The mesmerising evolution of Generative Artificial Intelligence (Gen.AI) has transformed knowledge management, yet evaluating its reliability and contextual precision remains a critical challenge. This paper proposes GenZ-AIQuanta, a hybrid quantum–classical (Q–C) evaluation model designed for text-to-text generative AI systems. The model integrates classical linguistic metrics—BLEU (Biilingual Evaluations Study) and BERT.Score (Bi-direction Encoder Representation from Transformer Score)—with quantum-derived semantic measures such as QSS (Quantum Semantic Similarity) and QSF (Quantum State Fidelity). Implemented using Python, Qiskit, and HuggingFace Transformers, the system enables multidimensional analysis via a hybrid aggregation engine. Experimental outcomes exhibit significant improvements in accuracy, contextual coherence, and explainability. Moreover, the EC (Explainability Coefficient) highlights enhanced interpretive reliability across evaluation layers. The proposed Q–C hybrid architecture establishes a scalable, explainable, and quantum-enhanced foundation for assessing next-generation GenAI models within knowledge-centric environments.
The mesmerising evolution of Generative Artificial Intelligence (Gen.AI) has transformed knowledge management, yet evaluating its reliability and contextual precision remains a critical challenge. This paper proposes GenZ-AIQuanta, a hybrid quantum–classical (Q–C) evaluation model designed for text-to-text generative AI systems. The model integrates classical linguistic metrics—BLEU (Biilingual Evaluations Study) and BERT.Score (Bi-direction Encoder Representation from Transformer Score)—with quantum-derived semantic measures such as QSS (Quantum Semantic Similarity) and QSF (Quantum State Fidelity). Implemented using Python, Qiskit, and HuggingFace Transformers, the system enables multidimensional analysis via a hybrid aggregation engine. Experimental outcomes exhibit significant improvements in accuracy, contextual coherence, and explainability. Moreover, the EC (Explainability Coefficient) highlights enhanced interpretive reliability across evaluation layers. The proposed Q–C hybrid architecture establishes a scalable, explainable, and quantum-enhanced foundation for assessing next-generation GenAI models within knowledge-centric environments.
Quantum Computing and Generative Artificial Intelligence are transforming the future of intelligent computing systems.The integration of quantum computing with generative AI introduces a new paradigm called Quantum-Enhanced Generative AI (QEGAI), which utilizes quantum mechanical principles such as superposition, entanglement, and quantum parallelism to improve computational efficiency, model training, optimization, and intelligent decision-making.Classical generative AI models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs) demand extensive computational resources and energy consumption.Quantum-enhanced techniques address these limitations by accelerating optimization and enabling faster processing of complex datasets.This paper presents an in-depth study of QEGAI architectures, working principles, applications, advantages, limitations, and future research directions.The proposed framework combines quantum processors with classical AI infrastructure to create scalable and intelligent next-generation computing environments.The study also explores the role of quantum machine learning, hybrid quantum-classical architectures, cybersecurity, healthcare, smart cities, and scientific simulations.Comparative analysis demonstrates the superior computational capabilities of QEGAI over traditional AI approaches in selected tasks.Finally, the paper discusses implementation challenges such as qubit instability, noise, hardware scalability, and ethical concerns while outlining future opportunities for sustainable intelligent computing systems.
This paper presents a comprehensive evaluation of quantum text generation models against traditional Transformer/MLP architectures, addressing the growing interest in quantum computing applications for natural language processing. We conduct systematic experiments comparing five distinct models: Transformer (baseline), Quantum Kernel Self-Attention Network (QKSAN), Quantum RWKV (QRWKV), and Quantum Attention Sequence Architecture (QASA) across five diverse datasets including simple sentences, short stories, quantum phrases, haiku poetry, and proverbs. Our evaluation employs multiple metrics including perplexity, BLEU scores, vocabulary diversity, repetition rates, and fluency measures to assess different aspects of text generation quality. The experimental results reveal that while traditional Transformer models maintain overall superiority with the lowest average perplexity (1.21) and highest BLEU-1 score (0.2895), quantum-inspired models demonstrate competitive performance in specific scenarios. Notably, QKSAN achieves a competitive BLEU-1 score of 0.2800 while maintaining zero repetition rates, and QRWKV demonstrates perfect vocabulary diversity (Distinct-1 = 1.000) in certain tasks.
