<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">njes</journal-id><journal-title-group><journal-title xml:lang="ru">Экономические системы</journal-title><trans-title-group xml:lang="en"><trans-title>Economic Systems</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2309-2076</issn><publisher><publisher-name>Дашков и К</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.29030/2309-2076-2025-18-4-39-57</article-id><article-id custom-type="elpub" pub-id-type="custom">njes-18</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ЦИФРОВАЯ ЭКОНОМИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>DIGITAL ECONOMY</subject></subj-group></article-categories><title-group><article-title>Преимущества использования квантовых вычислений, интегрированных в технологии ИИ, предсказательной аналитике и маркетинге</article-title><trans-title-group xml:lang="en"><trans-title>Advantages of using quantum computing integrated into AI technologies in predictive analytics and marketing</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Петухова</surname><given-names>А. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Petukhova</surname><given-names>A. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Анна Сергеевна Петухова – аспирант</p><p>Москва</p></bio><bio xml:lang="en"><p>Anna S. Petukhova – Ph.D. student</p><p>Moscow</p></bio><email xlink:type="simple">aspetukhova@hse.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Высшая школа бизнеса, Национальный исследовательский университет «Высшая школа экономики»<country>Россия</country></aff><aff xml:lang="en">Higher School of Business, National Research University «Higher School of Economics»<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>09</day><month>03</month><year>2025</year></pub-date><volume>18</volume><issue>4</issue><fpage>39</fpage><lpage>57</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Петухова А.С., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Петухова А.С.</copyright-holder><copyright-holder xml:lang="en">Petukhova A.S.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.njes.ru/jour/article/view/18">https://www.njes.ru/jour/article/view/18</self-uri><abstract><p>В связи со стремительным развитием технологий искусственного интеллекта (ИИ) специалисты по маркетингу сталкиваются с меняющимся ландшафтом, который требует всестороннего понимания потенциала и проблем применения ИИ. Квантовые вычислительные процессы – основа будущего развития предсказательной аналитики, так как их способность одновременно давать множество вариантов событий с разной вариацией их наступления позволяет достигать достоверности до 99,75%1 даже в условиях иррациональности. 2</p><p>Актуальность исследования связана с государственным приоритетом по развитию квантовых технологий, а также с достижениями технологий, в частности, удобством применения и точностью получаемых данных.</p><p>Исследование направлено на определение наиболее подходящей под задачи маркетинговой аналитики совокупности технологий ИИ с применением квантовых вычислительных процессов в целях повышения точности прогнозирования потребительского поведения и рыночных механизмов в долгосрочной перспективе.</p><p>Методологической базой исследования послужили научные работы в области маркетинговых стратегий, ИИ, в также описания основ действия квантовых компьютеров, практические работы ведущих центров по внедрению квантовых вычислений в бизнес-процессы. Теоретическая основа – библиометрический анализ, теория количественных методов, теория инноваций.</p><p>В статье представлен анализ литературы и систематизация различных программных квантовых продуктов, сопоставлены практические результаты в области применения квантовых вычислений в предсказательной аналитике в области маркетинга. Дана критическая оценка преимуществ внедрения технологий ИИ в связке с квантовымивычислительными процессами в формирование маркетинговых стратегий, включая долгосрочные прогнозы потребительского поведения и изменения рыночных механизмов.</p><p>Результатом исследования является фреймворк по внедрению технологий ИИ в связке с квантовыми вычислительными процессами в формирование маркетинговых стратегий.</p><p>Информационную базу составили 160 статей, написанных в области применения предсказательной аналитики, основанной на ИИ, в целях маркетинга, а также результаты оценки 26 кейсов применения квантовых вычислений в бизнес-процессах как в России, так и за рубежом.</p></abstract><trans-abstract xml:lang="en"><p>Due to the rapid development of artificial intelligence (AI) technologies, marketing professionals are faced with a changing landscape that requires a comprehensive understanding of the potential and challenges of AI applications. Quantum computing processes are the basis for the future development of predictive analytics, as their ability to simultaneously provide many variants of events with different variations of their occurrence allows achieving reliability of up to 99.75%, even in conditions of irrationality.</p><p>The relevance of the research is related to the state priority for the development of quantum technologies, as well as technological advances, in particular, ease of use and accuracy of the data obtained.</p><p>The research is aimed at determining the combination of AI technologies with the use of quantum computing processes that is most suitable for the tasks of marketing analytics in order to increase the accuracy of forecasting consumer behavior and market mechanisms in the long term.