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Artykuły w czasopismach na temat „Quantum neural network”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

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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Mitarai, Kosuke. "Quantum Features and Quantum Neural Network." Brain & Neural Networks 29, no. 4 (2022): 202–10. http://dx.doi.org/10.3902/jnns.29.202.

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Khalid, Mustafa, Jun Wu, Taghreed M. Ali, et al. "Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms." Brain Sciences 10, no. 7 (2020): 431. http://dx.doi.org/10.3390/brainsci10070431.

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Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks f

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Kim, Hyunji, Kyungbae Jang, Sejin Lim, Yeajun Kang, Wonwoong Kim, and Hwajeong Seo. "Quantum Neural Network Based Distinguisher on SPECK-32/64." Sensors 23, no. 12 (2023): 5683. http://dx.doi.org/10.3390/s23125683.

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As IoT technology develops, many sensor devices are being used in our life. To protect such sensor data, lightweight block cipher techniques such as SPECK-32 are applied. However, attack techniques for these lightweight ciphers are also being studied. Block ciphers have differential characteristics, which are probabilistically predictable, so deep learning has been utilized to solve this problem. Since Gohr’s work at Crypto2019, many studies on deep-learning-based distinguishers have been conducted. Currently, as quantum computers are developed, quantum neural network technology is developing.

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ANDRECUT, M., and M. K. ALI. "A QUANTUM NEURAL NETWORK MODEL." International Journal of Modern Physics C 13, no. 01 (2002): 75–88. http://dx.doi.org/10.1142/s0129183102002948.

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We present the algorithms necessary for the implementation of a quantum neural network with learning and classification tasks. A complete implementation for the classification and learning algorithms is given in terms of unitary quantum gates. Such a quantum neural network can be used to perform complex classification tasks or to solve the general problem of binary mapping.

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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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Al- Majeed, Ghassan H., Zainab T. Alisa, and Hassan Saadallah Naji. "Data Classification using Quantum Neural Network." Journal of Engineering 20, no. 04 (2023): 36–50. http://dx.doi.org/10.31026/j.eng.2014.04.03.

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In this paper, integrated quantum neural network (QNN), which is a class of feedforward neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of

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Tian, Jinkai, and Wenjing Yang. "Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks." Entropy 26, no. 11 (2024): 902. http://dx.doi.org/10.3390/e26110902.

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We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability.

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Torlai, Giacomo, Guglielmo Mazzola, Juan Carrasquilla, Matthias Troyer, Roger Melko, and Giuseppe Carleo. "Neural-network quantum state tomography." Nature Physics 14, no. 5 (2018): 447–50. http://dx.doi.org/10.1038/s41567-018-0048-5.

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Zhou, Rigui, and Qiulin Ding. "Quantum M-P Neural Network." International Journal of Theoretical Physics 46, no. 12 (2007): 3209–15. http://dx.doi.org/10.1007/s10773-007-9437-8.

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Kashif, Muhammad, and Saif Al-Kuwari. "Design Space Exploration of Hybrid Quantum–Classical Neural Networks." Electronics 10, no. 23 (2021): 2980. http://dx.doi.org/10.3390/electronics10232980.

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The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybri

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Xiong, Juechan, Xiao-Long Ren, and Linyuan Lü. "Finding Key Nodes in Complex Networks Through Quantum Deep Reinforcement Learning." Entropy 27, no. 4 (2025): 382. https://doi.org/10.3390/e27040382.

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Identifying key nodes in networks is a fundamental problem in network science. This study proposes a quantum deep reinforcement learning (QDRL) framework that integrates reinforcement learning with a variational quantum graph neural network, effectively identifying distributed influential nodes while preserving the network’s fundamental topological properties. By leveraging principles of quantum computing, our method is designed to reduce model parameters and computational complexity compared to traditional neural networks. Trained on small networks, it demonstrated strong generalization acros

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Uwizeyimana, Leopoldine, and Wilson Dr.Musoni. "Analytical Estimation of Quantum Convolutional Neural Network and Convolutional Neural Network for Breast Cancer Detection." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 316–26. https://doi.org/10.5281/zenodo.7652665.

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Experts predict that the use of artificial intelligence and quantum computing will transform medicine including medical imaging. One of the common malignant tumors in women and seriously threatens women’s physical and mental health is breast cancer. The high incidence and mortality of breast cancer are seriously threatening women’s physical and mental health. The long time it took to get breast cancer’s test result and the conditions which make delay without being treated which caused the loss of lives due to latency. Early screening for breast cancer via mammography, ultraso

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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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Liang, Ji Sheng, Hui Yang, Zi Bo Yuan, and Hui Wang. "The Research of Oil & Gas Energy Saving Index Prediction Based on Neural Network and Quantum-Behaved Particle Swarm Optimization." Applied Mechanics and Materials 347-350 (August 2013): 3550–54. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3550.

