QuCryptNLP a novel quantum-inspired transformer architecture that performs convolution on encrypted or obfuscated textual data during sentiment analysis and threat detection in cases where prior decryption is not possible, reducing key privacy and security requirements within natural language processing. QuCryptNLP allows for the derivation of statistical and contextual features from encoded inputs, thus depending on sentiment analysis and threat identification under strict confidentiality protocols by merging quantum feature mapping techniques with transformer architectures. The hybrid paradigm makes use of both classical deep learning and quantum-inspired representations, making it compatible with current classical hardware and future quantum systems. The crucial innovation of QuCryptNLP lies in our avoidance of conventional decryption schemes thereby enabling a transformative methodology for accessing sensitive text corpora from privacy-sensitive domains like defense, healthcare, and finance. Such skill is particularly essential in the face of burgeoning data privacy provisions, which demand analytic methods that keep security boundaries intact.
QuCryptNLP a novel quantum-inspired transformer architecture that performs convolution on encrypted or obfuscated textual data during sentiment analysis and threat detection in cases where prior decryption is not possible, reducing key privacy and security requirements within natural language processing. QuCryptNLP allows for the derivation of statistical and contextual features from encoded inputs, thus depending on sentiment analysis and threat identification under strict confidentiality protocols by merging quantum feature mapping techniques with transformer architectures. The hybrid paradigm makes use of both classical deep learning and quantum-inspired representations, making it compatible with current classical hardware and future quantum systems. The crucial innovation of QuCryptNLP lies in our avoidance of conventional decryption schemes thereby enabling a transformative methodology for accessing sensitive text corpora from privacy-sensitive domains like defense, healthcare, and finance. Such skill is particularly essential in the face of burgeoning data privacy provisions, which demand analytic methods that keep security boundaries intact.
Purpose This study aims to develop and validate a quantum-enhanced behavioral security framework that integrates a lightweight quantum obfuscation layer into classical intrusion detection systems (IDSs). The primary objective is to harden these models against post-quantum threats, specifically model extraction and feature inversion attacks, while maintaining high detection accuracy across both traditional machine learning and deep learning architectures. Design/methodology/approach A quantum feature obfuscation layer was designed using 6-qubit parameterized circuits with angle encoding and variational ansatz to nonlinearly transform behavioral features. This layer was integrated with classical classifiers (random forest, support vector machine, logistic regression) and a deep learning model multi-layer perceptron (MLP). The framework was evaluated on UNSW-NB15 and NSL-KDD datasets using Qiskit Aer simulators in Google Colab, measuring accuracy, precision, recall, F1-score, model extraction error (MEE) and feature inversion error (FIE). Findings Quantum-assisted models maintained detection performance comparable to classical baselines, with accuracy degradation of 0.7% across all architectures. Security resilience significantly improved, with a 3–4× increase in both MEE and FIE, indicating substantially enhanced resistance to model theft and feature reconstruction. The framework demonstrated improved noise tolerance under Gaussian perturbations. The MLP achieved 92.5% accuracy (classical) versus 91.8% (quantum-assisted), with 3.3× and 3.5× improvements in MEE and FIE, respectively. Research limitations/implications The evaluation was conducted entirely on quantum simulators, not capturing real hardware noise, decoherence and fidelity limitations. The threat model assumes secrecy of quantum circuit parameters, representing a form of model-level security through obscurity. Scalability is constrained by exponential simulation costs, limiting current experiments to 6 qubits. Only feedforward neural networks (MLP) were tested; advanced architectures like CNNs, LSTMs and transformers require future validation. The framework’s resilience against adversaries with partial knowledge of the ansatz structure remains unexplored. Practical implications The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware. With only 15–25 ms inference overhead and 40–60% training overhead, it offers viable post-quantum hardening for security-critical applications. Resource-constrained environments such as edge computing nodes and IoT deployments can benefit from this approach. The demonstration that meaningful security gains (3–4× improvement) are achievable using only 6 qubits lowers the barrier for organizations to adopt quantum-assisted security measures today. Social implications As quantum computing threatens classical cryptography, protecting behavioral analytics becomes crucial for critical infrastructure, financial systems and healthcare networks. This research contributes to building resilient cyber defense mechanisms that protect sensitive data and privacy even when encryption is compromised. By democratizing access to quantum-enhanced security through cloud-based simulation platforms, the framework helps bridge the gap between institutions with varying resources, promoting more equitable cybersecurity preparedness for the post-quantum era across both developed and developing nations. Originality/value This work shifts the focus of quantum-enhanced security from pure cryptographic replacement to model-hardening, harnessing quantum state complexity as a defensive mechanism rather than pursuing quantum advantage for classification speed. It introduces a novel quantum obfuscation layer specifically designed for IDS, validated across both traditional ML and deep learning architectures. The consistent security improvements (3–4×) across classifier types confirm the approach is classifier-agnostic. The Colab-based implementation ensures reproducibility and accessibility, providing a practical foundation for future quantum-aware cyber defense research and deployment.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
