mireabulletinRussian Technological JournalRussian Technological Journal2782-32102500-316XRTU MIREA10.32362/2500-316X-2023-11-5-19-33mireabulletin-760Research ArticleИНФОРМАЦИОННЫЕ СИСТЕМЫ. ИНФОРМАТИКА. ПРОБЛЕМЫ ИНФОРМАЦИОННОЙ БЕЗОПАСНОСТИINFORMATION SYSTEMS. COMPUTER SCIENCES. ISSUES OF INFORMATION SECURITYГеометрические свойства квантовой запутанности и машинное обучениеGeometric properties of quantum entanglement and machine learninghttps://orcid.org/0000-0003-4237-0491ЗуевС. В.ZuevS. V.

Зуев Сергей Валентинович, к.ф.-м.н., доцент, кафедра компьютерной инженерии и моделирования Физико-технического института

295007, Республика Крым, Симферополь, пр-т Вернадского, д. 4

Scopus Author ID 57292501000

ResearcherID U-1055-2017

Sergei V. Zuev, Cand. Sci. (Phys.-Math.), Associate Professor, Department of Computer Engineering and Modelling, Institute of Physics and Technologies

4, Vernadskogo pr., Simferopol, 295007 Republic of Crimea

Scopus Author ID 57292501000

ResearcherID U-1055-2017

zuevsv@cfuv.ruКрымский федеральный университет им. В.И. ВернадскогоРоссияV.I. Vernadsky Crimean Federal UniversityRussian Federation2023051020231151933Copyright © Зуев С.В., 20232023Зуев С.В.Zuev S.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.rtj-mirea.ru/jour/article/view/760Цели

Цели. Быстрая классификация данных на основе имеющихся в них закономерностей является одним из главных вопросов для построения систем адаптивного искусственного интеллекта. Цель работы – предложить и верифицировать метод такой классификации на основе представления данных в виде квантового состояния или (альтернативно) в виде геометрического объекта в пространстве, свойства которого позволяют производить машинное обучение «на лету» (онлайн-обучение).

Методы

Методы. В работе используется фейнмановский формализм для представления квантовых состояний и операций над ними, представление квантовых вычислений в виде квантовых схем, геометрические преобразования, топологическая классификация, а также методы классического и квантового машинного обучения. В качестве инструмента разработки использовался язык программирования Python, средства оптимизации для машинного обучения взяты из модуля SciPy. Размеченные данные для анализа взяты из открытых источников. Препроцессинг данных произведен методом отображения признаков в числовые векторы, затем применен метод приведения данных к нужной размерности и далее – отображение данных в квантовое состояние. Используется собственный эмулятор квантовых вычислений (находится в открытом доступе).

Результаты

Результаты. Результаты вычислительных экспериментов выявили способность очень простых квантовых схем к классификации данных без оптимизации. Получены сравнительные показатели качества классификации без использования оптимизации, а также с ее использованием. Эксперименты проведены с различными датасетами и для различных значений размерности пространств признаков. Работоспособность предложенных в работе моделей и методов машинного обучения, а также методов их объединения в сетевые структуры, подтверждена практически.

Выводы

Выводы. Предложенный метод машинного обучения и построения квантовых нейронных сетей может быть применен для создания систем адаптивного искусственного интеллекта в составе модуля онлайн-обучения. Отсутствие оптимизации в процессе онлайн-обучения позволяет применять его в потоке данных, т.е., адаптироваться к изменениям среды. Разработанное алгоритмическое обеспечение не требует наличия квантовых компьютеров и может быть применено при разработке программного обеспечения систем искусственного интеллекта на языке Python в качестве импортируемых модулей.

Objectives

Objectives. Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. This paper aims to propose and verify a method of such analysis based on the representation of data in the form of a quantum state, or, alternatively, in the form of a geometric object in a space allowing online machine learning.

Methods

Methods. This paper uses Feynman formalism to represent quantum states and operations on them, the representation of quantum computing in the form of quantum circuits, geometric transformations, topological classification, as well as methods of classical and quantum machine learning. The Python programming language is used as a development tool. Optimization tools for machine learning are taken from the SciPy module. The datasets for analysis are taken from open sources. Data preprocessing was performed by the method of mapping features into numerical vectors, then the method of bringing the data to the desired dimension was applied. The data was then displayed in a quantum state. A proprietary quantum computing emulator is used (it is in the public domain).

Results

Results. The results of computational experiments revealed the ability of very simple quantum circuits to classify data without optimization. Comparative indicators of classification quality are obtained without the use of optimization, as well as with its use. Experiments were carried out with different datasets and for different values of the dimension of feature spaces. The efficiency of the models and methods of machine learning proposed in the work, as well as methods of combining them into network structures, is practically confirmed.

Conclusions

Conclusions. The proposed method of machine learning and the model of quantum neural networks can be used to create adaptive artificial intelligence systems as part of an online learning module. Free online optimization learning process allows it to be applied in data streaming, that is, adapting to changes in the environment. The developed software does not require quantum computers and can be used in the development of artificial intelligence systems in Python as imported modules.

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The authors declare that there are no conflicts of interest present.