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Backpropagation Training in Adaptive Quantum Networks

Christopher Altman, Romàn R. Zapatrin
2009 International Journal of Theoretical Physics  

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2009 pre-print arXiv:0903.4416v1 fatcat:voq3rw6z4zao5hqwcgg7rfcayy
Quantum parallelism facilitates simultaneous training and revision of the system within this coherent state space, resulting in accelerated convergence to a stable network attractor under consequent iteration  ...  reconfiguration of whole-network quantum structure.  ...  Both CTA and RRZ acknowledge the hospitality of the organizers, and particularly Jaros law Pykacz. RRZ was supported under auspices of research grant RFFI 07-06-00119.  ... 
doi:10.1007/s10773-009-0103-1 fatcat:rwfri3w3sjcobn7ftsylcsny4y
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Christopher Altman, Romàn R. Zapatrin. "Backpropagation Training in Adaptive Quantum Networks." International Journal of Theoretical Physics 49.12 (2009) 2991-2997

The quest for a Quantum Neural Network

Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
2014 Quantum Information Processing  

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2014 pre-print arXiv:1408.7005v1 fatcat:czhmdiqrnjf35h5v2cdnlisbh4
An outlook on possible ways forward is given, emphasizing the idea of Open Quantum Neural Networks based on dissipative quantum computing.  ...  It is found that none of the proposals for a potential QNN model fully exploits both the advantages of quantum physics and computing in neural networks.  ...  The debate on quantum approaches to neural networks emerged in the wake of a booming research field of quantum computing two decades ago.  ... 
doi:10.1007/s11128-014-0809-8 fatcat:qdujedkxzfhi3mgrsjvs43seeq
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Maria Schuld, Ilya Sinayskiy, Francesco Petruccione. "The quest for a Quantum Neural Network." Quantum Information Processing 13.11 (2014) 2567-2586

Quantum-Inspired Neural Network with Quantum Weights and Real Weights

Fuhua Shang
2015 Open Journal of Applied Sciences  

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To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper.  ...  In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights.  ...  Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 61170132), the Scientific research and technology development project of CNPC (2013E-3809), and the  ... 
doi:10.4236/ojapps.2015.510060 fatcat:32hhdxio2zcgzmfv3h3g6oa43i
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Fuhua Shang. "Quantum-Inspired Neural Network with Quantum Weights and Real Weights." Open Journal of Applied Sciences 05.10 (2015) 609-617

A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks [article]

Tariq M. Khan, Antonio Robles-Kelly
2020 arXiv   pre-print

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Here, we depart from the classical perceptron and the elemental operations on quantum bits, i.e. qubits, so as to formulate the problem in terms of quantum perceptrons.  ...  Finally, but not least, the developments here are quite general in nature since the approach presented here can also be used for quantum-inspired neural networks implemented on conventional computers.  ...  Quantum-inspired neural networks (QiNNs) and Quantum computing-based neural networks have been shown to be more effective and efficient as compared to conventional ANNs [21] .  ... 
arXiv:2009.13264v1 fatcat:teliwerjfjdideqgnci65odyxm
Citation

Tariq M. Khan, Antonio Robles-Kelly. "A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks." arXiv (2020)

Quantum Competition Network Model Based On Quantum Entanglement

Yanhua Zhong, Changqing Yuan
2012 Journal of Computers  

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Simulation results show that a quantum competitive learning algorithm in the learning rate and convergence rate is far better than the basic competitive artificial neural network.  ...  This paper proposes a quantum competition neural network model compared to its classical counterpart from the relative parts of the complex system localizing operation without changing the perspective  ...  Since then, the neural network research returns to low.  ... 
doi:10.4304/jcp.7.9.2312-2317 fatcat:j2x2ech5cjed7etqmt5npzmhce
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Yanhua Zhong, Changqing Yuan. "Quantum Competition Network Model Based On Quantum Entanglement." Journal of Computers 7.9 (2012)

