Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy

Abstract

This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks (QNNs) in the Noisy Intermediate-Scale Quantum (NISQ) era. In this paper, we present an integrated framework that leverages and combines existing approaches to enhance QNN efficiency, security, and privacy. Specifically, established optimization strategies, including efficient parameter initialization, residual quantum circuit connections, and systematic quantum architecture exploration, are integrated to mitigate issues such as barren plateaus and error propagation. Moreover, the methodology incorporates current defensive mechanisms against adversarial attacks. Finally, Quantum Federated Learning (QFL) is adopted within this framework to facilitate privacy-preserving collaborative training across distributed quantum systems. Collectively, this synthesized approach seeks to enhance the robustness and real-world applicability of QNNs, laying the foundation for reliable quantum-enhanced machine learning applications in finance, healthcare, and cybersecurity.

4 pages, 6 figures. Accepted at IOLTS 2025

References in corpus (10)

• Quantum Computing in the NISQ era and beyond
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• FedQNN: Federated Learning using Quantum Neural Networks
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• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
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Cited by in corpus (2)

• HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
• IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring