We describe the design, implementation, and nine-month operational experience of the software management stack for Lagrange, an IQM Spark five-qubit superconducting quantum computer jointly acquired by LINKS Foundation, Politecnico di Torino and the Italian National Institute of Metrological Research (INRiM), and managed by LINKS. Lagrange is, to our knowledge, the first quantum computer in Italy that is fully operational and accessible to students and researchers from multiple institutions under formal service agreements, and to the general public under commercial agreements. When installed in mid-2025, the IQM Spark hardware was delivered by the vendor with authentication only: no billing, project management or fair usage enforcement were provided. We developed a modular middleware layer that filled that gap without modifying any vendor client software, by intercepting API calls through a proxy that enforces project-based budgets, reservation-aware authorisation, and per-user fairness policies. The middleware adopts a plugin architecture that cleanly separates vendor-specific logic from site-specific policies, enabling reuse across different quantum hardware backends and deployment contexts. Since entering production in September 2025, the system has processed over 240,000 quantum jobs totalling more than 1 week of QPU execution time, with greater than 98% uptime. Notably, students at Politecnico di Torino regularly use the machine during both lectures and formal examinations -- a practice we believe to be unique in Europe. We report on the system architecture, the operational lessons learned, and the infrastructure choices that made this deployment possible.
We describe the design, implementation, and nine-month operational experience of the software management stack for Lagrange, an IQM Spark five-qubit superconducting quantum computer jointly acquired by LINKS Foundation, Politecnico di Torino and the Italian National Institute of Metrological Research (INRiM), and managed by LINKS. Lagrange is, to our knowledge, the first quantum computer in Italy that is fully operational and accessible to students and researchers from multiple institutions under formal service agreements, and to the general public under commercial agreements. When installed in mid-2025, the IQM Spark hardware was delivered by the vendor with authentication only: no billing, project management or fair usage enforcement were provided. We developed a modular middleware layer that filled that gap without modifying any vendor client software, by intercepting API calls through a proxy that enforces project-based budgets, reservation-aware authorisation, and per-user fairness policies. The middleware adopts a plugin architecture that cleanly separates vendor-specific logic from site-specific policies, enabling reuse across different quantum hardware backends and deployment contexts. Since entering production in September 2025, the system has processed over 240,000 quantum jobs totalling more than 1 week of QPU execution time, with greater than 98% uptime. Notably, students at Politecnico di Torino regularly use the machine during both lectures and formal examinations -- a practice we believe to be unique in Europe. We report on the system architecture, the operational lessons learned, and the infrastructure choices that made this deployment possible.
Quantum computers are moving into HPC centers, and the main challenge is now integration rather than pure hardware access. Many current software paths still depend on vendor-specific adapter chains between user SDKs, schedulers, and backend APIs. This pattern makes operations more complex than necessary and slows the transition from pilots to production workflows. We present a practical integration path centered on the Quantum Device Management Interface (QDMI). Using IQM superconducting systems as a hardware case study, we implement an IQM-backed QDMI layer and connect it to two software layers that HPC centers working with quantum computers already care about: Slurm-based job execution and Qiskit-facing user workflows. The implementation is publicly available at https://github.com/iqm-finland/QDMI-on-IQM. The key message is simple: integrating quantum hardware into HPC does not have to be a bespoke engineering effort for each backend. Once the software-hardware boundary is standardized, large parts of the stack become reusable across providers and deployment styles. Our results do not claim that standardization eliminates all HPCQC challenges. They show that this specific boundary can already be standardized today in a way that is practical for users, operators, and vendors.
Open Quantum is a platform that provides researchers and developers with access to quantum processing units (QPUs) from multiple hardware providers through a unified API and native integrations with widely used quantum programming frameworks, including Qiskit and PennyLane. The platform is operated by Quantum Rings — a team best known for their large-scale quantum circuit simulation — in partnership with qBitTensor Labs, which operates a decentralized quantum-compute network on the Bittensor protocol. Open Quantum offers two execution tiers: Public Compute, in which circuits are routed through the decentralized network, with free or subsidized access in exchange for the user's consent to the retention and study of execution data; and Private Compute, in which circuits execute on directly managed infrastructure and are deleted after execution. This paper describes the platform's architecture and execution model; the identity and verification mechanisms that connect a permissionless incentive network to metered quantum hardware; and the consented, cross-vendor corpus of real quantum workloads that the public tier accumulates, from which the platform will publish aggregate analyses beginning at the close of Summer 2026.
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.
Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.
The integration of Quantum Computers (QC) within High-Performance Computing (HPC) environments holds significant promise for solving real-world problems by leveraging the strengths of both computational paradigms. However, the integration of a complex QC platform in an HPC infrastructure poses several challenges, such as operation stability in non-laboratory like environments, and scarce access for maintenance. Currently, only a few fully-assembled QCs currently exist worldwide, employing highly heterogeneous and cutting-edge technologies. These platforms are mostly used for research purposes, and often bear closer resemblance to laboratory assemblies rather than production-ready, stable, and consistently-performing turnkey machines. Moreover, public cloud services with access to such quantum computers are scarce and their availability is generally limited to few days per week. In November 2022, we introduced the first cloud-accessible general-purpose quantum computer based on single photons. One of the key objectives was to maintain the platform's availability as high as possible while anticipating seamless compatibility with HPC hosting environment. In this article, we describe the design and implementation of our cloud-accessible quantum computing platform, and demonstrate one nine availability (92%) for external users during a six-month period, higher than most online services. This work lay the foundation for advancing quantum computing accessibility and usability in hybrid HPC-QC infrastructures.
The quantum computing field has established a rigorous five-layer architecture spanning quantum physics, qubit hardware, quantum algorithms, hybrid classical-quantum execution, and application decision output. No prior academic work has formally described the governance architecture that must sit above this stack when AI systems execute on quantum infrastructure in regulated environments. This paper presents three original contributions. First, a formal identification of the governance gap at the top of the established quantum computing stack, expressed as six concrete failure modes demonstrating why treating quantum infrastructure governance and AI system governance as independent concerns produces irreducible compliance failures. Second, the unified quantum AI governance pyramid, a fifty-layer architecture combining the established five quantum computing layers with the forty-five layers of the YIN Mazari Architecture organized into nine governance tiers. The nine tiers span physics enforcement foundation, core cryptographic enforcement, cross-domain verification, human authority, neural causal reasoning, world models, recovery and response, hardware enforcement, and three certification output layers: ACR Certification with four tiers (Platinum, Gold, Silver, Unrated), ACR-Zero Certification establishing structural impossibility of unauthorized execution, and the AQL Standard for AI Liability Quantification enabling insurers to price AI liability from cryptographic enforcement data. Third, a formal proof of bidirectional coupling between the quantum computing stack and the YIN Mazari governance architecture, demonstrating that an AI system cannot be certified compliant while the quantum infrastructure beneath it fails its cryptographic posture requirements, and that infrastructure migration cannot proceed without knowing which AI systems depend on the assets being migrated. The paper establishes unified regulatory alignment across DORA Regulation EU 2022/2554, EU AI Act Regulation EU 2024/1689, NIS2 Directive EU 2022/2555, CNSA 2.0, and HIPAA Technical Safeguards simultaneously through a single compliance attestation spanning both halves of the pyramid. This architecture is protected under 41 USPTO provisional applications comprising 4,400+ claims with priority date November 23, 2025. PCT deadline November 23, 2026. Implementation of any component requires a license from the inventor.