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.
Technological advances in neutral-atom platforms have opened a new path toward designing efficient protocols for fault-tolerant quantum computing (FTQC). However, each physical operation is still orders of magnitude slower than on other platforms, such as superconducting qubits. Therefore, architects must identify fast and efficient FTQC architectures, which require reliable modeling tools to explore various design choices across the software, classical hardware, and quantum device stacks of neutral-atom platforms.
In this paper, we propose NAQsim, an open-source simulation framework for neutral-atom FTQC architectures based on transversal gates. As its key feature, NAQsim enables detailed full-stack architecture evaluation that opens opportunities to explore previously overlooked performance bottlenecks. As a first use case for NAQsim, we identify one such bottleneck, patch rotations, perform full-stack co-optimization, and finally derive a near-rotation-free architecture (D3-ROT). For practical FTQC benchmark workloads, D3-ROT achieves a 2.27x speedup from this single bottleneck alone, with minimal footprint overhead. These results point to a much broader space of full-stack optimizations that NAQsim makes accessible for transversal surface-code architectures and beyond.
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.