Forecasting Financial Risk using Quantum Neural Networks

Abdelali El Bouchti, Younes Tribis, Tarik Nahhal, Chafik Okar
2019 Journal of Information Security Research  

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In the current paper, we present quantum neural networks (QNNs) and a method of training which is well in quantum system and is improved with momentum accession and parameter self adaptive algorithm, and  ...  We apply this model to the empirical research on the financial risk forecasting of some Moroccan companies. Then we will compare the findings with the standard artificial neural network (ANNs).  ...  This paper propose a new training method for a simple QNN and it describes such a quantum neural network (QNN) and how training could be done on it.  ... 
doi:10.6025/jisr/2019/10/3/97-104 fatcat:srcajul4bferlonrtd4j23a4qi
Citation

Abdelali El Bouchti, Younes Tribis, Tarik Nahhal, Chafik Okar. "Forecasting Financial Risk using Quantum Neural Networks." Journal of Information Security Research 10.3 (2019) 97

Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation [article]

Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi, Elizabeth Behrman
2020 arXiv   pre-print

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Qubits or bi-level quantum bits often describe quantum neural network models.  ...  Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination.  ...  It is worth noting that the qutrit based quantum neural network provides faster convergence compared to the classical neural networks.  ... 
arXiv:2009.06767v1 fatcat:n5jjndkgwrgnrppc3qmlnnhc2y
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Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi, Elizabeth Behrman. "Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation." arXiv (2020)

Infinite Neural Network Quantum States [article]

Di Luo, James Halverson
2021 arXiv   pre-print

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A general framework is developed for studying the gradient descent dynamics of neural network quantum states (NNQS), using a quantum state neural tangent kernel (QS-NTK).  ...  We study infinite limits of neural network quantum states (∞-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics.  ...  It also offers practical guidance for choosing neural network architectures: convergence rates during training depend on the spectrum of the QS-NTK, evaluated on the training data.  ... 
arXiv:2112.00723v1 fatcat:yhydlginr5hchicdicaljz6asq
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Di Luo, James Halverson. "Infinite Neural Network Quantum States." arXiv (2021)

Reinforcement Learning with Deep Quantum Neural Networks

Wei Hu, James Hu
2019 Journal of Quantum Information Science  

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Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment.  ...  Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards  ...  Neural networks are the most versatile ML technique and as such, it has been a long-time desire and challenge to create neural networks on quantum computers.  ... 
doi:10.4236/jqis.2019.91001 fatcat:jls5a6knfbcftgig4q3cirjhru
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Wei Hu, James Hu. "Reinforcement Learning with Deep Quantum Neural Networks." Journal of Quantum Information Science 09.01 (2019) 1-14

Application of quantum genetic optimization of LVQ neural network in smart city traffic network prediction

Fuquan Zhang, Tsu-Yang Wu, Yiou Wang, Rui Xiong, Gangyi Ding, Peng Mei, Laiyang Liu
2020 IEEE Access  

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Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial  ...  Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed.  ...  properties of the quantum.  ... 
doi:10.1109/access.2020.2999608 fatcat:emblvim7yfcftbon3akva37ija
DOAJ
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Fuquan Zhang, Tsu-Yang Wu, Yiou Wang, Rui Xiong, Gangyi Ding, Peng Mei, Laiyang Liu. "Application of quantum genetic optimization of LVQ neural network in smart city traffic network prediction." IEEE Access (2020) 1-1

Design Space Exploration of Hybrid Quantum–Classical Neural Networks

Muhammad Kashif, Saif Al-Kuwari
2021 Electronics  

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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  ...  However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear.  ...  We observed that the accuracy improvement rate and model convergence are better in all hybrid variants for the majority of the experiments, and hence it is safe to say that when the amount of data is increased  ... 
doi:10.3390/electronics10232980 fatcat:pgxxq3vfsjb6pmjhnj65mxrwqe
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Muhammad Kashif, Saif Al-Kuwari. "Design Space Exploration of Hybrid Quantum–Classical Neural Networks." Electronics 10.23 (2021) 2980

A New Method of Image Compression Based on Quantum Neural Network

Huifang Li, Mo Li
2010 2010 International Conference of Information Science and Management Engineering  

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Since the initial weights of neural networks were slow convergence, we use Genetic Algorithm (GA) to optimize the neural network weights, and present a mechanism called clamping to improve the genetic  ...  In this paper we combine with quantum neural networks and image compression using Quantum Gates as the basic unit of quantum computing neuron model, and establish a three layer Quantum Back Propagation  ...  It takes advantages of Neural Networks and quantum computing, and has high theoretic value and using potential on account for increasing the system processing ability and the learning self-adapt ability  ... 
doi:10.1109/isme.2010.242 fatcat:7zkanbcmrbe7ll7dq4rxez5dji
Citation

Huifang Li, Mo Li. "A New Method of Image Compression Based on Quantum Neural Network." 2010 International Conference of Information Science and Management Engineering (2010)

Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization

Baoyu Xu, Hongjun Zhang, Zhiteng Wang, Huaixiao Wang, Youliang Zhang
2015 Mathematical Problems in Engineering  

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The model and algorithm of BP neural network optimized by expanded multichain quantum optimization algorithm with super parallel and ultra-high speed are proposed based on the analysis of the research  ...  The method optimizes the structure of the neural network effectively and can overcome a series of problems existing in the BP neural network optimized by basic genetic algorithm such as slow convergence  ...  In order to further improve the efficiency of the BP neural network and overcome the shortage of it, a lot of research has been conducted.  ... 
doi:10.1155/2015/362150 fatcat:todbwuzzkjejhdnxvn3lqezdpm
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Baoyu Xu, Hongjun Zhang, Zhiteng Wang, Huaixiao Wang, Youliang Zhang. "Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization." Mathematical Problems in Engineering (2015) 1-11

An efficient quantum multiverse optimization algorithm for solving optimization problems

Samira Sarvari, Nor Fazlida Mohd. Sani, Zurina Mohd Hanapi, Mohd Taufik Abdullah
2020 International Journal of Advances in Applied Sciences  

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In this research, a new algorithm called quantum multiverse optimization (QMVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS.  ...  The QMVO algorithm determining the neural network weights based on the kernel function, which can improve the accuracy and then optimize the training part of the artificial neural network.  ...  In this research, the neural network algorithm is first improved by determining the neural network weights based on the kernel function, and then optimize the training part of the artificial neural network  ... 
doi:10.11591/ijaas.v9.i1.pp27-33 fatcat:rlnny5fwirhf5nftac47wcpg4q
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Samira Sarvari, Nor Fazlida Mohd. Sani, Zurina Mohd Hanapi, Mohd Taufik Abdullah. "An efficient quantum multiverse optimization algorithm for solving optimization problems." International Journal of Advances in Applied Sciences 9.1 (2020) 27

Neural-Network Quantum States: A Systematic Review [article]

David R. Vivas, Javier Madroñero, Victor Bucheli, Luis O. Gómez, John H. Reina
2022 arXiv   pre-print

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One particular research line of such field of study is the so-called Neural-Network Quantum States, a powerful variational computational methodology for the solution of quantum many-body systems that has  ...  Here, a systematic review of literature regarding Neural-Network Quantum States is presented.  ...  Initially, the generic search equations ("machine learning" AND "quantum physics") and ("Neural-Network Quantum States" OR "Neural network quantum states" OR "Neural quantum states") were considered as  ... 
arXiv:2204.12966v1 fatcat:p6m3h5fqwnfara5fskhgyzmama
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David R. Vivas, Javier Madroñero, Victor Bucheli, Luis O. Gómez, John H. Reina. "Neural-Network Quantum States: A Systematic Review." arXiv (2022)

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