NDLI: Evolution of quantum algorithms

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Evolution of quantum algorithms

Content Provider	ACM Digital Library
Author	Spector, Lee
Abstract	Computer science will be radically transformed if ongoing efforts to build large-scale quantum computers eventually succeed and if the properties of these computers meet optimistic expectations. Nevertheless, computer scientists still lack a thorough understanding of the power of quantum computing, and it is not always clear how best to utilize the power that is understood. This dilemma exists because quantum algorithms are difficult to grasp and even more difficult to write. Despite large-scale international efforts, only a few important quantum algorithms are documented, leaving many essential questions about the potential of quantum algorithms unanswered. These unsolved problems are ideal challenges for the application of automatic programming technologies. Genetic programming techniques, in particular, have already produced several new quantum algorithms and it is reasonable to expect further discoveries in the future. These methods will help researchers to discover how additional practical problems can be solved using quantum computers, and they will also help to guide theoretical work on both the power and limits of quantum computing. This tutorial will provide an introduction to quantum computing and an introduction to the use of evolutionary computation for automatic quantum computer programming. No background in physics or in evolutionary computation will be assumed. While the primary focus of the tutorial will be on general concepts, specific results will also be presented, including human-competitive results produced by genetic programming. Follow-up material is available from the presenter's book, Automatic Quantum Computer Programming: A Genetic Programming Approach, published by Springer and Kluwer Academic Publishers.
Starting Page	2739
Ending Page	2768
Page Count	30
File Format	PDF
ISBN	9781450300735
DOI	10.1145/1830761.1830917
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Quantum computing Genetic programming Evolutionary algorithms
Content Type	Text
Resource Type	Article

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