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
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.
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.
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone. Coordinating these heterogeneous resources at scale demands robust orchestration, reproducibility, and observability. Even in the presence of fault-tolerant quantum devices, quantum computing will continue to operate within a broader hybrid ecosystem, where classical infrastructure plays a central role in task scheduling, data movement, error mitigation, and large-scale workflow coordination.
In this work, we present a cloud-native framework for managing hybrid quantum-HPC pipelines using Kubernetes, Argo Workflows, and Kueue. Our system unifies CPUs, GPUs, and QPUs under a single orchestration layer, enabling multi-stage workflows with dynamic, resource-aware scheduling. We demonstrate the framework with a proof-of-concept implementation of distributed quantum circuit cutting, showcasing execution across heterogeneous nodes and integration of classical and quantum tasks. This approach highlights the potential for scalable, reproducible, and flexible hybrid quantum-classical computing in cloud-native environments.
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.
This paper has a full-scale reference architecture for hybrid quantum-classical learning to include the integration of classical representation models with quantum neural networks (QNN), quantum support vector machines (QSVM), quantum principal component analysis (qPCA), variational quantum circuits (VQC), and QAOA-based combinatorial optimization to create next-generation predictive business intelligence and complex decision optimization subject to all real world operating constraints. The proposed structure has a layered system consisting of; (1) Quantum aware data encoding techniques through Amplitude, Basis and Angle Mapping; (2) Differentiable Hybrid Learning Pipelines optimized using Parameter Shift Gradient Methods across Simulators and Managed Quantum Processing Units; (3) Prescriptive Workflows that reformulate Multi-Objective Business Problems such as Supply Chain Coordination, Routing with Time Windows and Portfolio Construction into QUBO Representations Supported by Convex Warm Starts and Classical Post-Processing to Guarantee Feasibility and Explainability. An End-To-End Orchestration Layer will manage Circuit Templates, Data Hashes, Model Versions and Backend Execution Metadata to Provide Reproducibility, Traceability, Governance Compliance Across All Enterprise Pipelines. A Systematic Evaluation Protocol Will be Defined to Isolate Quantum Contribution Through Systematic Ablation Analysis of Classical-Only, Quantum-Only, and Hybrid Configurations Combined With Rolling Window Validation, Latency Profiling, Cost Efficiency Analysis, and Carbon Impact Accounting. Industry Standard Time Series and Probabilistic Accuracy Measures Including sMAPE, MASE, CRPS and Calibration Consistency Will Be Used to Evaluate the Performance of Predictive Models. Optimization Quality Will Be Evaluated Using Optimality Gap, Constraint Violation Rates, Time to Feasible Solution and Downstream Business Impact on Profit Margins and Service Levels. An Interpretability Layer Will Provide Transparency by Mapping Quantum Feature Spaces, Circuit Observables, and Kernel Alignment Structures to Domain-Specific Reasoning to Build Stakeholder Trust, Auditability and Regulatory Alignment. In Addition, The Framework Will Incorporate Adaptive Decision-Making Via Quantum Reinforcement Learning to Allow Continuous Optimization Under Dynamic and Uncertain Business Environments. An Energy Aware Execution Layer Will Dynamically Route Workloads Between Classical Simulators and Quantum Hardware According to Predefined Energy Budgets, Carbon Thresholds, and Performance Demands. The Overall Framework Provides a Scalable, Auditable and Ethically Aligned Blueprint for Deploying Hybrid Quantum-Classical Intelligence Within Existing Enterprise Ecosystems for Applications Such as Financial Forecasting, Retail Analytics, Dynamic Pricing, Market Risk Modeling, and Supply Chain Optimization.
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.