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Lecture notes in networks and systems

Quantum-Enhanced Hybrid Intelligence: Fuzzy Logic Meets Neural Networks in Data-Driven Systems

Amogh Kotha Nagaraj, Rajesh Eswarawaka, Aadi B. Kumar, Sujay Dayanandini

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-13203-1_35

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Introduction. The development of quantum computing and artificial intelligence necessitates the development of hybrid quantum-classical algorithms for solving complex computational problems. The relevance of the research is due to the need for new approaches to making creative AI decisions in conditions of exhaustion of training samples. (QVA) based on weak measurements with fuzzy filtering of input data is a promising research direction. The article first proposes a quantum variational autoencoder (QVA) based on weak measurements, which expands the space of possible solutions due to quantum effects – qubit entanglement, superposition of states and information teleportation. A fundamentally important modification is the introduction of weak measurements, which provide information about the quantum system with minimal impact on its state. The purpose of the article is to improve AI through modeling of autoencoder algorithms using weak measurements and fuzzy logic. Results. For the first time, numerical simulation of KVA based on weak measurements with fuzzy filtering was performed on classical computers and cloud services. The quality of KVA reconstruction is comparable to classical autoencoders. The simulation was performed for a one-dimensional signal, since for the CIFAR-10 and MNIST training samples, the simulation requires more than 5 petabytes of RAM. The KVA runtime in Google Colab was approximately 40 seconds. Conclusions. The integration of the fuzzy filtering mechanism into the KVA structure expands the capabilities of processing distorted and incomplete data. Such a modification increases the model's resistance to thermal noise and input data artifacts, improving the quality of information compression. Fuzzy clustering allows the system to effectively operate with ambiguous situations under conditions of uncertainty. Computer simulations have shown that adapting the fuzzy membership function to the type of input data, increasing the number of latent variables, and selecting the learning rate of the neural network can improve the quality of the reconstruction of the input signal. Keywords: quantum computing, neural network, variational autoencoder, fuzzy logic, weak measurements.
The rapid evolution of malware techniques makes conventional defenses increasingly ineffective. Although classical machine learning systems improve detection, they struggle when attempting to classify malware. Approaches that integrate fuzzy logic into machine learning models, specifically neural networks, alleviate this problem. However, this solution still exhibits frequent instability in the classification process. In this thesis, we introduce a novel Quantum Deep Fuzzy Neural Network (QDFNN) that combines quantum machine learning and fuzzy logic layer, enabling probabilistic reasoning through superposition and conditional activation gates to capture complex malware patterns. We derive multiple variants of the model and evaluate them on a popular Malware Detection dataset. The fully quantum variant (QDFNN) achieves an accuracy of 93.6%, outperforming several baseline models, including a hybrid variant, H-QDFNN, a shallow variant of QDFNN (QFNN), a shallow hybrid variant, H-QFNN, a quantum non-fuzzy variant, QNN, and several classical models, such as ANN, SVM, and logistic regression. We also extend our evaluation to include other important evaluation metrics, such as precision, recall, F1 score, and ROC-AUC, for which our proposed QDFNN outperformed all other baseline models. Finally, we evaluated the resilience of the proposed model against quantum noise by adopting six NISQ noise models and comparing our model with its variants, where we observed reasonable accuracy levels of the proposed model. This thesis demonstrates that integrating quantum fuzzy logic yields a potential competitive advantage, especially against conventional classical models, paving the way toward practical quantum-based malware detection systems.