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arXiv:2505.01735

Brain-Inspired Quantum Neural Architectures for Pattern Recognition: Integrating QSNN and QLSTM

Eva Andrés, Manuel Pegalájar Cuéllar, Gabriel Navarro

Published May 3, 2025• 0 citations
arXivHTMLPDF

Abstract

Recent advances in the fields of deep learning and quantum computing have paved the way for innovative developments in artificial intelligence. In this manuscript, we leverage these cutting-edge technologies to introduce a novel model that emulates the intricate functioning of the human brain, designed specifically for the detection of anomalies such as fraud in credit card transactions. Leveraging the synergies of Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, our approach is developed in two distinct stages, closely mirroring the information processing mechanisms found in the brain's sensory and memory systems. In the initial stage, similar to the brain's hypothalamus, we extract low-level information from the data, emulating sensory data processing patterns. In the subsequent stage, resembling the hippocampus, we process this information at a higher level, capturing and memorizing correlated patterns. We will compare this model with other quantum models such as Quantum Neural Networks among others and their corresponding classical models.

DOI
10.48550/arxiv.2505.01735

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Recent advances in the fields of deep learning and quantum computing have paved the way for innovative developments in artificial intelligence. In this manuscript, we leverage these cutting-edge technologies to introduce a novel model that emulates the intricate functioning of the human brain, designed specifically for the detection of anomalies such as fraud in credit card transactions. Leveraging the synergies of Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, our approach is developed in two distinct stages, closely mirroring the information processing mechanisms found in the brain's sensory and memory systems. In the initial stage, similar to the brain's hypothalamus, we extract low-level information from the data, emulating sensory data processing patterns. In the subsequent stage, resembling the hippocampus, we process this information at a higher level, capturing and memorizing correlated patterns. We will compare this model with other quantum models such as Quantum Neural Networks among others and their corresponding classical models.
In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.
The field of financial security faces a hard challenge when it comes to detecting credit card fraud because accurate efficient models are required to separate fraudulent transactions from legitimate ones. Most fraud detection systems today utilize deep learning methods supported by Graph Convolutional Networks and Graph Neural Networks. Companies experience several difficulties when implementing these detection methods because they demonstrate poor scale-up potential and high implementation expenses and insufficient accuracy in complex fraud detection tasks. The research presents the Quantum Long Short-Term Memory (QLSTM) model that demonstrates potential for credit card fraud detection purposes. Despite its quantum nature QLSTM optimizes learning functions by advancing representation of data features while speeding up training time. Our model receives evaluation against GNN along with GCN framework using numerous benchmark datasets for fraud detection. Experimental findings indicate that QLSTM delivers superior performance than GNN and GCN at every stage of various measurement benchmarks. The training process of QLSTM achieves near-perfect 99% accuracy after 100 epochs making it outperform GNN and GCN significantly according to our accuracy comparison graph.
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With Deep learning and quantum computing are emerging technologies with transformative potential in financial risk assessment, optimization, and fraud detection. The integration of these fields can significantly enhance the efficiency and accuracy of financial decision-making processes. However, existing fraud detection methods often suffer from high false positives, slow processing times, and limited adaptability to complex fraud patterns. Traditional machine learning models struggle with scalability and computational efficiency when dealing with large-scale financial data. To address these issues, we propose the Quantum-Inspired Neural Networks for Fraud Detection (Q-INN-FD) framework, which leverages quantum-inspired computing principles to enhance neural network architectures. The framework incorporates quantum-inspired feature encoding, variational quantum circuits, and hybrid classical-quantum learning mechanisms to improve pattern recognition in financial transactions. These techniques enable more efficient fraud detection by enhancing model robustness and reducing computational overhead. The proposed method is applied to financial datasets to detect fraudulent transactions, optimize risk assessment models, and improve decision-making in real-time scenarios. Our findings demonstrate that Q-INN-FD achieves higher accuracy, faster processing, and reduced false positive rates compared to traditional deep learning approaches. This paper highlights the potential of quantum-inspired deep learning for revolutionizing financial security and risk management.
Credit Card Fraud Detection (CCFD) is a major concern for the financial industry, requiring rapid and accurate identification of fraudulent transactions amidst vast datasets. The rapid evolution of quantum computing presents transformative opportunities for enhancing Machine Learning (ML) applications, particularly in the domain of financial security. This paper explores the implementation of Quantum Machine Learning (QML) techniques for CCFD, a critical challenge in the financial industry characterized by imbalanced datasets. Traditional ML models, while effective, often struggle with scalability and computational efficiency when processing large-scale transactional data. QML leverages quantum phenomenon namely entanglement and superposition to potentially outperform classical algorithms in feature extraction and pattern recognition. Our study proposes a hybrid quantumclassical framework, integrating Variational Quantum Circuits (VQC) with classical Neural Networks (NN) to classify fraudulent transactions. Using a simulated quantum environment, we preprocess a publicly available credit card transaction dataset, encoding features into quantum states for processing through a parameterized quantum circuit. The model is trained to improves its ability to detect fraud accurately with fewer false positives, addressing the critical issue of customer inconvenience.
Prediction of stock price remains a challenging task because financial markets are dynamic and often non-linear, unpredictable patterns. To deal with such unpredictable data, the architecture that’s created must identify both time based trends and hidden patterns in data. In this study, we explore a hybrid modelling approach by combining both the classical deep learning techniques with quantum computing principles. Specifically, a Long Short-Term Memory (LSTM) is combined with a Quantum Neural Network (QNN) to understand the benefits of using quantum in time series forecasting. Rather than depending on a single quantum configuration, multiple feature maps and ansatz circuit designs were evaluated to identify the most suitable arrangement for the QLSTM model. Resultant framework blends ideas from both classical computing and quantum approaches to improve how we predict patterns over time in real-life scenarios.