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Signals and communication technology

Hybrid Quantum–Classical Learning Architectures for 6G/7G Edge Intelligence

Anushikha Singh, Inam Ul Haq, Nehul Jain, Rohit Kumar, Mushtaq Ahmad Rather, Faheem Syeed Masoodi

Published Jan 1, 2026• 1 citation
Publisher
DOI
10.1007/978-981-95-1683-4_14

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The subject of this bachelor thesis was the investigation and proposal of efficient methods for integrating Quantum Machine Learning (QML) techniques into Edge Computing environments of modern 6G networks.The primary objective of the study was to overcome the computational and energy constraints faced by devices at the network edge when executing traditional artificial intelligence algorithms.In the context of this work, the 6G network architecture was analyzed, and the requirements for real-time sensor data processing were identified.The core principles of quantum computing were studied, and modern QML algorithms were evaluated, focusing on hybrid classical-quantum models compatible with NISQ-era devices.The methodology followed was based on the design of Parameterized Quantum Circuits (PQCs), applying optimization techniques such as angle encoding and circuit pruning to minimize circuit depth and power consumption.To evaluate the proposed architecture, simulations were conducted using the PennyLane and Qiskit frameworks.The results demonstrated that the use of quantum co-processors at the network edge can offer a significant reduction in the number of required parameters compared to classical neural networks, while ensuring high accuracy in data classification.In conclusion, the thesis highlighted that quantum computing can be a catalyst for the sustainability of Edge AI in 6G, providing an energy-efficient solution for devices with limited computational resources.
ABSTRACT Quantum‐native communication systems are reshaping secure, programmable, and coherence‐aware networking beyond the classical paradigm. As intelligent physical infrastructures, ranging from unmanned aerial vehicle swarms to satellite Quantum Key Distribution (QKD) platforms, demand trust, synchronization, and ultra‐reliability, conventional protocol designs struggle to preserve entanglement and coherence while coordinating control logic across hybrid domains. This paper surveys layered protocol architectures and secure control frameworks designed for post‐6G and emerging 7G+ quantum‐enabled networks. Quantum‐classical hybrid denotes networks in which quantum links used for entanglement distribution or QKD interoperate with classical links and protocols within a unified communication fabric. We develop a taxonomy of the protocol stack, spanning the entanglement‐aware physical layer to the service‐level agreement (SLA)‐driven application layer, and covering representative technologies such as Q‐SDN (quantum software‐defined networking), quantum transmission Control Protocol, hybrid QKD and PQC (post‐quantum cryptography) integration, and intent‐driven orchestration through large language model agents. We revisit security using blockchain‐based trust anchors and fidelity‐aware metadata frameworks, and examine quantum synchronization and teleportation‐resilient session‐control mechanisms for preserving inter‐digital‐twin coherence. Through a broad review of architectural blueprints, protocol proposals, use cases, and simulation‐driven models, this paper positions quantum‐native protocol stacks as a foundation for secure, resilient, and intelligent communication systems.