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This paper introduces method of gas energy saving target forecast that a quantum particle swarm optimization algorithm and BP neural network, using BP neural networks and quantum particle swarm global search ability strong advantage, through the method that improved average optimal position. It solved the BP neural network is being trapped in local minima and slow convergence speed problem. It realized target forecast that based on BP neural network and quantum particle swarm field energy saving. With the production water injection pump unit consumption data for training data, prediction resul

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Dong, Zhongtian, Marçal Comajoan Cara, Gopal Ramesh Dahale та ін. "ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks". Axioms 13, № 3 (2024): 188. http://dx.doi.org/10.3390/axioms13030188.

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This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Neural Networks (DNNs). We evaluate the performance of each network with three two-dimensional toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training dataset. Our results show that the Z2×Z2 EQNN and the QNN provide superior performance for smaller parameter sets and modes

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Moahmmed, Moahmmed, and Belal Al Al-Khateeb. "Quantum Convolutional Neural Network for Image Classification." Fusion: Practice and Applications 15, no. 2 (2024): 61–72. http://dx.doi.org/10.54216/fpa.150205.

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In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional

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Pasupuleti, Murali Krishna. "Hybrid Intelligence: Leveraging Superalgebraic Quantum States for Neural Network Acceleration." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 36–47. https://doi.org/10.62311/nesx/rp0325.

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Abstract: As artificial intelligence (AI) systems grow in complexity, the demand for efficient and scalable computational architectures has become increasingly urgent. This research introduces a hybrid intelligence paradigm that harnesses quantum superalgebraic states as a new computational substrate for accelerating neural network inference and training. By encoding activation patterns and weight transformations into superalgebraic quantum states, we establish a model that combines symbolic expressiveness with quantum parallelism. This framework explores the interaction between quantum algebr

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Kan, Bowen, Yingqi Tian, Daiyou Xie, Yangjun Wu, Yi Fan, and Honghui Shang. "Solving the Electronic Schrödinger Equation by Pairing Tensor-Network State with Neural Network Quantum State." Mathematics 12, no. 3 (2024): 433. http://dx.doi.org/10.3390/math12030433.

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Neural network methods have shown promise for solving complex quantum many-body systems. In this study, we develop a novel approach through incorporating the density-matrix renormalization group (DMRG) method with the neural network quantum state method. The results demonstrate that, when tensor-network pre-training is introduced into the neural network, a high efficiency can be achieved for quantum many-body systems with strong correlations.

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Gutiérrez, Irene López, and Christian B. Mendl. "Real time evolution with neural-network quantum states." Quantum 6 (January 20, 2022): 627. http://dx.doi.org/10.22331/q-2022-01-20-627.

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A promising application of neural-network quantum states is to describe the time dynamics of many-body quantum systems. To realize this idea, we employ neural-network quantum states to approximate the implicit midpoint rule method, which preserves the symplectic form of Hamiltonian dynamics. We ensure that our complex-valued neural networks are holomorphic functions, and exploit this property to efficiently compute gradients. Application to the transverse-field Ising model on a one- and two-dimensional lattice exhibits an accuracy comparable to the stochastic configuration method proposed in [

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Xiao, Hanwei, Xiaoguang Chen, and Jin Xu. "Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes." Applied Sciences 12, no. 11 (2022): 5662. http://dx.doi.org/10.3390/app12115662.

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The fidelity of quantum states is an important concept in quantum information. Improving quantum fidelity is very important for both quantum communication and quantum computation. In this paper, we use a quantum neural network (QNN) to enhance the fidelity of [6,2,2] quantum convolutional codes. Towards the circuit of quantum convolutional codes, the target quantum state |0⟩ or |1⟩ is turned into entangled quantum states, which can defend against quantum noise more effectively. As the quantum neural network works better for quantum states with low dimension, we divide the quantum circuits into

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Mohseni, Naeimeh, Junheng Shi, Tim Byrnes, and Michael J. Hartmann. "Deep learning of many-body observables and quantum information scrambling." Quantum 8 (July 18, 2024): 1417. http://dx.doi.org/10.22331/q-2024-07-18-1417.

