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Quantum computing model of an artificial neuron with continuously valued input data [article]

Stefano Mangini, Francesco Tacchino, Dario Gerace, Chiara Macchiavello, Daniele Bajoni
2020 arXiv   pre-print
The elementary building block of a neural network is an artificial neuron, i.e. a computational unit performing simple mathematical operations on a set of data in the form of an input vector.  ...  Inf. 5, 26], which fully exploits the use of superposition states to encode binary valued input data, can be further generalized to accept continuous – instead of discrete-valued input vectors, without  ...  We acknowledge ENI S.p.A. for having partially contributed to this project through the Framework agreement with Universita' di Pavia. We acknowledge use of the IBM Quantum Experience for this work.  ... 
arXiv:2007.14288v1 fatcat:ybctmfyuu5gljkijj2pecbnz5m

Quantum inspiration to build a neural model based on the Day-Ahead Market of the Polish Power Exchange

Dariusz Ruciński
2021 Studia Informatica  
The article is an attempt of the methodological approach to the proposed quantum-inspired method of neural modeling of prices quoted on the Day-Ahead Market operating at TGE S.A.  ...  In the proposed quantum-inspired neural model it was assumed, inter alia, that it is composed of 12 parallel Perceptron ANNs with one hidden layer.  ...  weighted adder of input streams on the i-th neuron, 𝑤 ij -weight values between the i-th neuron of one layer and the j-th neuron of the next layer, 𝑢 -value of the i-th input signal; Quantum inspiration  ... 
doi:10.34739/si.2021.25.02 fatcat:7k5bxigr7rdn7ej5f7bqa327wa

On quantum neural networks [article]

Alexandr A. Ezhov
2021 arXiv   pre-print
The widespread in 2020 modern definition of a quantum neural network as a model or machine learning algorithm that combines the functions of quantum computing with artificial neural networks deprives quantum  ...  The early definition of a quantum neural network as a new field that combines the classical neurocomputing with quantum computing was rather vague and satisfactory in the 2000s.  ...  The fact is that in the future, an artificial neural network worked not with bits, but with real, or even complex data.  ... 
arXiv:2104.07106v1 fatcat:rl4rdkel4bdbzfq6zyktnkkee4

Artificial neural network inspired by quantum computing solutions using the movement model of the PR-02 robot

Jerzy Tchórzewski, Dariusz Ruciński, Przemysław Domański, R. Nawrowski, J. Lorenc, Z. Nadolny, A. Tomczewski, J. Jajczyk, L. Kasprzyk, A. Bugała, K. Budnik
2018 ITM Web of Conferences  
The application of the neural model instead of the analytic model allowed for obtaining the improvement of the trajectory of the PR-02 robot's arm movement, while the application of the quantum artificial  ...  The paper proposes a new method of quantum computing using control and systems theory as well as matrix-quantum computing.  ...  In the analytic model (a) In the neural model (b) In the quantum-neural model (c) -quantum summator of the i-th neuron in the k-th weights layer defined as the sum of weighted quantum values of input signals  ... 
doi:10.1051/itmconf/20181901007 fatcat:dvzyq4wzl5hgjeqgioyl44bxdm

An artificial neuron implemented on an actual quantum processor

Francesco Tacchino, Chiara Macchiavello, Dario Gerace, Daniele Bajoni
2019 npj Quantum Information  
We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives answers consistent with the expected results.  ...  Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt's "perceptron", but its long term practical applications may be hindered by the fast scaling up  ...  Some results are reported in Fig. 4 for a selected choice of input vectors, where the artificial neuron output is computed both with standard linear algebra and with a quantum circuit on a virtual and  ... 
doi:10.1038/s41534-019-0140-4 fatcat:c2jxjdsjsjfdveva4xe3q6qeuq

Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution

Srishti Sahni, Vaibhav Aggarwal, Ashish Khanna, Deepak Gupta, Siddhartha Bhattacharyya
2020 Journal of Information and Organizational Sciences  
Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best  ...  The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%.  ...  The computational unit of an artificial neural network is an artificial neuron which is modeled after a biological neuron.  ... 
doi:10.31341/jios.44.2.9 fatcat:fm7ro6bihbbjfnw3pobqmbw7g4

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  
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  ...  Finally, we combined the Genetic Algorithm with quantum neural networks to finish image compression. Through an experiment we can see the superiority of the improved algorithm.  ...  Firstly, the conceptions of quantum computing and neural computing are discussed in this paper. A QNN model which can input quantum and output quantum are created.  ... 
doi:10.1109/isme.2010.242 fatcat:7zkanbcmrbe7ll7dq4rxez5dji

Quantum neuromorphic computing [article]

