The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) is reshaping computational paradigms, creating the need for a new class of system software capable of managing hybrid workloads. This paper presents Q-OS, a Quantum-Aware Operating System designed to orchestrate hybrid quantum-classical AI pipelines across cloud and edge environments. Q-OS introduces three novel system components: a Quantum Scheduling Kernel that intelligently allocates portions of AI workloads (such as optimization or sampling) to quantum co-processors; a Quantum Resource Virtualization Layer that abstracts quantum devices as logical accelerators; and a Quantum-AI Compiler Interface that dynamically maps AI operations from frameworks such as TensorFlow and PyTorch into executable quantum kernels. Experimental evaluations using hybrid workloads-including large language model inference (The Pile dataset), quantum-classical generative adversarial networks (CelebA dataset), and reinforcement learning control (OpenAI Gym)-demonstrate that Q-OS achieves up to 32% reduction in end-to-end latency and 27 % improvement in energy efficiency compared to classical distributed systems. The results indicate that Q-OS establishes a scalable foundation for the next generation of intelligent computing infrastructures, bridging quantum and AI ecosystems for real-world deployment.
The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) is reshaping computational paradigms, creating the need for a new class of system software capable of managing hybrid workloads. This paper presents Q-OS, a Quantum-Aware Operating System designed to orchestrate hybrid quantum-classical AI pipelines across cloud and edge environments. Q-OS introduces three novel system components: a Quantum Scheduling Kernel that intelligently allocates portions of AI workloads (such as optimization or sampling) to quantum co-processors; a Quantum Resource Virtualization Layer that abstracts quantum devices as logical accelerators; and a Quantum-AI Compiler Interface that dynamically maps AI operations from frameworks such as TensorFlow and PyTorch into executable quantum kernels. Experimental evaluations using hybrid workloads-including large language model inference (The Pile dataset), quantum-classical generative adversarial networks (CelebA dataset), and reinforcement learning control (OpenAI Gym)-demonstrate that Q-OS achieves up to 32% reduction in end-to-end latency and 27 % improvement in energy efficiency compared to classical distributed systems. The results indicate that Q-OS establishes a scalable foundation for the next generation of intelligent computing infrastructures, bridging quantum and AI ecosystems for real-world deployment.
The exponential growth of data-driven systems has outpaced the computational capacity of conventional cloud infrastructures, demanding new paradigms of intelligence and optimization. This research introduces a quantum-enabled cloud intelligence framework that integrates post-classical optimization methods with data-centric artificial intelligence pipelines. The study investigates how quantum-inspired and hybrid quantum–classical models can enhance large-scale optimization, feature extraction, and decision processes within distributed cloud environments. By coupling quantum approximate optimization algorithms (QAOA) and variational quantum circuits (VQC) with classical data analytics layers, the framework achieves reduced computational latency, improved convergence for high-dimensional problems, and enhanced interpretability of AI-driven outcomes. A prototype quantum-cloud orchestration layer is developed to dynamically allocate quantum resources through cloud APIs, enabling adaptive workload balancing across heterogeneous architectures. Empirical results from simulation and hybrid execution demonstrate significant performance gains over classical baselines in terms of both efficiency and solution quality. Beyond performance metrics, the study emphasizes ethical and sustainable computing principles, ensuring that quantum acceleration aligns with transparent, explainable, and accountable AI operations. The outcomes position quantum-enabled cloud intelligence as a foundational technology for the next generation of data-centric optimization and cognitive automation.
Quantum OS v5 is a hybrid quantum-classical computational operating environment designed for real-time quantum state inference, probabilistic dynamical reconstruction, and distributed quantum orchestration. The platform combines: - weak-measurement reconstruction, - Bayesian inference, - Sequential Monte Carlo (Particle Filtering), - GRU-based recurrent neural networks, - reinforcement-learning scheduling, - distributed DAG execution, - GPU/CUDA acceleration, - photonic validation, - and quantum-control primitives. Unlike traditional operating systems, Quantum OS v5 is not intended to replace hardware-level kernels such as Linux or Windows. Instead, it acts as a high-level orchestration infrastructure specialized for: - quantum computation workflows, - probabilistic inference, - hybrid AI-assisted simulation, - and scalable distributed execution. The architecture integrates: - asynchronous execution kernels, - resource-aware scheduling, - telemetry monitoring, - distributed cluster consensus, - sandbox isolation, - secure orchestration layers, - and modular plugin extensibility. Quantum OS v5 enables operational reconstruction of latent quantum dynamical structures from weak observational streams without violating standard quantum mechanics or uncertainty principles. The system establishes a computational bridge between: - quantum mechanics, - information theory, - machine learning, - distributed systems engineering, - and hybrid computational infrastructures. Potential applications include: - quantum state estimation, - adaptive quantum control, - quantum error correction, - photonic quantum computing, - AI-assisted quantum diagnostics, - distributed quantum telemetry, - and next-generation hybrid quantum-classical computing systems. Official Website: https://quantum-os-v5.netlify.app/ Repository: https://github.com/BarackNdenga/Quantum-Trajectory-Reconstruction
Quantum computing is often treated as an experimental add-on: it is often loosely coupled to classical infrastructure through high-level interpreted languages, cloud-based orchestration and physical measurement hardware. However, future deployments in both high-performance computing (HPC) and embedded environments will demand tighter integration for lower latencies, stronger determinism, and architectural consistency. We propose a vertically integrated quantum systems architecture that treats quantum accelerators as peripheral system components. A central element is the Quantum Abstraction Layer (QAL), introduced at the kernel level of the operating system. This design enables real-time, low-latency, and high-throughput interaction between quantum and classical resources and opens the door for robust scheduling, resource management, and cross-stack optimisation. Kernel-space integration is not merely a technical curiosity—it is a necessity. For HPC, it enables scalable, heterogeneous workloads; for embedded systems, it provides a foundation for future quantum deployments far beyond scripting-based control interfaces. We present early-stage results including a virtual QPU model integrated into a QEMU-based prototype system. The architecture is validated through functional emulation and time-accurate FPGA simulations, enabling realistic evaluation of hybrid system performance and quantum advantage scenarios. Our work lays the groundwork for a system-level co-design methodology tailored for the next generation of quantum-classical computing.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
Quantum computers are often treated as experimental add-ons that are loosely coupled to classical infrastructure through high-level interpreted languages and cloud-like orchestration. However, future deployments in both, high-performance computing (HPC) and embedded environments, will demand tighter integration for lower latencies, stronger determinism, and architectural consistency, as well as to implement error correction and other tasks that require tight quantum-classical interaction as generically as possible. We propose a vertically integrated quantum systems architecture that treats quantum accelerators and processing units as peripheral system components. A central element is the Quantum Abstraction Layer (QAL) at operating system kernel level. It aims at real-time, low-latency, and high-throughput interaction between quantum and classical resources, as well as robust low-level quantum operations scheduling and generic resource management. It can serve as blueprint for orchestration of lowlevel computational components “around” a QPU (and inside a quantum computer), and across different modalities. We present first results towards such an integrated architecture, including a virtual QPU model based on QEMU. The architecture is validated through functional emulation on three base architectures (x86_64, ARM64, and RISC-V), and timingaccurate FPGA-based simulations. This allows for a realistic evaluation of hybrid system performance and quantum advantage scenarios. Our work lays the ground for a system-level co-design methodology tailored for the next generation of quantum-classical computing.
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.