Supervised Machine Learning Strategies for Investigation of Weird Pattern Formulation from Large Volume Data Using Quantum Computing

Nivelkar, Mukta; Bhirud, S. G.

doi:10.1007/978-981-16-2164-2_45

Cited by 3 publications

(1 citation statement)

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“…Other use of QC can be seen in Ong and Tan ( 2022 ), where the authors proposed a work using a quantum circuit program generation with a genetic algorithm for the Open Quantum Assembly Language. In other research, was proposed a Supervised Machine Learning Strategies for Investigation of Weird Pattern Formulation from Large Volume Data Using Quantum Computing, in this work, Quantum machine learning accelerated the supervised, unsupervised, and reinforcement learning methods obtained better results than the classical machi Nivelkar and Bhirud ( 2022 ).…”

Section: Literature Reviewmentioning

confidence: 79%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

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In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problems currently intractable with traditional algorithms can be solved with QA. On the other hand, DL algorithms offer what is known as machine learning techniques. DL is concerned with teaching a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of plain text, images, or sound. The inspiration for deep learning is the way that the human brain filters information. Therefore, in this research, we analyzed these two areas to observe the most relevant works and applications developed by the researchers in the world.

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Section: Literature Reviewmentioning

confidence: 79%