</p><p>The methodological basis of the research was scientific work in the field of marketing strategies, artificial intelligence, as well as descriptions of the basics of quantum computers, practical work of leading centers for the implementation of quantum computing in business processes. The theoretical basis is bibliometric analysis, theory of quantitative methods, theory of innovation. For the purposes of the research, literature was analyzed and various AI software products were systematized, a list of cases was provided and practical results in the field of quantum computing application in predictive analytics in the field of marketing were compared. Their impact on such marketing components as market analysis and forecasting, audience sentiment analysis, and offer personalization is assessed.</p><p>The information base consisted of a study of 160 articles written in the field of predictive analytics based on AI for marketing purposes, as well as an assessment of 26 cases of the use of quantum computing in business processes both in Russia and abroad.</p><p>The key focus of the research is a critical assessment of the benefits of implementing artificial intelligence technologies in conjunction with quantum computing processes in shaping marketing strategies, including long—term forecasts of consumer behavior and changes in market mechanisms.</p><p>The result of the research is a framework for the introduction of artificial intelligence technologies in conjunction with quantum computing processes in the formation of marketing strategies.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>маркетинг</kwd><kwd>квантовые вычисления</kwd><kwd>искусственный интеллект</kwd><kwd>генеративный искусственный интеллект</kwd><kwd>маркетинговый анализ</kwd><kwd>предсказательная аналитика</kwd></kwd-group><kwd-group xml:lang="en"><kwd>мarketing</kwd><kwd>quantum computing</kwd><kwd>artificial intelligence</kwd><kwd>generative artificial intelligence</kwd><kwd>marketing analysis</kwd><kwd>predictive analytics</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Chinnaraju A. Quantum Computing in Consumer Behavior: A Theoretical Framework for Market Prediction and Decision Analytics // International Journal of Advanced Research in Science Communication and Technology. 2025. No. 5 (2). February. Р. 339–371.</mixed-citation><mixed-citation xml:lang="en">Chinnaraju A. Quantum Computing in Consumer Behavior: A Theoretical Framework for Market Prediction and Decision Analytics. International Journal of Advanced Research in Science Communication and Technology. 2025;(5(2)):339-371.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Yearsley J., Pothos E. Quantum models of cognition and decision // Psychological Review. 2014. No. 121 (4). Р. 629–654.</mixed-citation><mixed-citation xml:lang="en">Yearsley J., Pothos E. Quantum models of cognition and decision. Psychological Review. 2014;(121(4)):629-654.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Aerts D., Sozzo S. Quantum structure in cognition and economics // Journal of Mathematical Psychology. 2016. No. 75. Р. 1–15.</mixed-citation><mixed-citation xml:lang="en">Aerts D., Sozzo S. Quantum structure in cognition and economics. Journal of Mathematical Psychology. 2016;(75):1-15.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Trivedi L.T., Thorat C.V. Exploring Quantum Machine Learning Algorithms for Enhanced Data Analysis // International Journal on Recent and Innovation Trends in Computing and Communication. 2023. Vol. 11. Iss. 10. URL: https://doi.org/10.17762/ijritcc.v11i10.8813 (accessed: 05.05.2025).</mixed-citation><mixed-citation xml:lang="en">Trivedi L.T., Thorat C.V. Exploring Quantum Machine Learning Algorithms for Enhanced Data Analysis. International Journal on Recent and Innovation Trends in Computing and Communication. 2023;11(10). URL: https://doi.org/10.17762/ijritcc.v11i10.8813.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Носова М.Г. Математическое моделирование социально-экономических процессов методами теории массового обслуживания // Реестр новых научных направлений. Москва, 2018.</mixed-citation><mixed-citation xml:lang="en">Nosova M.G. Mathematical modeling of socio-economic processes using queueing theory methods. Register of new scientific directions. Moscow, 2018. P. 44. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Bruza P., Wang Z., Busemeyer J. Quantum cognition: A new theoretical approach // Trends in Cognitive Sciences. 2015. No. 19 (7). Р. 383–393.</mixed-citation><mixed-citation xml:lang="en">Bruza P., Wang Z., Busemeyer J. Quantum cognition: A new theoretical approach. Trends in Cognitive Sciences. 2015;(19(7)):383-393.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Иночкин А.Ю., Луньков А.Д., Сидоров С.П. Анализ средствами квантильной регрессии направленных взаимосвязей между доходностями российских компаний // Компьютерные науки и информационные технологии: материалы Международной научной конференции (Саратов, 2–3 июля 2018 г.). Саратов : ИЦ «Наука», 2018. С. 164–167.</mixed-citation><mixed-citation xml:lang="en">Inochkin A.Yu., Lunkov A.D., Sidorov S.P. Analysis of Directional Relationships between Russian Companies’ Profitability Using Quantile Regression. Computer Science and Information Technology: Proceedings of the International Scientific Conference (Saratov, July 2–3, 2018). Saratov : Scientific Center «Science», 2018. Р. 164–167. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Zhang Q., Sornette D. Early Warning Signals of Financial Crises with Multi-Scale Quantile</mixed-citation><mixed-citation xml:lang="en">Zhang Q., Sornette D. Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities. Swiss Finance Institute Research Paper Series. 2015. Р. 15–43. URL: https://doi.org/10.1371/journal.pone.0165819.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Regressions of Log-Periodic Power Law Singularities // Swiss Finance Institute Research Paper Series. 2015. Р. 15–43. URL: https://doi.org/10.1371/journal.pone.0165819 (accessed: 05.05.2025).</mixed-citation><mixed-citation xml:lang="en">Bottou L., et al. Advanced robust and nonparametric methods in efficiency analysis. Journal of Productivity Analysis. 2013;(39(2)):141-159</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Bottou L., et al. Advanced robust and nonparametric methods in efficiency analysis // Journal of Productivity Analysis. 2013. No. 39 (2). Р. 141–159.</mixed-citation><mixed-citation xml:lang="en">Lionetti M. Quantum Marketing: Suggestions from Physics for a New Marketing Model part II. Journal: Marketing Exchanges. 2022;(4):54-59.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Lionetti M. Quantum Marketing: Suggestions from Physics for a New Marketing Modelpart II // Journal: Marketing Exchanges. 2022. No. 4. Р. 54–59.</mixed-citation><mixed-citation xml:lang="en">Tariq M.U. Quantum Algorithms and Predictive Analytics: Revolutionizing Consumer Insight Chapter. 2025. URL: https://www.researchgate.net/publication/387462232_Quantum_Algorithms_and_Predictive_Analytics_Revolutionizing_Consumer_Insight.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Tariq M.U. Quantum Algorithms and Predictive Analytics: Revolutionizing Consumer Insight Chapter. 2025. URL: https://www.researchgate.net/publication/387462232_Quantum_Algorithms_and_Predictive_Analytics_Revolutionizing_Consumer_Insight (accessed: 05.05.2025). 12. Taiwo I., Ogunbajo A. Quantum computing – Enhanced AI systems for advanced business intelligence applications // International Journal of Science and Research Archive. 2005. No. 14 (1). Р. 1839–1847.</mixed-citation><mixed-citation xml:lang="en">Taiwo I., Ogunbajo A. Quantum computing – Enhanced AI systems for advanced business intelligence applications. International Journal of Science and Research Archive. 2005;(14(1)):1839-1847.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Matsakos T., Nield S. Quantum Monte Carlo simulations for financial risk analytics: scenario generation for equity, rate, and credit risk factors. URL: https://doi.org/10.22331/q-2024-04-04-1306 (accessed: 05.05.2025).</mixed-citation><mixed-citation xml:lang="en">Matsakos T., Nield S. Quantum Monte Carlo simulations for financial risk analytics: scenario generation for equity, rate, and credit risk factors. URL: https://doi.org/10.22331/q-2024-04-04-1306.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Ganguli D., et al. Predictability and Surprise in Large Generative Models. Association for Computing Machinery. 2022. URL: https://dl.acm.org/doi/abs/10.1145/3531146.3533229 (accessed: 05.05.2025).</mixed-citation><mixed-citation xml:lang="en">Ganguli D., et al. Predictability and Surprise in Large Generative Models. Association for Computing Machinery. 2022. URL: https://dl.acm.org/doi/abs/10.1145/3531146.3533229.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Koenker R. Quantile regression. NY : Cambridge University Press, 2005.</mixed-citation><mixed-citation xml:lang="en">Koenker R. Quantile regression. NY: Cambridge University Press, 2005.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Shahmandi M., Wilson P., Thelwall M. A Bayesian hurdle quantile regression model for citation analysis with mass points at lower values // Quantitative Science Studies. 2021. No. 2 (3). Р. 912–931. URL: https://doi.org/10.1162/qss_a_00147 (accessed: 05.05.2025).</mixed-citation><mixed-citation xml:lang="en">Shahmandi M., Wilson P., Thelwall M. A Bayesian hurdle quantile regression model for citation analysis with mass points at lower values. Quantitative Science Studies. 2021;(2(3)):912-931. URL: https://doi.org/10.1162/qss_a_00147.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Friendly M., Meyer D. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. CRC Press, 2016.</mixed-citation><mixed-citation xml:lang="en">Friendly M., Meyer D. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. CRC Press, 2016.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Coxe S., West S. G., Aiken L.S. The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives // Journal of Personality Assessment. 2009. No. 91 (2). Р. 121–136.</mixed-citation><mixed-citation xml:lang="en">Coxe S., West S. G., Aiken L. S. The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives. Journal of Personality Assessment. 2009;(91(2)):121-136.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Постникова Е. Квантильная регрессия. Новосибирск : НГУ, 2000.</mixed-citation><mixed-citation xml:lang="en">Postnikova E. Quantile Regression. Novosibirsk: NSU, 2000. (In Russ.).</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Chen L., et al. Quantum blockchain for secure transactions // Nature Quantum Information. 2023. No. 9 (1). Р. 1–12.</mixed-citation><mixed-citation xml:lang="en">Chen L., et al. Quantum blockchain for secure transactions. Nature Quantum Information. 2023;(9(1)):1-12.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Wang Y., et al. Quantum cloud computing for market analytics. Quantum Reports. 2025. No. 7 (1). Р. 1–15.</mixed-citation><mixed-citation xml:lang="en">Wang Y., et al. Quantum cloud computing for market analytics. Quantum Reports. 2025;(7(1)):61-15.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
