By Lee Spector
Automatic Quantum laptop Programming offers an creation to quantum computing for non-physicists, in addition to an creation to genetic programming for non-computer-scientists. The ebook explores numerous ways that genetic programming can help automated quantum computing device programming and provides distinctive descriptions of particular suggestions, besides a number of examples in their human-competitive functionality on particular difficulties. resource code for the author’s QGAME quantum computing device simulator is incorporated as an appendix, and tips to extra on-line assets provide the reader with an array of instruments for computerized quantum laptop programming.
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Automatic Quantum Computer Programming: A Genetic Programming Approach
Automated Quantum computing device Programming presents an creation to quantum computing for non-physicists, in addition to an advent to genetic programming for non-computer-scientists. The e-book explores numerous ways that genetic programming can help computerized quantum machine programming and offers specific descriptions of particular ideas, in addition to a number of examples in their human-competitive functionality on particular difficulties.
This publication constitutes the refereed lawsuits of the 18th foreign convention on automatic Deduction, CADE - 18, held in Copenhagen, Denmark, in July 2002. The 27 revised complete papers and 10 method descriptions provided including 3 invited contributions have been conscientiously reviewed and chosen from 70 submissions.
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Additional info for Automatic Quantum Computer Programming: A Genetic Programming Approach
Sample text
Branchi... brancho... (END) This is actually a sequence of instruction expressions, beginning with the MEASURE expression that specifies the qubit to measure. Any number of instruction expressions may occur between the MEASURE expression and the first following END; all of these will be executed in the branch of the simulation corresponding to a measurement of 1. Similarly, any number of instruction expressions may occur between the first following END and a subsequent END; all of these will be executed in the branch of the simulation corresponding to a measurement of 0.
The essential feature of this program representation with respect to genetic programming is syntactic uniformity — any sub-program can be substituted for any other sub-program within any program, and the result will necessarily be syntactically well formed. It is therefore easy to devise genetic operators that operate "blindly" on programs but nonetheless always produce syntactically valid results. 1, which shows the tree form of the arithmetic expression given above. In traditional genetic programming all of the constant terminals used for a particular run must be of the same data type.
If there is no END following the MEASURE expression then the entire remainder of the program is branchi and there is no' brancho. Similarly, if there is only one subsequent END then the entire program beyond that END is brancho. Unmatched ENDs are ignored. A few additional instruction expressions provide benefits in special circumstances. Expressions of the form: (MATRIX-GATE M history) allow for the inclusion of gates with arbitrary unitary matrices. M here is a fully expanded matrix, of size 2" x 2" for an n-qubit system, expressed in Lisp 2D array notation.