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Applications of Quantum Computing in Artificial Intelligence
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Applications of Quantum Computing in Artificial Intelligence

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Physics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2313

Special Issue Editor

Prof. Dr. Oscar Humberto Ross

E-Mail Website1 Website2
Guest Editor
Centro de Investigación y Desarrollo de Tecnología Digital, Instituto Politécnico Nacional, México City 07738, Mexico
Interests: intelligent systems; quantum computing; quantum intelligent systems; evolutionary computation; fuzzy systems; neural networks; deep learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the computational demands of tackling complex problems often entail handling vast datasets and intricate learning structures, employing neurons, fuzzy logic, and optimization algorithms. Quantum artificial intelligence, an emerging and promising paradigm, seeks to address computationally intractable issues by integrating quantum computing with artificial intelligence. This fusion aims to be exponentially faster than the classical and non-classical methods. At present, various approaches are being explored to achieve this integration; therefore, this Special Issue is dedicated to presenting innovative theoretical and practical proposals within the realm of quantum machine learning. It covers topics, such as quantum learning theory, quantum deep learning, quantum convolutional neural networks, quantum transfer learning, quantum optimization algorithms, and other techniques, that amalgamate quantum computing and learning algorithms. The overarching goal is to contribute to the advancement of this field by showcasing cutting-edge ideas and methodologies.

Prof. Dr. Oscar Humberto Ross
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum machine learning
  • quantum intelligent systems
  • quantum learning systems
  • quantum deep learning
  • quantum transfer learning
  • quanvolutional neural networks

Published Papers (2 papers)

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Research

16 pages, 523 KiB  
Article
An Effective Strategy for Sentiment Analysis Based on Complex-Valued Embedding and Quantum Long Short-Term Memory Neural Network
by Zhulu Chu, Xihan Wang, Meilin Jin, Ning Zhang, Quanli Gao and Lianhe Shao
Axioms 2024, 13(3), 207; https://doi.org/10.3390/axioms13030207 - 21 Mar 2024
Viewed by 766
Abstract
Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing [...] Read more.
Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model. Full article
(This article belongs to the Special Issue Applications of Quantum Computing in Artificial Intelligence)
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30 pages, 4038 KiB  
Article
Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle for Efficient Placement of Unmanned Aerial Vehicles in Natural Disaster Areas
by Enrique Ballinas, Oscar Montiel, Anabel Martínez-Vargas and Gabriela Rodríguez-Cortés
Axioms 2024, 13(1), 48; https://doi.org/10.3390/axioms13010048 - 13 Jan 2024
Viewed by 970
Abstract
A Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle (HQGAFARA) is introduced in this work to determine the optimal placements for Unmanned Aerial Vehicles (UAVs) aimed at maximizing coverage in disaster-stricken areas. The HQGAFARA is a hybrid quantum fuzzy meta-heuristic that uses [...] Read more.
A Hybrid Quantum Genetic Algorithm with Fuzzy Adaptive Rotation Angle (HQGAFARA) is introduced in this work to determine the optimal placements for Unmanned Aerial Vehicles (UAVs) aimed at maximizing coverage in disaster-stricken areas. The HQGAFARA is a hybrid quantum fuzzy meta-heuristic that uses the Deutsch–Jozsa quantum circuit to generate quantum populations synergistically working as haploid recombination and mutation operators that take advantage of quantum entanglement, providing exploitative and explorative features to produce new individuals. In place of the conventional lookup table or mathematical equation, we introduced a fuzzy heuristic to adapt the rotation angle employed in quantum gates. The hybrid nature of this algorithm becomes evident through its utilization of both classical and quantum computing components. Experimental evaluations were conducted using two distinct test sets. The first set, termed the “best case”, represents conditions that are the most favorable for determining the UAV positions, while the second set, the “worst-case”, simulates highly challenging conditions for locating the UAV positions, thereby posing a significant test for the proposed algorithm. We carried out statistical comparative analyses, assessing the HQGAFARA against other hybrid quantum algorithms that employ different rotation angles and against the classical genetic algorithm. The experimental results demonstrated that the HQGAFARA performed comparably, if not better, to the classical genetic algorithm regarding precision. Furthermore, quantum algorithms showcased their computational prowess in experiments related to the convergence time. Full article
(This article belongs to the Special Issue Applications of Quantum Computing in Artificial Intelligence)
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