Neural network based on QBP and its performance

Matsui, Nobuyuki; Kouda, Noriaki; Nishimura, Haruhiko

doi:10.1109/ijcnn.2000.861311

Cited by 27 publications

(11 citation statements)

References 3 publications

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“…Despite Shor's [49] remarkable success, quantum computation appears, to be much more amenable to computational intelligence-type problems rather than to traditional problems requiring exact, deterministic solutions. Numerous research publications have been produced combining QC and AI such as neural networks [50,51], decision trees [52], Bayesian networks [53], genetic algorithms [54], and learning DNF [55].…”

Section: Intelligent Systems Based On Quantum Computingmentioning

confidence: 99%

Quantum computing measurement and intelligence

Ezziane

2009

Int J of Quantum Chemistry

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ABSTRACT:One of the grand challenges in the nanoscopic computing era is guarantees of robustness. Robust computing system design is confronted with quantum physical, probabilistic, and even biological phenomena, and guaranteeing highreliability is much more difficult than ever before. Scaling devices down to the level of single electron operation will bring forth new challenges due to probabilistic effects and uncertainty in guaranteeing "zero-one" based computing. Minuscule devices imply billions of devices on a single chip, which may help mitigate the challenge of uncertainty by replication and redundancy. However, such device densities will create a design and validation nightmare with the sheer scale. The questions that confront computer engineers regarding the current status of nanocomputing material and the reliability of systems built from such minuscule devices are difficult to articulate and answer. This article illustrates and discusses two types of quantum algorithms as follows: (1) a simple quantum algorithm and (2) a quantum search algorithm. This article also presents a review of recent advances in quantum computing and intelligence and presents major achievements and obstacles for researchers in the near future.

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Section: Intelligent Systems Based On Quantum Computingmentioning

confidence: 99%

Quantum computing measurement and intelligence

Ezziane

2009

Int J of Quantum Chemistry

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“…Despite Shor's 49 remarkable success, quantum computation appears, to be much more amenable to computational intelligence‐type problems rather than to traditional problems requiring exact, deterministic solutions. Numerous research publications have been produced combining QC and AI such as neural networks 50, 51, decision trees 52, Bayesian networks 53, genetic algorithms 54, and learning DNF 55.…”

Section: Quantum Algorithmsmentioning

confidence: 99%

Binding of chlorpheniramine maleate to pharmacologically important alginic acid, carboxymethylcellulose, κ‐carageenan, and ι‐carrageenan as studied by diafiltration

Moreno‐Villoslada

Oyarzún

Miranda

et al. 2005

J of Applied Polymer Sci

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ABSTRACT:The binding of cationic chlorpheniramine maleate (CPM) to the anionic water-soluble polymers (WSPs) alginic acid, carboxymethylcellulose, -carageenan, and -carageenan was evaluated by diafiltration at pH 7.5 and in the absence and presence of 0.13M NaCl. CPM interacted with all of the WSPs when no NaCl was present in the solution, with charge-related formation constants of around 700 M Ϫ1 for all of the polymers, whereas the interactions were cleaved in the presence of 0.13M NaCl, indicating interactions of an electrostatic nature screened by the single electrolyte.

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“…ZHU Da-qi et al applied this model to fault diagnosis for photovoltaic radar electronic equipment, and acquired the satisfied results [7]. In 2000, Matsui et al proposed a quantum neural networks model based on the single bit rotation gate and the 2-bit controlled-NOT gate, presented the algorithm of model, and investigated its performance on solving the 4-bit parity check problem and the function approximation problem [8]. In this model, the input is based on quantum bits (qubits).…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Neural Networks with Application

Li¹

2015

OJAppS

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In this paper, a novel neural network is proposed based on quantum rotation gate and controlled-NOT gate. Both the input layer and the hide layer are quantum-inspired neurons. The input is given by qubits, and the output is the probability of qubit in the state 1. By employing the gradient descent method, a training algorithm is introduced. The experimental results show that this model is superior to the common BP networks.

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Neural network based on QBP and its performance

Cited by 27 publications

References 3 publications

Quantum computing measurement and intelligence

Quantum computing measurement and intelligence

Binding of chlorpheniramine maleate to pharmacologically important alginic acid, carboxymethylcellulose, κ‐carageenan, and ι‐carrageenan as studied by diafiltration

Quantum-Inspired Neural Networks with Application

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