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Machine learning has shown significant breakthroughs in quantum science, where in particular deep neural networks exhibited remarkable power in modeling quantum many-body systems. Here, we explore how the capacity of data-driven deep neural networks in learning the dynamics of physical observables is correlated with the scrambling of quantum information. We train a neural network to find a mapping from the parameters of a model to the evolution of observables in random quantum circuits for various regimes of quantum scrambling and test its generalization and extrapolation capabilities in apply

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Matsui, Nobuyuki. "Quantum Neural Network: Prospects for Quantum Machine Learning." Journal of The Japan Institute of Electronics Packaging 23, no. 2 (2020): 139–44. http://dx.doi.org/10.5104/jiep.23.139.

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Chen, Bu-Qing, and Xu-Feng Niu. "Quantum Neural Network with Improved Quantum Learning Algorithm." International Journal of Theoretical Physics 59, no. 7 (2020): 1978–91. http://dx.doi.org/10.1007/s10773-020-04470-9.

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Hou, Xuan. "Research on Hyperspectral Data Classification Based on Quantum Counter Propagation Neural Network." Advanced Materials Research 546-547 (July 2012): 1377–81. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1377.

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It proposes the model and learning algorithm of Quantum Counter Propagation Neural Network and applies which in hyperspectral data classification as well. On one hand, introducing quantum theory into the structure or training process of Counter Propagation Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Counter Propagation Neural Network by the means of quoting the thought, concept and principles of quantu

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Landman, Jonas, Natansh Mathur, Yun Yvonna Li, et al. "Quantum Methods for Neural Networks and Application to Medical Image Classification." Quantum 6 (December 22, 2022): 881. http://dx.doi.org/10.22331/q-2022-12-22-881.

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Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications. In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known t

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Ryu, Ju-Young, Eyuel Elala, and June-Koo Kevin Rhee. "Quantum Graph Neural Network Models for Materials Search." Materials 16, no. 12 (2023): 4300. http://dx.doi.org/10.3390/ma16124300.

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Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar num

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Ma, Xian Min, and Mei Hui Xu. "Fault Diagnosis of Coal Electrical Shearer Based on Quantum Neural." Applied Mechanics and Materials 574 (July 2014): 452–56. http://dx.doi.org/10.4028/www.scientific.net/amm.574.452.

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An improved quantum neural network model and its learning algorithm are proposed for fault diagnosis of the coal electrical haulage shearer in order to on line monitor working states of the large mining rotating machines. Based on traditional BP neural network, the three-layer quantum neural network is constructed to combine quantum calculation and neural network for the error correction learning algorithm. According to the information processing mode of the biology neuron and the quantum computing theory, the improved quantum neural network model has the ability of identifying uncertainty in

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ГРИНЬКО, ІРИНА, ТЕТЯНА СКРИПНИК та ОЛЕКСАНДР БАРМАК. "КВАНТОВІ ЗГОРТКОВІ НЕЙРОННІ МЕРЕЖІ: ОСОБЛИВОСТІ РЕАЛІЗАЦІЇ У ТЕХНІЧНИХ, ПРИРОДНИЧИХ І СОЦІАЛЬНО-ЕКОНОМІЧНИХ СИСТЕМАХ". Herald of Khmelnytskyi National University. Technical sciences 323, № 4 (2023): 87–94. https://doi.org/10.31891/2307-5732-2023-323-4-87-94.

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The paper analyses and investigates the usage of quantum convolutional neural networks in technical, natural, and socio-economic systems. Quantum convolutional neural networks are a novel approach to information processing that is based on the principles of quantum mechanics and artificial intelligence. In technical systems, the potential of using quantum convolutional neural networks for solving complex tasks such as image processing, machine learning, and prediction has been explored. The results have shown that quantum convolutional neural networks can provide more accurate and faster compu

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KONISHI, EIJI. "MODELING QUANTUM MECHANICAL OBSERVERS VIA NEURAL-GLIAL NETWORKS." International Journal of Modern Physics B 26, no. 09 (2012): 1250060. http://dx.doi.org/10.1142/s0217979212500609.

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We investigate the theory of observers in the quantum mechanical world by using a novel model of the human brain which incorporates the glial network into the Hopfield model of the neural network. Our model is based on a microscopic construction of a quantum Hamiltonian of the synaptic junctions. Using the Eguchi–Kawai large N reduction, we show that, when the number of neurons and astrocytes is exponentially large, the degrees of freedom (d.o.f) of the dynamics of the neural and glial networks can be completely removed and, consequently, that the retention time of the superposition of the wav

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Maksimovic, Milan, and Ivan S. Maksymov. "Transforming Neural Networks into Quantum-Cognitive Models: A Research Tutorial with Novel Applications." Technologies 13, no. 5 (2025): 183. https://doi.org/10.3390/technologies13050183.

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Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mim

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Sun, Yinqian, Yi Zeng, and Tielin Zhang. "Quantum superposition inspired spiking neural network." iScience 24, no. 8 (2021): 102880. http://dx.doi.org/10.1016/j.isci.2021.102880.

