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Quantum inductive learning and quant...
~
Lukac, Martin.
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Quantum inductive learning and quantum logic synthesis.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Quantum inductive learning and quantum logic synthesis./
作者:
Lukac, Martin.
面頁冊數:
365 p.
附註:
Source: Dissertation Abstracts International, Volume: 70-07, Section: B, page: 4387.
Contained By:
Dissertation Abstracts International70-07B.
標題:
Engineering, Electronics and Electrical. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3368250
ISBN:
9781109295368
Quantum inductive learning and quantum logic synthesis.
Lukac, Martin.
Quantum inductive learning and quantum logic synthesis.
- 365 p.
Source: Dissertation Abstracts International, Volume: 70-07, Section: B, page: 4387.
Thesis (Ph.D.)--Portland State University, 2009.
Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified approach to QLS as Quantum Computing is still being discovered and novel applications are being identified.
ISBN: 9781109295368Subjects--Topical Terms:
626636
Engineering, Electronics and Electrical.
Quantum inductive learning and quantum logic synthesis.
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Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified approach to QLS as Quantum Computing is still being discovered and novel applications are being identified.
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The intent of this study is to experimentally explore principles of Quantum Logic Synthesis and its applications to Inductive Machine Learning.
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Based on algorithmic approach, I first design a Genetic Algorithm for Quantum Logic Synthesis that is used to prove and verify the methods proposed in this work.
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Based on results obtained from the evolutionary experimentation, I propose a fast, structure and cost based exhaustive search that is used for the design of a novel, least expensive universal family of quantum gates.
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The results from both the evolutionary and heuristic search are used to formulate an Inductive Learning Approach based on Quantum Logic Synthesis with the intended application being the humanoid behavioral robotics.
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The presented approach illustrates a successful algorithmic approach, where the search algorithm was able to invent/discover novel quantum circuits as well as novel principles in Quantum Logic Synthesis.
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