Hybrid quantum datacentres are advancing rapidly, and yet most of the efforts are pointing to hardware development, while the software layer that orchestrates classical and quantum resources remains underdeveloped. This thesis designs and implements a state aware hybrid orchestrator that receives workload requests and places them into the designated cluster. The orchestrator is built as a set of REST services organised into NorthBound, allocation, resource and Southbound blocks. The classical cluster is deployed on an OpenStack environment using DevStack, while the quantum cluster is emulated by quantum agents. Two placement algorithms are implemented, First Fit and Load Balancing. Both operate over both clusters, they filter and select the corresponding node that suits the logic of the algorithm. Every allocation is persisted so the system recovers the status after a restart. A natural language entry layer was also implemented, combining retrieval augmentation generation with a large language model, converting the entry into a validated typed schema and routing to the appropriate tier, while a dashboard exposes the full pipeline and the live infrastructure state with role based visibility. The system was evaluated under identical 100 request campaigns using a mock backend that enables large scale testing without saturating the physical laboratory. Load Balancing increased the number of successful allocations by 19.15 per cent over First Fit, an advantage concentrated in the RAM constrained classical tier, and the natural language layer reached a routing accuracy of 96.8 per cent. The results confirm that a natural language interface can drive an orchestrator reliably. The main limitations are the emulated quantum cluster and a single classical laboratory deployment, future work includes integrating a real quantum backend, adding authentication to the dashboard, and introducing time aware resource release.
Hybrid quantum datacentres are advancing rapidly, and yet most of the efforts are pointing to hardware development, while the software layer that orchestrates classical and quantum resources remains underdeveloped. This thesis designs and implements a state aware hybrid orchestrator that receives workload requests and places them into the designated cluster. The orchestrator is built as a set of REST services organised into NorthBound, allocation, resource and Southbound blocks. The classical cluster is deployed on an OpenStack environment using DevStack, while the quantum cluster is emulated by quantum agents. Two placement algorithms are implemented, First Fit and Load Balancing. Both operate over both clusters, they filter and select the corresponding node that suits the logic of the algorithm. Every allocation is persisted so the system recovers the status after a restart. A natural language entry layer was also implemented, combining retrieval augmentation generation with a large language model, converting the entry into a validated typed schema and routing to the appropriate tier, while a dashboard exposes the full pipeline and the live infrastructure state with role based visibility. The system was evaluated under identical 100 request campaigns using a mock backend that enables large scale testing without saturating the physical laboratory. Load Balancing increased the number of successful allocations by 19.15 per cent over First Fit, an advantage concentrated in the RAM constrained classical tier, and the natural language layer reached a routing accuracy of 96.8 per cent. The results confirm that a natural language interface can drive an orchestrator reliably. The main limitations are the emulated quantum cluster and a single classical laboratory deployment, future work includes integrating a real quantum backend, adding authentication to the dashboard, and introducing time aware resource release.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum–classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum–classical HPC ecosystems.
Abstract Quantum computers are increasingly used not as standalone machines but as accelerators within hybrid workflows, in which a classical system prepares a circuit, submits it to a quantum processor, and interprets probabilistic results. This thesis treats the quantum–classical interface as a systems problem rather than a physics one. The orchestration, scheduling, and observability taken for granted in classical cloud-native operations are largely absent at the quantum execution boundary. From a layered analysis of the interface and a survey of existing orchestration and observability work, the thesis derives a set of requirements and answers them with a proof-of-concept, the Quantum Circuit Controller (QCC), a control plane for the quantum–classical interface. It brings cloud-native orchestration and vendor-neutral, standards-based observability to the quantum execution boundary, treating a quantum backend as a managed, observable resource rather than an opaque remote endpoint. Exercised end-to-end on a representative quantum workload across both simulated and real hardware, it satisfies these requirements and makes the computational cost and behaviour of the interface visible. The thesis proposes two standardisation pathways, a vendor-neutral execution interface and a set of quantum observability conventions. Throughout, the work brings the discipline of site reliability engineering to the quantum–classical space. Keywords quantum–classical interface hybrid quantum–classical workloads Kubernetes OpenTelemetry observability cloud-native orchestration Qiskit
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.
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.