Article 4117

Title of the article



Volchikhin Vladimir Ivanovich, Doctor of engineering sciences, professor, President of Penza State University (40 Krasnaya street, Penza, Russia),
Ivanov Aleksandr Ivanovich, Doctor of engineering sciences, associate professor, head of the laboratory of biometric and neural network technologies, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia),
Bezyaev Aleksandr Viktorovich, Candidate of engineering sciences, leading specialist of STC “Atlas” branch in Penza (9 Sovetskaya street, Penza, Russia),
Elfimov Andrey Vladimirovich, Engineer, “Argus” branch of Penza  Research Electrotechnical Institute (69a Pobedi avenue, Penza, Russia),
Yunin Aleksey Petrovich, Head of research department, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia),

Index UDK

 519.2, 612.087, 621.319.7




Background. The aim of the work is to estimate the benefit of using the program support, the effects of quantum superposition of the neural network’s output.
Materials and methods. To observe the effects of quantum superposition the authors used a method consisting in blurring of deterministic data of one “Friend” image example by "white noise" generator data. For this reason, a part of neural network convereter’s output discharges are unstable.
Results. It is proposed to use codes to detect and correct errors, storing syndromes of related errors in the form hash functions from the true state of the corrected code.
Conclusions. The implementation of the self-correcting code with its length of 256 bits, capable of detecting and correcting 12 errors, gives a benefit in reduction of the computation by 20 orders of magnitude. This benefit is due to the use of effects of the support of quantum superposition having 12 q-bits of length.

Key words

quantum superposition, neural network “biometrics – code” converter, discrete spectrum of output states, statistical analysis on small samples.

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