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Photonics
Special Issues
Recent Progress in Quantum Communication
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Recent Progress in Quantum Communication

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: 23 June 2026 | Viewed by 963

Editor

Dr. Dawei Wang

E-Mail Website
Guest Editor
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China
Interests: quantum key distribution; parametric optical processing; digital signal processing; optical polarization manipulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will showcase rapid advancement and cutting-edge research in the field of quantum communication, a critical technology for ensuring information security in the future. It will highlight progress in practical Quantum Key Distribution (QKD) systems, including the deployment of satellite-based QKD for global secure networks and the development of high-rate, long-distance fiber-optic systems. Significant emphasis is placed on overcoming scalability challenges through the integration of quantum repeaters and the exploration of quantum memories to enable efficient entanglement distribution across vast distances.

Furthermore, this issue will explore the burgeoning domain of quantum networks, featuring research on quantum teleportation, the interconnection of quantum processors, and hybrid quantum–classical network architectures. Contributions will also cover the development of robust and miniaturized hardware, such as improved single-photon sources and detectors, which are essential for moving from laboratory demonstrations to real-world applications. This compilation will provide a comprehensive overview of the state of the art, addressing both theoretical innovations and experimental breakthroughs that are paving the way for a secure quantum internet.

Dr. Dawei Wang
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics 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 key distribution (QKD)
  • quantum networks
  • quantum internet
  • quantum repeaters
  • entanglement distribution
  • single-photon sources
  • quantum cryptography
  • satellite QKD
  • quantum memories
  • integrated quantum photonics

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Published Papers (1 paper)

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Research

32 pages, 4400 KB  
Article
Research on Space-Time Data Prediction Model of Quantum Long Short-Term Memory Network Fusion
by Bing Han, Jian Kang, Meng Zhang and Qian Wu
Photonics 2026, 13(5), 477; https://doi.org/10.3390/photonics13050477 - 11 May 2026
Viewed by 427
Abstract
This study proposes a novel hybrid prediction model (QGCN-LSTM) that combines Quantum Graph Convolutional Networks (QGCN) with classical Long Short-Term Memory (LSTM). The model takes space-time data as input and employs a hierarchical graph-based quantum encoding strategy. Specifically, classical spatial features are first [...] Read more.
This study proposes a novel hybrid prediction model (QGCN-LSTM) that combines Quantum Graph Convolutional Networks (QGCN) with classical Long Short-Term Memory (LSTM). The model takes space-time data as input and employs a hierarchical graph-based quantum encoding strategy. Specifically, classical spatial features are first aggregated into critical regional hubs, which are then mapped into the Hilbert space through a dense quantum encoding layer. Multi-scale features are extracted through the collaborative computation of QGCN and quantum gated recurrent units, and a quantum attention module is introduced to dynamically screen key information. Finally, the prediction results are generated through quantum measurement and a classical output layer. In the space-time data prediction task of urban traffic flow, a benchmark model system covering classical, cutting-edge, and traditional architectures was constructed. The experimental results show that QGCN-LSTM utilizes quantum entanglement gates to establish non-local road network associations, dynamically allocate feature weights to enhance the impact of critical time steps, and achieves deep compression of lines through quantum line pruning technology, effectively alleviating the common problem of “poor plateau” in quantum neural network training. In terms of prediction accuracy, the mean absolute error (MAE) of its key hub nodes is reduced by 34.1% compared to the graph convolution LSTM (GCN-LSTM) model, and the Spatial Correlation Index (SCI) is improved to 0.89. In addition, it also shows excellent performance in dynamic response, edge computing efficiency, and other aspects, meeting the real-time requirements of the traffic signal control system. This study provides an effective paradigm for the application of quantum collaborative architecture in complex spatiotemporal prediction tasks. Full article
(This article belongs to the Special Issue Recent Progress in Quantum Communication)
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