The convergence of Quantum Computing and Generative Artificial Intelligence (AI) marks a transformative leap in the field of drug discovery. Traditional drug development pipelines are often slow, costly, and limited by classical computational constraints. This paper presents a novel hybrid approach—QuantumGeneraDrug—that integrates quantum generative models with deep learning architectures to accelerate molecule generation, optimize binding affinities, and reduce computation time. A comprehensive literature survey highlights the evolution of both quantum algorithms and generative AI models applied to pharmaceutical research. The proposed system demonstrates improved performance over existing frameworks in key metrics such as valid molecule rate, druglikeness (QED) score, binding affinity accuracy, and processing time per compound. Comparative results are illustrated using numerical tables and visualized via bar and pie charts. The study concludes that Quantum Generative AI holds immense promise for next-generation drug discovery, especially in tackling complex diseases and enabling personalized medicine.
This chapter examines the convergence of quantum computing and artificial intelligence through the emerging field of Quantum Artificial Intelligence (QAI). The discussion explains how quantum principles such as superposition, entanglement, and quantum interference can enhance machine learning, generative AI, optimization, and intelligent systems beyond the limitations of classical computing. The chapter presents concepts of quantum computing, quantum machine learning, quantum neural networks, and hybrid quantum-classical architectures while also discussing applications in healthcare, cybersecurity, finance, manufacturing, and scientific research. In addition, the chapter addresses challenges related to scalability, explainability, cybersecurity, governance, ethics, and unequal access to quantum technologies. The chapter also introduces a conceptual QAI Ecosystem Model and outlines future research directions involving Quantum-enhanced Explainable AI, agentic AI, quantum internet systems, and quantum-safe cybersecurity.
Generative AI has emerged as a transformative paradigm for diverse applications, yet the escalating scale of modern models exposes critical computational and memory bottlenecks in classical hardware. This paper investigates the intersection of quantum computing and generative artificial intelligence (QGAI) to address these limitations and scale modern generative models. As models grow to billions of parameters, classical systems face bottlenecks in memory, energy, and training efficiency, while quantum computing offers exponential representational benefits for high-dimensional data. The paper analyzes five core quantum generative architectures-Quantum Circuit Born Machines, Quantum Generative Adversarial Networks, Quantum Boltzmann Machines, Quantum Variational Autoencoders, and Quantum Diffusion models, highlighting their design principles, learning mechanisms, and applications. QGAI models have demonstrated significant promise in domains such as drug discovery, human-machine interaction, IoT security, and financial modelling. Despite these advances, QGAI remains constrained by qubit noise, barren plateaus, and integration challenges. We conclude by identifying ten open research challenges and propose directions for achieving scalable, interpretable, and energy-efficient quantum generative learning.
The advent of Generative Artificial Intelligence (GenAI) has significantly impacted various industries, including healthcare. This paper presents an advanced Healthcare Chatbot leveraging Large Language Models (LLMs) for preliminary disease diagnosis. Traditional healthcare systems, especially in remote areas, struggle with accessibility and timely intervention. By integrating GenAI models, our chatbot enhances patient interactions, symptom analysis, and early medical insights. Additionally, we use a parameterized quantum circuit to integrate Quantum Machine Learning-based embeddings in order to improve semantic retrieval and alleviate the dimensionality bottleneck of classical embeddings. By introducing entanglementbased encoding, this quantum-enhanced vectorization improves robustness and generalization in low-dimensional latent space. This kind of integration shows how QML and GenAI work together to create healthcare dialogue systems that are effective, scalable, and contextually aware.