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Zhou, Min-Gang, Zhi-Ping Liu, Hua-Lei Yin, Chen-Long Li, Tong-Kai Xu, and Zeng-Bing Chen. "Quantum Neural Network for Quantum Neural Computing." Research, April 14, 2023. http://dx.doi.org/10.34133/research.0134.

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Shen, Feihong, and Jun Liu. "Quantum Fourier Convolutional Network." ACM Transactions on Multimedia Computing, Communications, and Applications, July 5, 2022. http://dx.doi.org/10.1145/3514249.

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The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted by the scope of the hardware development. Nevertheless, many neural network algorithms had been proposed before GPUs become powerful enough for running very deep models. Similarly, quantum algorithms can also be proposed as knowledge reserve before real quantum computers are easily accessible. Specifically, taking advantage of both the neural networ

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Kobayashi, Nozomu, Yoshiyuki Suimon, Koichi Miyamoto, and Kosuke Mitarai. "The cross-sectional stock return predictions via quantum neural network and tensor network." Quantum Machine Intelligence 5, no. 2 (2023). http://dx.doi.org/10.1007/s42484-023-00136-x.

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AbstractIn this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tenso

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Tariq Mahmood, Talab Hussain, and Maqsood Ahmed. "Quantum Computer Architecture: A Quantum Circuit-Based Approach Towards Quantum Neural Network." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 60, no. 2 (2023). http://dx.doi.org/10.53560/ppasa(60-2)668.

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According to recent research on the brain and cognition, the microtubule level activities in the brain are in accordance with the quantum mechanical concepts. Consciousness is the emergent phenomenon of the brain’s subsystems and the quantum neural correlates. Based on the global work-space theory and traditional neural networks, investigations in machine consciousness and machine intelligence are reporting new techniques. In this study, a novel approach using circuit-based quantum neural network is proposed and simulated. This approach satisfies all the criteria of quantum computing and is te

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Zhang, Yulu, and Hua Lu. "Protecting security of quantum neural network with sampling checks." Frontiers in Physics 11 (June 21, 2023). http://dx.doi.org/10.3389/fphy.2023.1236828.

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With the development of quantum computing, the application of quantum neural networks will be more and more extensive, and its security will also face more challenges. Although quantum communication has high security, quantum neural networks may have many internal and external insecure factors in the process of information transmission, such as noise impact during the preparation of input quantum states, privacy disclosure during transmission, and external attacks on the network structure, which may cause major security incidents. Because of the possible insecurity factors of quantum neural ne

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Hashimoto, Koji, Yuji Hirono, Jun Maeda, and Jojiro Totsuka-Yoshinaka. "Neural network representation of quantum systems." Machine Learning: Science and Technology, September 30, 2024. http://dx.doi.org/10.1088/2632-2153/ad81ac.

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Abstract It has been proposed that random wide neural networks near Gaussian process are quantum field theories around Gaussian fixed points. In this paper, we provide a novel map with which a wide class of quantum mechanical systems can be cast into the form of a neural network with a statistical summation over network parameters. Our simple idea is to use the universal approximation theorem of neural networks to generate arbitrary paths in the Feynman’s path integral. The map can be applied to interacting quantum systems / field theories, even away from the Gaussian limit. Our findings bring

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Jaouni, Tareq, Sören Arlt, Carlos Ruiz-Gonzalez, Ebrahim Karimi, Xuemei Gu, and Mario Krenn. "Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments." Machine Learning: Science and Technology, February 5, 2024. http://dx.doi.org/10.1088/2632-2153/ad2628.

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Abstract Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI (XAI) technique called inception or deep dreaming, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we "invert" the neural network -- effectively asking how it imagines a quantum sy

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Wu, Sixuan, Yue Zhang, and Jian Li. "Quantum data parallelism in quantum neural networks." Physical Review Research 7, no. 1 (2025). https://doi.org/10.1103/physrevresearch.7.013177.

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Quantum neural networks hold promise for achieving lower generalization error bounds and enhanced computational efficiency in processing certain datasets. However, the integration of quantum superposition in data parallelism—a key aspect in the applications of classical neural network—has been largely unexplored in the context of quantum neural networks. Here, we demonstrate the effective application of quantum parallelism, via quantum superposition and entanglement, to achieve data parallelism in generic quantum neural network models. We address two classes of encoding schemes for the trainin

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Rebentrost, Patrick, Thomas R. Bromley, Christian Weedbrook, and Seth Lloyd. "Quantum Hopfield neural network." Physical Review A 98, no. 4 (2018). http://dx.doi.org/10.1103/physreva.98.042308.

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