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Computer Science - Artificial Intelligence | Automatic Quantum Computer Programming - A Genetic Programming Approach

Automatic Quantum Computer Programming

A Genetic Programming Approach

Series: Genetic Programming, Vol. 7

Spector, Lee

2004, XI, 153 p.

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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.

Content Level » Research

Keywords » algorithms - code - communication - evolution - evolutionary computation - genetic algorithms - genetic operator - genetic programming - programming language - quantum computer - quantum computing - quantum gate - qubit - search algorithm - simulation

Related subjects » Artificial Intelligence - Quantum Physics - Software Engineering - Theoretical Computer Science

Table of contents 

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. Appendices: QGAME Source Code. Index.

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