Depth optimization for topological quantum circuits

Alfailakawi, Mohammad Gh.; Ahmad, Imtiaz; Alterkawi, Laila; Hamdan, Suha A.

doi:10.1007/s11128-014-0867-y

Cited by 5 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meta-heuristics can be divided into evolutionary algorithms (EAs) such as genetic algorithms (GAs), differential evolution (DE); and swarm intelligence algorithms such as particle swarm (PSO), ant colony (ACO), grey wolf (GWO) [2], phylogram analysis (OPA) [3], and cuckoo search (CS) among others [4]. To further improve the performance of meta-heuristics, researchers have applied a variety of techniques such as stochastic operators [5] or hybridization to solve specific optimization problems [6]- [8]. Due to the popularity of meta-heuristics in successfully solving optimization problems, these algorithms are being introduced in engineering and other curricula to equip students with the required skills for the market specifically in the emerging field of machine learning [9].…”

Section: Introductionmentioning

confidence: 99%

Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

2021

Self Cite

View full text Add to dashboard Cite

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other metaheuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

show abstract