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Recent Advances in Quantum Machine Learning
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Recent Advances in Quantum Machine Learning

A Special Issue of Entropy (ISSN 1099-4300) belonging to the section "Quantum Information".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1119

Editors

Dr. Nan Jiang

E-Mail Website
Guest Editor
College of Computer Science, Beijing University of Technology, Beijing 100124, China
Interests: quantum machine learning; quantum computation; quantum program quality assurance
Dr. Jian Wang

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: quantum machine learning; quantum computation

Special Issue Information

Dear Colleagues,

The intersection of quantum computing and machine learning has emerged as one of the most promising frontiers in modern computational science. As classical machine learning models grow increasingly complex and data-intensive, quantum computing offers fundamentally new computational paradigms that may overcome key limitations in speed, expressibility, and energy efficiency. Quantum machine learning (QML) seeks to harness quantum mechanical phenomena, such as superposition, entanglement, and quantum interference, to accelerate and enhance learning algorithms.

Recent advances in variational quantum circuits, quantum neural networks, and quantum gradient descent methods have demonstrated encouraging results in tasks such as classification, optimization, and function approximation. Hybrid quantum–classical architectures, which combine the strengths of both paradigms, have attracted particular attention as near-term quantum hardware continues to mature. Meanwhile, information-theoretic perspectives, including quantum entropy measures and channel capacity, provide rigorous frameworks for understanding generalization, expressibility, and the fundamental limits of quantum learning systems.

This Special Issue invites contributions that explore the full spectrum of recent advances in quantum machine learning, including, but not limited to, novel quantum learning algorithms, hybrid quantum–classical models, quantum optimization methods, theoretical analyses using information and entropy measures, and practical implementations on near-term quantum devices. We welcome both theoretical and applied work and encourage submissions that bridge quantum information theory with machine learning practice.

Dr. Nan Jiang
Dr. Jian Wang
Guest Editors

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-anonymized 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 machine learning
  • variational quantum circuits
  • quantum neural networks
  • hybrid quantum–classical models
  • quantum optimization
  • quantum gradient descent
  • quantum information theory
  • quantum entropy
  • near-term quantum devices
  • quantum computing

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Published Papers (2 papers)

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Research

33 pages, 9074 KB  
Article
A Quantum-Inspired Residual Self-Attention Network for Multimodal Sentiment Analysis
by Yupeng Liu, Xianjie Feng and Yewang Zhong
Entropy 2026, 28(9), 1014; https://doi.org/10.3390/e28091014 - 11 Sep 2026
Viewed by 222
Abstract
Multimodal sentiment analysis is challenging because textual, visual, and acoustic evidence is heterogeneous and oft en weakly aligned. Here, we present QRSAN, a quantum-inspired residual self-attention network that integrates an LSTM text encoder, modality-specific multilayer perceptrons for visual and acoustic inputs, normalized complex-valued [...] Read more.
Multimodal sentiment analysis is challenging because textual, visual, and acoustic evidence is heterogeneous and oft en weakly aligned. Here, we present QRSAN, a quantum-inspired residual self-attention network that integrates an LSTM text encoder, modality-specific multilayer perceptrons for visual and acoustic inputs, normalized complex-valued encoding, a TFN-derived interaction expansion, real-valued residual self-attention, and classical projection-score mapping. QRSAN runs entirely on classical hardware and does not perform physical quantum computation. Across CMU-MOSI, CMU-MOSEI, and IEMOCAP, QRSAN was evaluated using a common protocol. It achieved the highest numerical mean ACC and Binary_F1 among the evaluated models on CMU-MOSI, whereas its IEMOCAP label-wise accuracy was below that of EF-LSTM. These findings support the utility of combining constrained complex-valued representations with residual self-attention using the evaluated settings, without claiming universal state-of-the-art performance. Full article
(This article belongs to the Special Issue Recent Advances in Quantum Machine Learning)
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21 pages, 511 KB  
Article
Kernel-Weighted Aggregation for Hyperparameter-Free Quantum Federated Learning
by Rui Huang
Entropy 2026, 28(9), 958; https://doi.org/10.3390/e28090958 - 26 Aug 2026
Viewed by 251
Abstract
Quantum federated learning (QFL) enables collaborative training of variational quantum circuits across decentralized clients, but client drift under non-IID data distributions degrades standard Federated Averaging (FedAvg). Existing aggregation methods either introduce tunable hyperparameters that depend on unknown data heterogeneity or incur structural costs [...] Read more.
Quantum federated learning (QFL) enables collaborative training of variational quantum circuits across decentralized clients, but client drift under non-IID data distributions degrades standard Federated Averaging (FedAvg). Existing aggregation methods either introduce tunable hyperparameters that depend on unknown data heterogeneity or incur structural costs such as order-dependent error propagation. We propose Kernel-Weighted Aggregation (KWA), a hyperparameter-free aggregation strategy that constructs a non-parametric kernel density estimate over the received client parameter vectors at each communication round. Clients in high-density regions receive greater voting power, and isolated clients are automatically attenuated. The kernel bandwidth is the median of all pairwise squared distances. We evaluate KWA against six state-of-the-art QFL methods and five classical robust baselines on five binary MNIST digit-pair tasks under IID, Dir(0.5), and Dir(0.1) distributions with a unified four-qubit benchmark. Under IID data, KWA recovers the performance of FedAvg. Under Dir(0.5), KWA attains the highest average accuracy (0.7120), followed by FedAvg (0.7093). The margin at N=4 clients is not statistically significant. It grows to +0.023 at N=16 in the client scaling experiment. Under this distribution, the five classical robust baselines also rank below FedAvg. Under Dir(0.1), KWA exceeds FedAvg by 0.028 on average, with a pooled paired t-test p=0.068. Larger-circuit and three-class experiments show that the advantage does not yet transfer consistently beyond the four-qubit binary setting. KWA thus provides a hyperparameter-free aggregation strategy that recovers FedAvg under IID conditions, adapts to client drift, and requires no manually tuned hyperparameters. Full article
(This article belongs to the Special Issue Recent Advances in Quantum Machine Learning)
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