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Review
. 2025 Jun 21:15:103445.
doi: 10.1016/j.mex.2025.103445. eCollection 2025 Dec.

Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security

Harshala Shingne  1   2 , Diptee Chikmurge  3 , Priya Parkhi  4   5 , Poorva Agrawal  1   2
Affiliations
Review

Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security

Harshala Shingne et al. MethodsX. .

Abstract

The need for secure communication systems has driven extensive research into quantum-based security mechanisms, particularly Quantum Key Distribution (QKD). However, traditional QKD systems, within dynamic environments incorporating network fluctuation and attacks, have been relatively limited because static protocols cannot support high key generation rates and security. This work addresses these challenges by proposing the integration of AI and machine learning optimization techniques into quantum communication protocols to enhance both security and efficiency. We here propose three advanced models: first, Deep Reinforcement Learning is applied to adaptively optimize QKD protocols by dynamically adjusting the key generation parameters with respect to environmental conditions. In the state-of-the-art method, the DRL-based approach enlarges the secure key generation rate by 15-20 % and suppresses QBER 30-40 % under noisy conditions. A VAE is used for the detection of anomalies in quantum networks that effectively detects eavesdropping. By incorporating quantum-specific feature extraction and latent variable disentanglement, the VAE model detects attack detection accuracy of 85-90 % with a reduction of 25 % in false positives. Finally, it considers the optimization of cryptographic protocols in a distributed quantum network using Multi-Agent Deep Q-Networks. This multi-agent system strengthens both the security and computational efficiency by reducing attack vulnerabilities by 15-18 % and lowering the computational complexity by 20-25 %. In all, the integration of AI with machine learning methods brings far better enhancements in the field of quantum communication system security and efficiency, addressing critical limitations of conventional QKD systems and pointing to the way to more resilient adaptive quantum security solutions.

Keywords: Deep Reinforcement Learning for Quantum Communication Device QKD Optimization; Deep reinforcement learning; Multi-agent systems; Quantum cryptography; Quantum key distribution; Variational autoencoder.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig 1
Fig. 1
Model architecture of the proposed quantum security analysis.
Fig 3
Fig. 3
Secure key generation rate comparison (kbps).
Fig 4
Fig. 4
Quantum bit error rate (QBER) comparison ( %) Sets.
Fig 5
Fig. 5
False positive rate in anomaly detection ( %) process.
Fig 2
Fig. 2
Overall flow of the proposed analysis process.
See this image and copyright information in PMC

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