Danijela Marković, Julie Grollier
2020 arXiv   pre-print
Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and compute.  ...  We discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.  ...  computing makes use of continuous quantum variables such as amplitude of the electromagnetic field, instead of discrete valued energy states of qubits, |0 and |1 .  ... 
arXiv:2006.15111v1 fatcat:dfi6au2f7reuxc6ty2afyma2dq

Single Flux Quantum Based Ultrahigh Speed Spiking Neuromorphic Processor Architecture [article]

Ali Bozbey, Mustafa Altay Karamuftuoglu, Sasan Razmkhah, Murat Ozbayoglu
2020 arXiv   pre-print
For the first time, we propose an ultrahigh speed, spiking neuromorphic processor architecture built upon single flux quantum (SFQ) based artificial neurons (JJ-Neuron).  ...  We experimentally demonstrate the soma part of the JJ-Neuron for various activation functions together with peripheral SFQ logic gates.  ...  Additionally, quantum computers are recently emerging and quantum supremacy is approaching 2 .  ... 
arXiv:1812.10354v3 fatcat:vcivrsruh5edbb55qmds2armmy

Quantum Neural Network States: A Brief Review of Methods and Applications

Zhih‐Ahn Jia, Biao Yi, Rui Zhai, Yu‐Chun Wu, Guang‐Can Guo, Guo‐Ping Guo
2019 Advanced Quantum Technologies  
In this work, we briefly review the progress of using the artificial neural network to build quantum many-body ansatz states.  ...  One of the main challenges of quantum many-body physics is that the dimensionality of the Hilbert space grows exponentially with the system size, which makes it extremely difficult to solve the Schrödinger  ...  He also acknowledges Liang Kong, Tian Lan for discussions during his stay in Yau mathematical science center of Tsinghua University, and he also benefits from the discussion with Giuseppe Carleo during  ... 
doi:10.1002/qute.201800077 fatcat:24f2hoscwnf23occgjvzhajn3y

A Natural Quantum Neural-Like Network

Mitja Perus, Horst Bischof, Tarik Hadzibeganovic
2007 NeuroQuantology  
An analogous natural, i.e. relatively non-artificial, quantum information processing system is developed in this article.  ...  Neuro-quantum interaction can regulate the "collapse"-readout of quantum computation results.  ...  information processing of input data.  ... 
doi:10.14704/nq.2005.3.3.69 fatcat:6nz7e6uyfbh3rc2sydklbgfjxm

Neuron Network Applied to Video Encoder [chapter]

Branko Markoski, Jovan etraji, Jasna Mihailovi, Branko Petrevski, Miroslava Petrevski, Borislav Obradovi, Zoran Miloevi, Zdravko Ivankovi, Dobrivoje Martinov, Duanka Tesanovi
2009 Micro Electronic and Mechanical Systems  
Artificial neuron, based on sum input and transfer function, computes output values.  ...  In computer sciences, neural network is an intertwined network of elements that processes data.  ...  Several chapters cover state-of-the-art microfabrication techniques and materials as enabling technologies for the microsystems.  ... 
doi:10.5772/7025 fatcat:sxck2zqp4rcstdrtsoemhe2n74

Neuro-, Genetic-, and Quantum Inspired Evolving Intelligent Systems

Nikola Kasabov
2006 2006 International Symposium on Evolving Fuzzy Systems  
This paper discusses opportunities and challenges for the creation of evolving artificial neural network (ANN) and more general -computational intelligence (CI) models inspired by principles at different  ...  With more genetic information available now, it becomes possible to integrate the gene and the neuronal information into neuro-genetic models and to use them for a better understanding of complex brain  ...  data, some times in an on-line mode, and through interaction with other models and the environment.  ... 
doi:10.1109/isefs.2006.251165 fatcat:g3xz7yejnndd3et2f7ug7b6uni

Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions [article]

Pranav Kairon, Siddhartha Bhattacharyya
2020 arXiv   pre-print
We provide a statistical comparison between two models , both of which perform better than the classical artificial neural networks.  ...  Hence we present a comparative analysis of continuous variable quantum neural networks (Variational circuits) and quantum backpropagating multi layer perceptron (QBMLP).  ...  Quantum computing [5] is an amalgamation of quantum physics and computer science.  ... 
arXiv:2008.07617v2 fatcat:fai3mx6yazdhlpfjwlzyaj7r7y

Learning Algebraic Models of Quantum Entanglement [article]

Hamza Jaffali, Luke Oeding
2019 arXiv   pre-print
We show how artificial neural networks can be trained to predict the entanglement type for quantum states.  ...  We give a thorough overview of supervised learning and network design for learning membership on algebraic varieties via deep neural networks.  ...  In 1943, McCulloch and Pitts proposed the first model of an artificial neuron [55] .  ... 
arXiv:1908.10247v1 fatcat:kjrblo7wt5gitniqewwomidkja
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