition. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7253-1_6.

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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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Conference papers on the topic "Quantum Neural Networks"

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Liu, Chen-Yu, Kuan-Cheng Chen, and Chu-Hsuan Abraham Lin. "Learning Quantum Phase Estimation by Variational Quantum Circuits." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651206.

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Khatoniar, Ria, Debanjan Konar, and Vaneet Aggarwal. "Quantum-Enhanced Spiking Neural Networks." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10370.

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Alibrandi, Umberto, Claudio Perez, and Khalid M. Mosalam. "Quantum Physics Stochastic Neural Networks (QPNN)." In 2024 8th International Conference on System Reliability and Safety (ICSRS). IEEE, 2024. https://doi.org/10.1109/icsrs63046.2024.10927527.

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I, Varalakshmi, Akila A.K, Jazeera R, and Nanda S. Krishna. "Ransomware Detection Using Quantum Neural Networks." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894015.

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Chen, Samuel Yen-Chi, Huan-Hsin Tseng, Hsin-Yi Lin, and Shinjae Yoo. "Learning to Measure Quantum Neural Networks." In 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2025. https://doi.org/10.1109/icasspw65056.2025.11011001.

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Upadhyay, Suryansh, and Swaroop Ghosh. "Quantum Quandaries: Unraveling Encoding Vulnerabilities in Quantum Neural Networks." In 2025 26th International Symposium on Quality Electronic Design (ISQED). IEEE, 2025. https://doi.org/10.1109/isqed65160.2025.11014416.

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Innan, Nouhaila, Muhammad Al-Zafar Khan, Alberto Marchisio, Muhammad Shafique, and Mohamed Bennai. "FedQNN: Federated Learning using Quantum Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651123.

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Gushanskiy, Sergey, Viktor Potapov, and Maxim Polenov. "Quantum Neural Networks: Bridging Topological Structures and Variational Quantum Circuits." In 2025 International Russian Smart Industry Conference (SmartIndustryCon). IEEE, 2025. https://doi.org/10.1109/smartindustrycon65166.2025.10986184.

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Mahmud, Istiak, Ayush Asthana, Mark Hoffmann, and Ahmeb Abdelhadi. "Physics-Informed Neural Networks for Quantum Wavefunctions." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10927810.

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Herbst, Sabrina, Vincenzo De Maio, and Ivona Brandic. "On Optimizing Hyperparameters for Quantum Neural Networks." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00174.

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Reports on the topic "Quantum Neural Networks"

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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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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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Aktar, Shamminuj, Andreas Baertschi, Abdel-Hameed Badawy, Diane Oyen, and Stephan Eidenbenz. Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2350603.

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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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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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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap

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