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Quantum Computing Based Technique for Cancer Disease Detection

Setu Kumar Chaturvedi

Published Jan 1, 2014• 0 citations
OpenAlex

Abstract

In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease. Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies. This research work incorporates Quantum computing with the clustering algorithm i.e. Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique. The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection. Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering. Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer. So, the proposed system gives a new approach in the field of cancer disease diagnosis. The results obtained by different Hierarchical Clustering Techniques are discussed.

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In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease. Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies. This research work incorporates Quantum computing with the clustering algorithm i.e. Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique. The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection. Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering. Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer. So, the proposed system gives a new approach in the field of cancer disease diagnosis. The results obtained by different Hierarchical Clustering Techniques are discussed.
In present scenario where various diseases are here and out of them the cancer is one of them most devastating disease.Presently the detection procedure is very much time consuming and the results obtain by them are not so fast, so here in this respect there is a need of more accurate, fast and efficient method through computing technologies.This research work incorporates Quantum computing with the clustering algorithm i.e.Shor Algorithm of Quantum Computing with various Hierarchical Clustering Technique.The Hierarchical Clustering Technique helps in clustering of results to obtain an approach for Cancer Disease Detection.Now accurate results are obtained, when adopting Shor Algorithm of quantum computing in disease detection technique with different Hierarchical Clustering.Here the Shor Algorithm helps to increase the efficiency in term of accuracy and Hierarchical Clustering Technique helps to detect the stages of Cancer.So, the proposed system gives a new approach in the field of cancer disease diagnosis.The results obtained by different Hierarchical Clustering Techniques are discussed.
Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.
Barring any cancer prevention breakthroughs, the expansion of the aged population will likely increase number of older individuals diagnosed for cancer in the coming decades. Dimensions of the cancer burden and its devastating manner of challenge ahead are inferred in the context of with aging populations to underscore the possible increase that demographic factors may have on the magnitude of the cancer problem for older persons in the future years. Presently the detection procedure is very time consuming and not accurate, in this respect there is a need of more accurate, fast and efficient method through computing technologies. The present research work incorporates quantum computing with clustering algorithm i.e. Shor's algorithm of quantum computing with hierarchical clustering technique. Here adaptation of Shor's algorithm helps to increase accuracy, and hierarchical clustering technique helps to detect the stages of cancer.
Diagnosis and exact recommendation for a cancer patient is very much sensitive and of greater influencing in cure of this diseases.Traditionally the diagnosis procedure was very time consuming and not so accurate, so here in this respect there is a need of more accurate, fast and easily to understand method / diagnosis tool which gives reliable results.Here we use the shor algorithm, Hce tool and we get accurate and frequent result, when we adopt the approach of quantum computing with the traditional tools of diagnosis.
Quantum Computing is a prominent word in this era as it allows computation to be performed in no time. The motive of using Quantum Computing (QC) is that even exponentially large number of problems can be solved using it which was earlier difficult with the classical computing. Conventional methods are based on usage of bits which consist of 0's and 1's while QC works with qubits. The main issue is that conventional computing has issue of storage as well as computation even when parallel computation is performed on it. Concept of quantum parallelism allows the computation to be performed in exponentially very low time as compared to conventional method. This paper will discuss about Quantum Computing Algorithms and how Shor's algorithm is able to break RSA algorithms is discussed. Entanglement and superposition of qubits helps fast computation. The demonstration of the applicability has been evaluated based on Computation time, storage capacity, accuracy, confidentiality, efficiency, integrity, and availability. Among various algorithms Shor's technique is able to break various encryption algorithm with more supremacy as compared to conventional computing methods. In nutshell, paper will discuss about various QC algorithms and will illustrate how shor's algorithm is able to crack RSA.
Quantum machine learning (QML) in the field of disease detection and prediction use quantum computing techniques and algorithms to analyze and classify large datasets of medical information, by identifying subtle patterns and predict the occurrence or progression of diseases. It involves applying machine learning techniques to data from biological and medical research, such as-genomic and proteomic data, medical imaging, electronic health records, and clinical trial data, using quantum computing algorithms and architectures to perform these analyses more efficiently and accurately than classical computing methods. This approach has the potential to provide new insights into complex biological systems and facilitate the development of more effective treatments and personalized medicine. In this paper, a systematic review of the use of QML algorithms has been conducted, which focuses on the detection and prediction of diseases among patients. The current essence of the field along with the challenges and limitations of current works have also been discussed. After evaluating the implemented and proposed methods of data analysis, algorithm development, usefulness and efficiency of the system in various disease detection and prediction, a recommendation was made on the open research scopes in this field at the end of the paper.