Quantum Bayesian inference with Suport vector states for intrusion detection

TL;DR

This paper introduces a quantum Bayesian inference approach utilizing support vector states and quantum circuits for intrusion detection, demonstrating its feasibility and interpretability in security scenarios.

Contribution

It presents a novel quantum Bayesian inference method with explicit circuit construction and symbolic post-selection for intrusion detection applications.

Findings

Successfully encodes probabilities via unitary gates

Extracts posterior distributions through symbolic post-selection

Achieves aligned joint, marginal, and conditional probabilities in security scenarios

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