Current Volume 9
Implementation Framework of Quantum Neural Networks for Wireless Communication Applications
Abstract
Quantum computing harnesses quantum mechanics principles like superposition and entanglement to process information using qubits, which can represent multiple states simultaneously unlike classical bits. This enables exponential speedups for complex problems such as optimization, cryptography, and simulations that challenge classical computers. Conventional Quantum Neural Network (QNN) frameworks for wireless communications face significant emulation constraints, including exponential resource scaling, barren plateaus, and hardware noise limitations that hinder practical deployment. This paper reframes the existing QNN architecture—featuring variational quantum circuits trained on priority-based CSMA/CA simulation data—as a baseline system, highlighting its emulation-bound performance comparable to traditional neural networks via Euclidean/cosine similarity benchmarking. Key limitations include qubit/layer scaling issues, prolonged training with small batches, and susceptibility to decoherence, restricting scalability for 6G complexities. The proposed advancement transitions to fault-tolerant quantum hardware via Torch Quantum-Qiskit, incorporating distributed QNNs and error mitigation for MAC/physical layer optimizations like MIMO beamforming, channel estimation, interference mitigation, and access control. Benefits encompass exponential computational advantages, enhanced reliability in high-dimensional environments, and seamless edge-cloud integration, enabling superior efficiency over classical methods in future networks. This generalized methodology empowers wireless practitioners with minimal quantum expertise to achieve transformative network performance.
Keywords
Edge-cloud integration Quantum computing, Conventional Quantum Neural Network, Torch Quantum-Qiskit.
Citations
IRE Journals:
Seggam Leekshitha, M Karthikeyan, D Hemanth, S Vikram, Perugu Sivaramakrishna "Implementation Framework of Quantum Neural Networks for Wireless Communication Applications" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 323-329 https://doi.org/10.64388/IREV9I10-1715919
IEEE:
Seggam Leekshitha, M Karthikeyan, D Hemanth, S Vikram, Perugu Sivaramakrishna
"Implementation Framework of Quantum Neural Networks for Wireless Communication Applications" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715919
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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