Quantum Bayesian inference with Suport vector states for intrusion detection

2025/07/01 by Mridha, Nayema, Garrv Sipani, Eva R Gaarder +10
Computer Science · Physics and Astronomy · #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2507.00403

openalex publication_date 2025/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a quantum Bayesian inference method for intrusion detection, using explicitly constructed quantum circuits and statevector simulation. Prior and conditional probabilities are encoded via unitary gates, and posterior distributions are extracted through symbolic post-selection. Applied to a scenario with network spikes, system vulnerabilities, and false alarms, the method yields joint, marginal, and conditional probabilities aligned with causal structure. Our results demonstrate the feasibility and interpretability of quantum-native inference for information security applications

Citations

• Autoencoder-based learning of Quantum phase transitions in the two-component Bose-Hubbard model
• Quantum Bayesian Computation
• Holographic Renormalization with Machine learning

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