Advances in Quantum Machine Learning, Information Theory, and Quantum Photonics

Dear Colleagues,

Quantum computing integrates quantum physics with computer science and is advancing computation, information processing, and machine learning. Although quantum devices use terms familiar from classical architectures such as registers, gates, and memory, their physical implementations are fundamentally different. Qubits support superposition and entanglement, allowing a computation to explore multiple paths within a single execution and enabling potential speedups in linear algebra operations that underlie learning and inference. The advent of NISQ processors has accelerated research on heuristic and hybrid quantum machine learning, yet device size and noise still require workflows that combine quantum and classical resources. Quantum information theory provides the formal basis, including entanglement theory, capacity results, coding, and error correction, and quantum photonics supplies practical platforms through integrated photonic circuits, deterministic and indistinguishable single photon sources, and efficient detectors.

This Special Issue focuses on advances in quantum machine learning (QML), quantum information theory (QIT), and quantum photonics, with attention to circuits, algorithms, implementations, benchmarking, and practical applications. We welcome original research articles and reviews that span theory and experiment, including work on hybrid quantum and classical computing and machine learning.

Topics of interest include, but are not limited to, the following:

• QML algorithms and theory covering variational, kernel, and generative models and analyses of expressivity, generalization, and noise;
• Quantum federated and distributed learning addressing privacy-preserving protocols, communication-efficient aggregation, quantum data encoding across nodes, and convergence;
• Optimization and training for QML, including barren plateau mitigation, noise-aware gradients, initialization strategies, and landscape characterization;
• Hybrid quantum and classical workflows emphasizing algorithm hardware codesign, resource estimation, hardware-efficient ansätze, orchestration, and end-to-end pipelines;
• Quantum information theory and resources encompassing entanglement, resource theories, nonlocality, quantum channels, coding theorems, and capacity bounds;
• Quantum error correction and fault tolerance covering stabilizer and LDPC codes, decoding algorithms, threshold estimation, and logical qubit implementations;
• Photonic quantum computing and integrated photonics addressing measurement based computation, cluster state generation, photon sources and detectors, and interferometer design;
• Quantum communication and networking, including QKD, repeaters, entanglement distribution, network protocols, and early quantum internet primitives;
• Applications and case studies spanning chemistry and materials, optimization, finance, healthcare, security, sensing, and metrology on real or emulated hardware.

We look forward to receiving your contributions.

Dr. Chao Ren
Prof. Dr. Mikael Skoglund
Dr. Huihui Zhu
Guest Editors

Manuscript Submission Information

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• quantum machine learning
• quantum information theory
• quantum photonics
• hybrid quantum classical computing
• quantum federated and distributed learning
• quantum error correction and fault tolerance
• quantum communication and networking
• NISQ algorithms and applications