Quantum Genetic Optimization for Negative Selection Algorithms in Anomaly Detection

Problem
This paper addresses the limitations of traditional Negative Selection Algorithms (NSAs) in anomaly detection, particularly their inefficiency in detector generation. The authors highlight that while NSAs are inspired by the human immune system’s self/non-self discrimination, their performance is often hindered by classical evolutionary optimization methods. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the Quantum Genetic Negative Selection Algorithm (QGNSA), which integrates a Quantum Genetic Algorithm (QGA) into the EvoSeedRNSA framework. The QGNSA replaces the classical evolutionary optimization process with quantum-inspired techniques, leveraging quantum superposition and probabilistic amplitude adjustment to enhance the exploration of the search space and improve convergence efficiency. The authors detail the architecture of the QGNSA, emphasizing its ability to generate detectors more effectively than classical methods. The training compute specifics are not disclosed, but the authors suggest that the quantum approach allows for a more efficient search in high-dimensional spaces.

Results
Empirical evaluations were conducted using the Metaverse Financial Transactions Dataset, where QGNSA demonstrated a significant improvement in anomaly detection accuracy compared to classical NSAs. The paper reports that QGNSA outperformed its classical counterpart by a margin of approximately 15% in detection accuracy, with robustness maintained across various hyperparameter configurations. The results indicate that the quantum-enhanced approach not only improves accuracy but also retains stability under different operational conditions, showcasing the potential of quantum computing in enhancing artificial immune systems.

Limitations
The authors acknowledge several limitations, including the current reliance on classical simulation of quantum algorithms rather than deployment on actual quantum hardware, which may affect the practical applicability of their findings. They also note that the scalability of QGNSA to larger datasets and its performance in real-time anomaly detection scenarios remain to be explored. Additionally, the paper does not address the potential overhead associated with quantum circuit design and the complexity of integrating quantum algorithms into existing systems.

Why it matters
This research has significant implications for the field of anomaly detection, particularly in high-dimensional data environments where traditional methods struggle. By demonstrating the advantages of quantum computing in enhancing the efficiency and effectiveness of NSAs, the work opens avenues for future research into hybrid quantum-classical approaches and the optimization of quantum circuits for practical applications. The findings suggest that quantum algorithms could play a crucial role in advancing artificial immune systems, potentially leading to more robust and efficient anomaly detection frameworks in various domains.

Authors: Giancarlo P. Gamberi, Calebe P. Bianchini
Source: arXiv:2605.22527
URL: https://arxiv.org/abs/2605.22527v1