Sentiment analysis (SA) is a critical component of Natural Language Processing (NLP), particularly for automatic text classification. However, previous approaches have shortcomings in capturing nuances such as negation, word pairings, and contextual understanding, particularly in languages like Hindi. To solve these issues, this research offers a novel Quantum Neural Network (QNN) technique designed specifically for sentiment analysis in Hindi text data, which employs quantum computing concepts to capture linguistic nuances and context better. This study uses a Quantum Variational Auto Encoder to encode classical data into a quantum form, capturing diverse sentiments like sarcasm and colloquial expressions. A Tuned Quantum Convolutional Neural Network architecture is introduced to capture complex linguistic syntax. A novel Sequential-based hyperband optimization technique is used to enhance model performance. The hybrid approach significantly improves accuracy and efficiency in handling quantum data, contributing to the advancement of SA, particularly in Hindi Movie Reviews. The findings demonstrate that the proposed strategy performs best with accuracy 97.64 %, precision 85.93%, recall 99.17%, F-1 score 92.20%, than other accepted strategies.
This paper presents SentiQNN (Sentiment Quantum Neural Network), a novel quantum-inspired neural network architecture specifically designed for sentiment analysis tasks in natural language processing. SentiQNN leverages quantum computing principles including superposition, entanglement, and quantum measurement to enhance traditional neural network capabilities for text classification. The model incorporates complex-valued embeddings, quantum mixture operations, and multi-task learning with sentiment lexicon integration to achieve superior performance in sentiment classification tasks. Through extensive experimentation across multiple benchmark datasets, SentiQNN demonstrates state-of-the-art performance with 88.11% accuracy, outperforming traditional classical approaches by 3-7%. The architecture introduces innovative quantum state preparation layers, quantum circuit operations, and quantum measurement mechanisms specifically optimized for natural language understanding. Our comprehensive evaluation reveals that quantum-inspired approaches can significantly enhance sentiment analysis performance while maintaining computational efficiency. The findings contribute to the emerging field of quantum machine learning and provide a foundation for future research in quantum-enhanced natural language processing systems.
With the rise of Quantum Natural Language Processing (QNLP) for complex language tasks, ensuring data privacy and security has become a critical challenge. Traditional NLP relies on vast sensitive textual data, posing risks such as data leakage and compliance issues. While quantum computing enhances efficiency and expressiveness, it also introduces new privacy concerns. This paper explores the intersection of QNLP and privacy-preserving techniques, examining vulnerabilities, adapting classical privacy methods to quantum settings, and investigating emerging quantum-based solutions. By addressing key challenges, we aim to bridge the gap between quantum advancements and secure NLP, fostering robust and privacy-compliant QNLP models.
The convergence of quantum computing, blockchain technologies, and contextual artificial intelligence offers a transformative opportunity to meet the growing demands of secure, adaptive, and decentralized systems. However, most existing approaches focus on isolated or pairwise integrations of these technologies and do not provide a comprehensive architectural synthesis. This paper introduces a quantum-classical hybrid architecture that unifies quantum-enhanced cryptographic processing, blockchain-based trust mechanisms, and context-aware AI reasoning within a modular and interoperable system design. The proposed architecture consists of four interoperable layers: a quantum layer for cryptographic functions and computational enhancements, a classical layer for coordination and orchestration, a contextual AI layer for real-time inference and adaptation, and a blockchain layer for transparent and tamper-resistant record keeping. A key innovation of this architecture lies in its use of contextual AI models, such as transformers, to interpret dynamic environments and guide decisions across quantum and blockchain components. This paper presents use cases in healthcare, finance, and supply chain domains which are theoretical, yet based on realistic challenges and operational demands. These scenarios illustrate how each layer of the architecture contributes to practical decision-making, enhancing the feasibility of future deployments. The paper also discusses implementation challenges such as quantum hardware limitations, integration complexity, and data privacy, and outlines a trajectory for advancement through quantum middleware, explainable AI, and post-quantum cryptographic integration. By aligning emerging technologies within a unified structure, this study offers a forward-looking blueprint for secure, intelligent, and context-aware systems in decentralized environments.