The Usage of Quantum Computer and Computing for High Performance in Machine Learning Methods

Year 2021, Volume: 14 Issue: 1, 47 - 56, 28.06.2021

Hasan Yetiş Mehmet Karaköse

https://doi.org/10.54525/tbbmd.845472

Cited By: 2

Abstract

Today, multi-core, multi-processor and graphic processor computers are used to meet the increasing performance needs of machine learning methods that require high computational power. Although these technologies enable the methods to accelerate, they are not sufficient to be implemented and developed in a practical way. In this study, the current and potential uses of quantum computing methods and quantum computers, which have just started to be applied in the laboratory environment, on machine learning methods are discussed. Quantum computing and its use in machine learning methods are discussed under three headings: quantum-inspired algorithms, hybrid quantum algorithms and quantum algorithms. Nowadays, quantum-inspired algorithms are widely applied that enable improvement of existing methods by taking advantage of quantum computing. Accelerations are achieved with hybrid quantum systems that have sub-threads made by quantum computers. For machine learning methods working entirely on quantum computers, it is necessary to eliminate the hardware disadvantages of quantum computers and to develop more quantum-based methods. In general, studies are of the opinion that by solving the memory problem of quantum computers, there will be ground-breaking developments in the field of machine learning.

Keywords

quantum computing, quantum computer, machine learning, high performance

Makine Öğrenmesi Yöntemlerinde Yüksek Başarım için Kuantum Bilgisayar ve Hesaplamanın Kullanımı

Abstract

Günümüzde yüksek hesaplama gücü gerektiren makine öğrenmesi yöntemlerinin artmakta olan performans ihtiyaçlarını karşılamak için çok çekirdekli, çok işlemcili ve grafik işlemcili bilgisayarlar kullanılmaktadır. Bu teknolojiler yöntemlerin hızlanmasını sağlasa da, pratik bir şekilde gerçekleştirilip geliştirilmesi için yeterli değillerdir. Bu çalışmada henüz laboratuvar ortamında uygulamaları başlanan kuantum bilgisayarların ve kuantum hesaplama yöntemlerinin makine öğrenmesi yöntemleri üzerinde mevcut ve potansiyel kullanımları ele alınmaktadır. Kuantum bilgisayar ve hesaplamanın makine öğrenmesi yöntemlerinde kullanımı, kuantum uyarlamalı algoritmalar, hibrit kuantum algoritmalar ve kuantum algoritmalar olmak üzere üç başlıkta ele alınmıştır. Günümüzde kuantum hesaplamanın avantajlarından yararlanarak mevcut yöntemlerin iyileştirmesini sağlayan kuantum uyarlamalı algoritmalar yaygın olarak uygulanmaktadır. Alt iş parçacıklarının kuantum bilgisayarlara yaptırılmasını amaçlayan hibrit kuantum sistemler ile hızlanmalar elde edilmektedir. Tamamen kuantum bilgisayarlarda çalışan makine öğrenmesi yöntemleri için ise kuantum bilgisayarların sahip oldukları donanımsal dezavantajların ortadan kaldırılması ve daha çok kuantum yöntem geliştirilmesi gerekmektedir. Genel olarak yapılan çalışmalar, kuantum bilgisayarların hafıza probleminin çözülmesi ile makine öğrenmesi alanında çığır açıcı gelişmeler olacağı görüşündedir.

Keywords

kuantum hesaplama, kuantum bilgisayar, makine öğrenmesi, yüksek başarım

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