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Constrained Quantum Optimization Algorithm

Authors: Sara El Gaily; Sandor Imre;

Constrained Quantum Optimization Algorithm

Abstract

Classical computing still suffers from the high computational complexity of finding the extreme value of a constrained goal function or an unordered database with respect to a certain constraint. Quantum computing offers new possibilities and solutions due to its quantum nature to surmount this problem. In this paper, we extended a new version of quantum existence testing (QET) _we call it a constrained quantum relation testing (CQRT)_ we also investigate analytically its classical and quantum certainty. Furthermore, we show how the CQRT can be applied for extending the quantum extreme value searching algorithm (QEVSA) version to a constrained quantum optimization algorithm (CQOA). And we gave an extensive explanation of how the CQOA works and how it finds the extreme constrained optimum value solution with high complexity reduction, maximum exponential speed, and high certainty.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
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