کاربرد الگوریتمهای یادگیری ماشین کوانتومی در علوم مالی

نوع مقاله : مقاله ترویجی

نویسندگان

1 دانشگاه صنعتی امیرکبیر

2 دانشگاه بین المللی امام رضا

3 گروه مدیریت واقتصاد، دانشکده علوم انسانی واجتماعی، دانشگاه گلستان، گلستان، ایران

چکیده

یادگیری ماشین، مجموعهای از الگوریتمها است که این امکان را برای کامپیوتر ایجاد میکند که الگوهای آماری در داده موجود را بدون برنامه نویسی صریح بیاموزد. یادگیری ماشین کاربردهای بسیاری در سرویسهای مالی از قبیل تشخیص کلاهبرداری، مدیریت سبد کالای خودکار، تخمین سطوح ریسک معاملات وغیره دارد. اما از انجا که اغلب این روشها نیاز به حجم زیادی از داده دارد، انجام این محاسبات بر روی کامپیوترهای کلاسیک میزان زیادی زمان و منابع محاسباتی نیاز دارد که ممکن است در عمل قابل پیادهسازی نباشند. کامپیوترهای کوانتومی به دلیل قدرت پردازش موازی میتوانند مسائل محاسباتی خاصی را بسیار سریعتر از الگوریتمهای همتای کلاسیک خود حل کنند. بنابراین میتوان به افزایش سرعت کوانتومی در الگوریتمهای یادگیری ماشین رسید. در این مقاله تاثیر یادگیری ماشین کوانتومی بر علوم مالی بررسی میشود. بطور مشخص پژوهش حاضر تلاش کرده است که آن دسته از مسائل محاسباتی در حوزه علوم مالی که استفاده از روش یادگیری ماشین کوانتومی نسبت به بهترین الگوریتمهای کلاسیک متناظر برتری ایجاد می‌کند را شناسایی نماید. همچنین امکان‌پذیری تحقق فیزیکی این روشها در کوتاه مدت نیز بررسی شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Application of Quantum Machine Learning Algorithms in Financial Sciences

نویسندگان [English]

  • Mohammad Mahdi Lotfi Heravi 1
  • Monireh Houshmand 2
  • Marzieh Asaadi 3
1 Amirkabir University of Technology
2 Imam Reza International University
3 Department of Management, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran
چکیده [English]

Machine learning is a set of algorithms that make it possible for a computer to learn statistical patterns in existing data without explicit programming. Machine learning has many applications in financial services such as fraud detection, automated portfolio management, estimating transaction risk levels, and more. But since most of these methods require large amounts of data, performing these calculations on classical computers requires a large amount of computational time and resources. Because of their parallel processing power, quantum computers can solve certain computational problems much faster than their classical counterpart algorithms. Therefore, it is possible to increase the quantum speed in machine learning algorithms. This article examines the impact of quantum machine learning on financial science. Specifically, the present study has tried to identify those computational problems in the field of financial sciences that the use of quantum machine learning method is superior to the best classical algorithms. The feasibility of physical realization of these methods in the short term has also been investigated.

کلیدواژه‌ها [English]

  • Machine Learning
  • Financial Sciences
  • Quantum Computing
  • Deep Learning
  • Reinforcement Learning
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