Evolutionary design of reversible digital circuits using IMEP the case of the even parity problem

Hadjam, Fatima Zohra; Moraga, Claudio

doi:10.1109/cec.2010.5586252

Cited by 4 publications

(2 citation statements)

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“…Another recent comprehensive review is presented in Saeedi and Markov [2013] where algorithmic paradigms such as search-based, cycle-based, introduced in Hadjam and Moraga [2010aMoraga [ , 2010b. It has proven its efficiency to find some optimal solutions to well-known benchmarks however, it failed to solve some others particularly due to the constraint to obtain fanout free results, which is one of the main requirements of a reversible circuit.…”

Section: Introductionmentioning

confidence: 99%

Rimep2

Hadjam

Moraga

2014

J. Emerg. Technol. Comput. Syst.

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RIMEP (Reversible Improved Multi Expression Programming), is a system that has been developed for designing reversible digital circuits. This article discloses a new version of RIMEP called "RIMEP2". The goal was to evolve reversible circuits in a "fanout free" search space. The major changes that RIMEP has undergone, are made in the structure of the chromosome and in the fitness calculation. Although the changes seem to be minor, the impact is effective. The execution time has been considerably decreased and optimal competitive solutions were found for a set of 30 selected benchmarks, where a quantum cost reduction up to 96.13% was reached with an average of 42.17%. ACM Reference Format:Fatima Zohra Hadjam and Claudio Moraga. 2014. RIMEP2: Evolutionary design of reversible digital circuits.

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Section: Introductionmentioning

confidence: 99%

Rimep2

Hadjam

Moraga

2014

J. Emerg. Technol. Comput. Syst.

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“…They are used to solve difficult search and optimization problems that are otherwise unsolvable by exhaustive or analytic approaches in reasonable amount of time or with bounded memory. The application of evolutionary algorithm for quantum synthesis is a topic that has been previously studied since the beginning of the century [8], [9], [10]. A lot of work has been done for solving the problem in classical paradigm using different approaches and hardware, however the execution time is a limiting factor even for the most optimal evolutionary and general algorithms [11] directly designing quantum circuits.…”

Section: Introductionmentioning

confidence: 99%

Quantum encoded quantum evolutionary algorithm for the design of quantum circuits

Krylov

Lukáč

2019

Proceedings of the 16th ACM International Conference on Computing Frontiers

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In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can in theory be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated in GPGPU. The main target of this paper, is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum circuits. The results demonstrate that on one hand the quantum encoding and quantum implementation compatible implementation provides certain disadvantages with respect to the classical evolutionary computation. On the other hand, encoding certain components in a quantum compatible manner could in theory allow to accelerate the search. This acceleration would in turn counter weight the implementation limitations.

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