Evolutionary approach to quantum symbolic logic synthesis

Martin Lukac; Marek Perkowski

doi:10.1109/CEC.2008.4631254

Evolutionary approach to quantum symbolic logic synthesis

Martin Lukac, Marek Perkowski

INF - Computer and Mathematical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. 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.

Original language	English
Title of host publication	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Pages	3374-3380
Number of pages	7
DOIs	https://doi.org/10.1109/CEC.2008.4631254
Publication status	Published - 2008 Nov 17
Event	2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 6

Other

Other	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Country/Territory	China
City	Hong Kong
Period	08/6/1 → 08/6/6

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

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10.1109/CEC.2008.4631254

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abstract = "In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. 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.",

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AB - In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. 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.

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BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008

T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008

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ER -

Evolutionary approach to quantum symbolic logic synthesis

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