Adiabatic Quantum Computing in Machine Learning
Başlık çevirisi mevcut değil.
- Tez No: 720621
- Danışmanlar: DR. JERZY DAJKA
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, İstatistik, Computer Engineering and Computer Science and Control, Statistics
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2019
- Dil: Çekçe
- Üniversite: Katowıce Sılesıa Unıversıtesı (unıversıty Of Sılesıa In Katowıce)
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
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Özet (Çeviri)
Inspired by the applications which are implemented on D-Wave quantum computers, we investigate the usage of adiabatic quantum computation and quantum annealing in machine learning models. To construct the background of research study, first of all, fundamentals of quantum mechanics/computation are introduced. Since the concept of quantum annealing is based on Ising Model, the transformation of some NP and P problems are found useful to discuss. For this purpose, the Ising formulations of graph coloring, Hamiltonian cycle and linear least square problems are discussed. Finally, as the main objective of study, training of Boltzmann machines by quantum annealing are compared with classical implementations. In the last part, I have benefited from some researches trained on D-Wave quantum annealing machines.
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