DocumentCode :
2917500
Title :
Evolutionary approach to quantum symbolic logic synthesis
Author :
Lukac, Martin ; Perkowski, Marek
Author_Institution :
Dept. of Electr. & Comput. Eng., Portland State Univ., Portland, OR
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
3374
Lastpage :
3380
Abstract :
In this paper we present an evolutionary approach to the quantum symbolic logic synthesis. We use a genetic algorithm to synthesize quantum circuits from examples, allowing to synthesize functions that are both completely and incompletely specified. The symbolic synthesis is implemented in the GA so as to verify our approach. The Occam Razor principle, fundamental to inductive learning as well as to logic synthesis, is satisfied in this approach by seeking circuits of reduced complexity. The GA is tested on a set of benchmark functions representing single output quantum circuits as well as multiple entangled-qubit state generators.
Keywords :
evolutionary computation; formal logic; genetic algorithms; learning by example; Occam Razor principle; benchmark functions; evolutionary approach; genetic algorithm; inductive learning; quantum circuits; quantum symbolic logic synthesis; single output quantum circuits; Automatic control; Benchmark testing; Circuit synthesis; Circuit testing; Genetic algorithms; Logic circuits; Minimization; Quantum computing; Quantum entanglement; Quantum mechanics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631254
Filename :
4631254
Link To Document :
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