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June 27, 2022
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Proceedings Paper
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Quantum Natural Language Processing Based Sentiment Analysis Using Lambeq Toolkit

Publicated to: ICPC2T 2022 - 2nd International Conference on Power, Control and Computing Technologies, Proceedings. - 2022-01-01 (), DOI: 10.1109/ICPC2T53885.2022.9776836

Authors:

Ganguly S; Morapakula SN; Coronado LMP
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Affiliations

Escuela Tecnica Superior de Ingenieria de Sistemas Informaticos, Universidad Politecnica de Madrid - Author
Karunya Institute of Technology and Sciences - Author

Abstract

Sentiment classification is one of the best use cases of classical natural language processing (NLP). We witness its power in various domains such as banking, business, and the marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology that can provide a quantum advantage for NLP tasks. In this paper, we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and decent accuracy for experiments run on a noisy quantum device. We utilize the lambeq QNLP toolkit and t|ket > by Cambridge Quantum (Quantinuum) to produce the results.
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Keywords

LambeqQuantum computingQuantum natural language processing

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • Scopus: 19
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 47.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 45 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.
  • The number of mentions on Wikipedia: 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/85414/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 178
  • Downloads: 2
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: India.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Ganguly S) and Last Author (POZO CORONADO, LUIS MIGUEL).

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