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Entropy
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The Future of Quantum Machine Learning and Quantum AI
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The Future of Quantum Machine Learning and Quantum AI

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 4373

Special Issue Editor

Prof. Dr. Andreas (Andrzej) Wichert

E-Mail Website
Guest Editor
Department of Computer Science and Engineering (DEI) , Technical University of Lisbon, 2744-016 Porto Salvo, Portugal
Interests: machine learning; artificial intelligence; quantum computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of quantum coprocessors for extensive and non-tractable computation routines in AI will lead to new machine learning and artificial intelligence applications.

However, we need a deeper understanding of the mathematical framework and the resulting constraints. What are the quantum machine learning applications? What are the advantages of quantum machine learning algorithms to combat various proposed artificial problems? Can we apply quantum machine learning and quantum AI for real-world applications?

Linear algebra-based quantum machine learning is based on quantum gates that describe quantum basic linear algebra subroutines. These subroutines exhibit theoretical exponential speedups compared to their classical counterparts and are essential for machine learning. The quantum algorithm for linear systems of equations is one of the main fundamental algorithms expected to provide an increase in speed compared to traditional algorithms. The algorithm is also called the HHL algorithm and is based on Kitaev’s phase algorithm. Quantum principal component analysis (qPCA) and quantum random-access memory (qRAM) have been previously described, and quantum kernels and quantum advantage kernels have already been introduced and identified. Still, there are many open problems, such as the efficient preparation of data or the estimation of the expected values that describe the results.

We discussed these problems in the Special Issue "Quantum Machine Learning 2022" and made much progress. Based on its success, we are continuing this trend with the current Special Issue "The Future of Quantum Machine Learning and Quantum AI". To view the Special Issue "Quantum Machine Learning 2022", please consult the following link:

https://www.mdpi.com/journal/entropy/special_issues/quantum_machine_learning_2022

Prof. Dr. Andreas (Andrzej) Wichert
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. Entropy 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 2600 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-inspired machine learning
  • quantum-inspired AI
  • quantum genetic algorithms
  • quantum machine learning applications
  • linear algebra-based quantum machine learning
  • quantum kernels
  • efficient preparation of data
  • quantum programming languages
  • variational algorithms
  • quantum decision trees
  • quantum neural networks
  • quantum annealing

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Published Papers (4 papers)

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Research

19 pages, 2580 KiB  
Article
A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory
by Kimleang Kea, Dongmin Kim, Chansreynich Huot, Tae-Kyung Kim and Youngsun Han
Entropy 2024, 26(11), 954; https://doi.org/10.3390/e26110954 - 6 Nov 2024
Viewed by 605
Abstract
The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of [...] Read more.
The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of ML, prediction techniques have become more efficient but computationally demanding for classical computers. Given the rise of quantum computing (QC), which holds great promise for being exponentially faster than current classical computers, it is natural to explore ML within the QC domain. In this study, we leverage a hybrid quantum-classical ML approach to predict a company’s stock price. We integrate classical long short-term memory (LSTM) with QC, resulting in a new variant called QLSTM. We initially validate the proposed QLSTM model by leveraging an IBM quantum simulator running on a classical computer, after which we conduct predictions using an IBM real quantum computer. Thereafter, we evaluate the performance of our model using the root mean square error (RMSE) and prediction accuracy. Additionally, we perform a comparative analysis, evaluating the prediction performance of the QLSTM model against several other classical models. Further, we explore the impacts of hyperparameters on the QLSTM model to determine the best configuration. Our experimental results demonstrate that while the classical LSTM model achieved an RMSE of 0.0693 and a prediction accuracy of 0.8815, the QLSTM model exhibited superior performance, achieving values of 0.0602 and 0.9736, respectively. Furthermore, the QLSTM outperformed other classical models in both metrics. Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI)
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13 pages, 1093 KiB  
Article
Quantum Machine Learning—Quo Vadis?
by Andreas Wichert
Entropy 2024, 26(11), 905; https://doi.org/10.3390/e26110905 - 24 Oct 2024
Viewed by 847
Abstract
The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due [...] Read more.
The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the possibility of compressed data representation. However, can we really apply quantum machine learning for real-world applications? What are the advantages of quantum machine learning algorithms in addition to some proposed artificial problems? Is the promised exponential or quadratic speed-up realistic, assuming that real quantum computers exist? Quantum machine learning is based on statistical machine learning. We cannot port the classical algorithms directly into quantum algorithms due to quantum physical constraints, like the input–output problem or the normalized representation of vectors. Theoretical speed-ups of quantum machine learning are usually analyzed in the literature by ignoring the input destruction problem, which is the main bottleneck for data encoding. The dilemma results from the following question: should we ignore or marginalize those constraints or not? Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI)
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16 pages, 3583 KiB  
Article
BHT-QAOA: The Generalization of Quantum Approximate Optimization Algorithm to Solve Arbitrary Boolean Problems as Hamiltonians
by Ali Al-Bayaty and Marek Perkowski
Entropy 2024, 26(10), 843; https://doi.org/10.3390/e26100843 - 6 Oct 2024
Viewed by 787
Abstract
A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the “Boolean-Hamiltonians Transform for QAOA” (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial [...] Read more.
A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the “Boolean-Hamiltonians Transform for QAOA” (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial optimization problems using QAOA, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for Boolean problems, by transforming such problems from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a transformation, we noticed that the total utilized numbers of qubits and quantum gates are dramatically minimized for the generated Hamiltonians of QAOA. In this article, arbitrary Boolean problems are examined by successfully solving them with our BHT-QAOA, using different structures based on various logic synthesis methods, an IBM quantum computer, and a classical optimization minimizer. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, and machine learning, just to name a few, in the hybrid classical-quantum domain. Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI)
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12 pages, 1877 KiB  
Article
Breast Cancer Detection with Quanvolutional Neural Networks
by Nadine Matondo-Mvula and Khaled Elleithy
Entropy 2024, 26(8), 630; https://doi.org/10.3390/e26080630 - 26 Jul 2024
Viewed by 1609
Abstract
Quantum machine learning holds the potential to revolutionize cancer treatment and diagnostic imaging by uncovering complex patterns beyond the reach of classical methods. This study explores the effectiveness of quantum convolutional layers in classifying ultrasound breast images for cancer detection. By encoding classical [...] Read more.
Quantum machine learning holds the potential to revolutionize cancer treatment and diagnostic imaging by uncovering complex patterns beyond the reach of classical methods. This study explores the effectiveness of quantum convolutional layers in classifying ultrasound breast images for cancer detection. By encoding classical data into quantum states through angle embedding and employing a robustly entangled 9-qubit circuit design with an SU(4) gate, we developed a Quantum Convolutional Neural Network (QCNN) and compared it to a classical CNN of similar architecture. Our QCNN model, leveraging two quantum circuits as convolutional layers, achieved an impressive peak training accuracy of 76.66% and a validation accuracy of 87.17% at a learning rate of 1 × 10−2. In contrast, the classical CNN model attained a training accuracy of 77.52% and a validation accuracy of 83.33%. These compelling results highlight the potential of quantum circuits to serve as effective convolutional layers for feature extraction in image classification, especially with small datasets. Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI)
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