Quantum Computer Operating Systems, Architecture Analysis and Proposed Enhancements Quantum Computing Systems are transitioning from laboratory curiosities into cloud-accessible platforms that serve thousands of concurrent users across heterogeneous hardware modalities. The software stack has not kept pace. No mature, general-purpose quantum operating system (QOS) exists that offers real-time scheduling guarantees, cross-vendor circuit portability, and integrated security in the manner that classical operating systems provide for conventional computing. This research examines the architectural foundations, design trade-offs, and practical limitations of the principal quantum software stacks deployed at scale today, including IBM Qiskit Runtime, Google Cirq, Microsoft Azure Quantum, and Origin Quantum’s Origin Pilot. Origin Pilot, developed by Origin Quantum Computing Technology (Hefei, China), reached Version 4.0 in February 2026 as an open-source release and has processed more than 339,000 quantum jobs for users in over 120 countries—the most geographically distributed quantum operating system deployment studied here. Beyond cataloguing these systems, three concrete architectural enhancements are proposed: (1) an artificial intelligence (AI)-driven workload prediction layer that reduces mean queue latency by formulating scheduling as a multi-armed bandit problem, (2) a cross-platform circuit portability layer (CPPL) built on a hardware-agnostic intermediate representation (IR), and (3) a federated quantum processing unit (QPU) resource pooling mechanism that coordinates geographically distributed quantum hardware through consensus-based admission control. A structured gap analysis across seven dimensions confirms that no existing system satisfies more than four simultaneously. The findings argue that quantum operating system design must treat heterogeneity and latency as first-class design concerns, not afterthoughts.
Quantum Computer Operating Systems, Architecture Analysis and Proposed Enhancements Quantum Computing Systems are transitioning from laboratory curiosities into cloud-accessible platforms that serve thousands of concurrent users across heterogeneous hardware modalities. The software stack has not kept pace. No mature, general-purpose quantum operating system (QOS) exists that offers real-time scheduling guarantees, cross-vendor circuit portability, and integrated security in the manner that classical operating systems provide for conventional computing. This research examines the architectural foundations, design trade-offs, and practical limitations of the principal quantum software stacks deployed at scale today, including IBM Qiskit Runtime, Google Cirq, Microsoft Azure Quantum, and Origin Quantum’s Origin Pilot. Origin Pilot, developed by Origin Quantum Computing Technology (Hefei, China), reached Version 4.0 in February 2026 as an open-source release and has processed more than 339,000 quantum jobs for users in over 120 countries—the most geographically distributed quantum operating system deployment studied here. Beyond cataloguing these systems, three concrete architectural enhancements are proposed: (1) an artificial intelligence (AI)-driven workload prediction layer that reduces mean queue latency by formulating scheduling as a multi-armed bandit problem, (2) a cross-platform circuit portability layer (CPPL) built on a hardware-agnostic intermediate representation (IR), and (3) a federated quantum processing unit (QPU) resource pooling mechanism that coordinates geographically distributed quantum hardware through consensus-based admission control. A structured gap analysis across seven dimensions confirms that no existing system satisfies more than four simultaneously. The findings argue that quantum operating system design must treat heterogeneity and latency as first-class design concerns, not afterthoughts.
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 integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.
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.
Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5× higher fidelity, increases resource utilization by up to 9.6×, and reduces waiting times by up to 5× while sacrificing only 1--3\% fidelity, on average, compared to the baselines.
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.