Automatic Quantum Computer Programming

A Genetic Programming Approach
PDF
(153 Seiten)
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ISBN-13:
9781402078958
Einband:
PDF
Seiten:
153
Autor:
Lee Spector
Serie:
Genetic Programming
eBook Typ:
PDF
eBook Format:
PDF
Kopierschutz:
Adobe DRM [Hard-DRM]
Sprache:
Englisch
Beschreibung:

Automatic Quantum Computer Programming provides an introduction to quantum computing for non-physicists, as well as an introduction to genetic programming for non-computer-scientists. The book explores several ways in which genetic programming can support automatic quantum computer programming and presents detailed descriptions of specific techniques, along with several examples of their human-competitive performance on specific problems. Source code for the author's QGAME quantum computer simulator is included as an appendix, and pointers to additional online resources furnish the reader with an array of tools for automatic quantum computer programming.
Automatic Quantum Computer Programming provides an introduction to quantum computing for non-physicists, as well as an introduction to genetic programming for non-computer-scientists. The book explores several ways in which genetic programming can support automatic quantum computer programming and presents detailed descriptions of specific techniques, along with several examples of their human-competitive performance on specific problems. Source code for the author's QGAME quantum computer simulator is included as an appendix, and pointers to additional online resources furnish the reader with an array of tools for automatic quantum computer programming.
Preface. 1: The Power of Quantum Computing. 1. What is Quantum Computing? 2. Possibilities Count. 3. The Role of Automatic Programming. 2: Quantum Computer Simulation. 1. Bits, Qubits and Gates. 2. Gate-Level Simulation. 3: Quantum Computer Programming. 1. QGAME: Quantum Gate and Measurement Emulator. 2. Visualization. 3. Example: Grover's Database Search Algorithm. 4: Genetic and Evolutionary Computation. 1. What Is Genetic and Evolutionary Computation? 2. Genetic Algorithms. 3. Scalability via Parallelism. 4. Applicability of Genetic and Evolutionary Computation. 5: Genetic Programming. 1. Programming by Genetic Algorithm. 2. Traditional Program Representations. 3. Traditional Genetic Operators. 4. Example: Symbolic Regression. 5. Obtaining Genetic Programming Results. 6: Evolution of Complex Programs. 1. Types, Modules and Development. 2. The Push Programming Language. 3. Push Examples. 4. PshGP: Genetic Programming with Push. 5. Autoconstructive Evolution. 7: Evolution of Quantum Programs. 1. Program Representations. 2. Fitness. 3. Operators and Refinements. 8: Evolved Quantum Programs. 1. The 1-Bit Deutsch-Jozsa Problem. 2. Grover's Database Search Problem. 3. Scaling Majority-ON. 4. The OR and AND/OR Problems. 5. Gate Communication Problems. 6. Significance of These Results. 9: Conclusions and Prospects. Appendic

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