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International Journal of Intelligent Systems and Applications in Engineering

A Clustering Approach for Information Retrieval Using A Quantum-Based Computation Technique

Rupam Bhagawati, Thiruselvan Subramanian

Published Feb 2, 2024• 0 citations
OpenAlex

Abstract

Today’s era of the internet and digitalization requires information of various forms. Information in any configuration is predominant in performing various tasks like management and retrieval. Static information is mostly present in documents and to perform retrieval, browsing, and managing this kind of information, we have many strategies available in classical form. Many classical forms can also be categorized from high level to low level. To some extent, the realm of Quantum Computing is also employed for the task and many contemporary researchers state efficient algorithms for the task. Relevant information per the user’s need is the most prominent goal of an Information System. Documents play an important role in information in this regard as well as keeping uncertainty on the relevancy of correct information as per the requirement. Documents are representations of all kinds of information related to numerous fields like academia, media, law, engineering, geography, and many more. A huge collection of information representation is scattered everywhere throughout the logical world of the internet. The collection is a blender of all types of documents from different fields and semantics. Searching and sorting complete relevant documents from this gigantic blender according to information need is an onerous task to an extent. Grouping the same information in clusters brings a change to the relevancy rate of the required information. Quantum mechanics track towards Quantum information processing provides a realm for clustering of information in the form of acquaintances. Using quantum computation in the realm of quantum mechanics, an algorithm is proposed for the task which will lead to the grouping of information by considering the microscopic properties of each and every acquaintance.

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Today’s era of the internet and digitalization requires information of various forms. Information in any configuration is predominant in performing various tasks like management and retrieval. Static information is mostly present in documents and to perform retrieval, browsing, and managing this kind of information, we have many strategies available in classical form. Many classical forms can also be categorized from high level to low level. To some extent, the realm of Quantum Computing is also employed for the task and many contemporary researchers state efficient algorithms for the task. Relevant information per the user’s need is the most prominent goal of an Information System. Documents play an important role in information in this regard as well as keeping uncertainty on the relevancy of correct information as per the requirement. Documents are representations of all kinds of information related to numerous fields like academia, media, law, engineering, geography, and many more. A huge collection of information representation is scattered everywhere throughout the logical world of the internet. The collection is a blender of all types of documents from different fields and semantics. Searching and sorting complete relevant documents from this gigantic blender according to information need is an onerous task to an extent. Grouping the same information in clusters brings a change to the relevancy rate of the required information. Quantum mechanics track towards Quantum information processing provides a realm for clustering of information in the form of acquaintances. Using quantum computation in the realm of quantum mechanics, an algorithm is proposed for the task which will lead to the grouping of information by considering the microscopic properties of each and every acquaintance.
In this internet and digitalization age, information in any form is very important to perform a digital task. The processing of any information to obtain the desired results requires a specific medium and sometimes to accomplish various tasks related to that set of information like browsing, searching, sorting, retrieval and management. In order to perform those tasks on the information, which are present on various acquaintances, we need to analyze the information by performing an unsupervised clustering in the realm of quantum computation. Quantum clustering is the core technique used in quantum computation to perform clustering of information with several algorithms that have been introduced and studied till date to analyze the cluster for increasing the efficiency of information exploration, information retrieval, information management and browsing system. Hence, introducing a quantum clustering technique to form clusters which would include sentences from a set of informative data set and the formation of clusters would be carried out by performing Semantic Analysis.
In the domain of Quantum Information processing (QIP), quantum clustering approach plays a vital role for processing information. The quantum clustering technique is basically used for providing effective information navigation and browsing mechanism. There are many classical approach of clustering the documents like k-means clustering algorithm, hierarchical clustering algorithm, information bottleneck clustering algorithm and many more. In this paper we proposed a clustering method which is based on quantum theory. For clustering the documents, such that which provides an agreeable result in comparison of existing classical approach.
The accuracy of information retrieval systems is measured by the relevancy of retrieved results as per the user’s query. Relevant results are presented by performing various methods viz. indexing and crawling and the output of these processes is the retrieved results that have to pass through the ranking process which is the central goal of information retrieval systems. The ranking is carried out through the classification or clustering of processed results which can include redundant and noisy features. The accuracy of classification or clusters for the ranking process can be maximized by removing noisy and duplicate features through the feature selection method. Although feature selection is an expensive computational process, after many decades, quantum computation tools are in use for many algorithms to implement realistic problems, particularly in the standard of Quantum Annealing. This paper focuses to prospect the standard of quantum computing in order to increase the quality of information classes through the feature selection method. The persuasiveness of the quantum approach is comparable to the classical process that focused on the reliability of quantum methodology from different perspectives.
In recent years we have seen that the world is increasingly driven by data or information. Data has always been important for business and research, but in the current era the concept of big data is changing the way people employ data for decision making. Big data is playing a big role in informed decision making processes. In recent years, the importance of clustering with big sequential data sets has grown substantially due to its wide applications in areas like DNA genome modeling and financial data analysis. Handling big data clustering activity is a complex task. This intrinsic difficulty worsens with huge sequential data (can be termed as “Big Sequences” ) where the structure of the underlying process is often difficult to deduce because of the difference in lengths of sequences. In this chapter, we have proposed an interesting approach for clustering sequencing data in a quantum paradigm which is based on Quantum K-Means clustering and the Hidden Markov Model. In this approach, a new representation space of input data objects (sequences) is established. The vector of each object's similarities with respect to a pre-determinate set of other objects describes the respective object. These similarities are identified using the Hidden Markov Model . A novel representation space of input sequences has been mapped to quantum states with the help of quantum RAM (QRAM) . Then clustering is performed using quantum K -means clustering over the new transformed input data set. We have observed an exponential speed up gain in run time complexity in our approach as compared to the classical approaches.
September 2006Abstract: Traditional database query languages are based on set theory and crisp first order logic. However, many applications require retrieval-like queries which return result objects associated with a degree value of being relevant to the query. Traditionally, retrieval systems estimate relevance by exploiting hidden object semantics whereas query processing in database systems relies on matching select-conditions with attribute values. Thus, different mechanisms were developed for database and information retrieval systems. In consequence, there is a lack of support for queries involving both retrieval and database search terms. In this work, we develop a unifying framework based on the mathematical formalism of quantum mechanics and quantum logic. Van Rijsbergen already discussed the strong relation between the formalism of quantum mechanics and information retrieval. The goal of this work is to interrelate concepts from database query processing to concepts from quantum mechanics and logic. As result, we obtain a common theory which allows us to incorporate seamlessly retrieval search into traditional database query processing. Exploiting our theoretical results, we introduce the quantum query language QQL. In contrast to competing approaches, our formalism is based on quantum logic.
A key concept in many Information Retrieval (IR) tasks, e.g. document indexing, query language modelling, aspect and diversity retrieval, is the relevance measurement of topics, i.e. to what extent an information object (e.g. a document or a query) is about the topics. This paper investigates the interference of relevance measurement of a topic caused by another topic. For example, consider that two user groups are required to judge whether a topic q is relevant to a document d, and q is presented together with another topic (referred to as a companion topic). If different companion topics are used for different groups, interestingly different relevance probabilities of q given d can be reached. In this paper, we present empirical results showing that the relevance of a topic to a document is greatly affected by the companion topic’s relevance to the same document, and the extent of the impact differs with respect to different companion topics. We further analyse the phenomenon from classical and quantum-like interference perspectives, and connect the phenomenon to nonreality and contextuality in quantum mechanics. We demonstrate that quantum like model fits in the empirical data, could be potentially used for predicting the relevance when interference exists.