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The Future of Quantum Machine Learning and Quantum AI, 2nd Edition

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3053

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,

Using 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 in combating 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 describing 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 increase 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, many open problems exist, 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 Issues “Quantum Machine Learning 2022” and “The Future of Quantum Machine Learning and Quantum AI” and made much progress. Based on their success, we continue this trend with the current Special Issue, “The Future of Quantum Machine Learning and Quantum AI, 2nd Edition”. To view previous Special Issues, please consult the following link:

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

https://www.mdpi.com/journal/entropy/special_issues/7EU7D9707M

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 250 words) can be sent to the Editorial Office for assessment.

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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Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 2376 KB  
Article
Nested Grover’s Algorithm for Tree Search
by Andreas Wichert
Entropy 2026, 28(1), 24; https://doi.org/10.3390/e28010024 - 24 Dec 2025
Viewed by 263
Abstract
We investigate optimizing quantum tree search algorithms by employing a nested Grover Algorithm. This approach seeks to enhance results compared to previous Grover-based methods by expanding the tree of partial assignments to a specific depth and conducting a quantum search within the subset [...] Read more.
We investigate optimizing quantum tree search algorithms by employing a nested Grover Algorithm. This approach seeks to enhance results compared to previous Grover-based methods by expanding the tree of partial assignments to a specific depth and conducting a quantum search within the subset of remaining assignments. The study explores the implications and constraints of this approach, providing a foundation for quantum artificial intelligence applications. Instead of utilizing conventional heuristic functions that are incompatible with quantum tree search, we introduce the partial candidate solution, which indicates a node at a specific depth of the tree. By employing such a function, we define the concatenated oracle, which enables us to decompose the quantum tree search using Grover’s algorithm. With a branching factor of 2 and a depth of m, the costs of Grover’s algorithm are O(2m/2). The concatenated oracle allows us to reduce the cost to O(m·2m/4) for m partial candidate solutions. Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI, 2nd Edition)
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30 pages, 1203 KB  
Article
Quantum AI in Speech Emotion Recognition
by Michael Norval and Zenghui Wang
Entropy 2025, 27(12), 1201; https://doi.org/10.3390/e27121201 - 26 Nov 2025
Cited by 1 | Viewed by 770
Abstract
We evaluate a hybrid quantum–classical pipeline for speech emotion recognition (SER) on a custom Afrikaans corpus using MFCC-based spectral features with pitch and energy variants, explicitly comparing three quantum approaches—a variational quantum classifier (VQC), a quantum support vector machine (QSVM), and a Quantum [...] Read more.
We evaluate a hybrid quantum–classical pipeline for speech emotion recognition (SER) on a custom Afrikaans corpus using MFCC-based spectral features with pitch and energy variants, explicitly comparing three quantum approaches—a variational quantum classifier (VQC), a quantum support vector machine (QSVM), and a Quantum Approximate Optimisation Algorithm (QAOA)-based classifier—against a CNN–LSTM (CLSTM) baseline. We detail the classical-to-quantum data encoding (angle embedding with bounded rotations and an explicit feature-to-qubit map) and report test accuracy, weighted precision, recall, and F1. Under ideal analytic simulation, the quantum models reach 41–43% test accuracy; under a realistic 1% NISQ noise model (100–1000 shots) this degrades to 34–40%, versus 73.9% for the CLSTM baseline. Despite the markedly lower empirical accuracy—expected in the NISQ era—we provide an end-to-end, noise-aware hybrid SER benchmark and discuss the asymptotic advantages of quantum subroutines (Chebyshev-based quantum singular value transformation, quantum walks, and block encoding) that become relevant only in the fault-tolerant regime. Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI, 2nd Edition)
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24 pages, 3666 KB  
Article
Contrastive Learning Pre-Training and Quantum Theory for Cross-Lingual Aspect-Based Sentiment Analysis
by Xun Li and Kun Zhang
Entropy 2025, 27(7), 713; https://doi.org/10.3390/e27070713 - 1 Jul 2025
Cited by 1 | Viewed by 1361
Abstract
The cross-lingual aspect-based sentiment analysis (ABSA) task continues to pose a significant challenge, as it involves training a classifier on high-resource source languages and then applying it to classify texts in low-resource target languages, thereby bridging linguistic gaps while preserving accuracy. Most existing [...] Read more.
The cross-lingual aspect-based sentiment analysis (ABSA) task continues to pose a significant challenge, as it involves training a classifier on high-resource source languages and then applying it to classify texts in low-resource target languages, thereby bridging linguistic gaps while preserving accuracy. Most existing methods achieve exceptional performance by relying on multilingual pre-trained language models (mPLM) and translation systems to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which ultimately hinders classification performance in target languages. To address this challenge, we propose CLQT, a novel framework that combines contrastive learning pre-training with quantum theory to address the cross-lingual ABSA task. Firstly, we develop a contrastive learning strategy to align data between the source and target languages. Subsequently, we incorporate a quantum network that employs quantum projection and quantum entanglement to facilitate effective knowledge transfer across languages. Extensive experiments reveal that the novel CLQT framework both achieves strong results and has a beneficial overall influence on the cross-lingual ABSA task. Full article
(This article belongs to the Special Issue The Future of Quantum Machine Learning and Quantum AI, 2nd Edition)
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