With an increasingly diverse portfolio of quantum backends, the adoption of standardized interfaces has become a key prerequisite for scalable access and interoperability within quantum software stacks. The Quantum Device Management Interface (QDMI) addresses this challenge and is emerging as one of the de facto standards for hardware abstraction, enabling the unified management not only of individual Quantum Processing Units (QPUs) but also of complete full-stack cloud services. This paper presents a case study demonstrating the integration of QDMI with Amazon Braket, a quantum computing cloud service that provides a single access point to a wide range of hardware technologies. By treating the cloud service itself as a unified device, the proposed implementation enables management of the complete task lifecycle - ranging from authentication and circuit submission to result retrieval - across Braket's heterogeneous set of simulators and hardware backends. We detail the engineering insights gained from this integration and present a hands-on example workflow, ultimately paving the way for integrated access to cloud-hosted quantum resources from QDMI-enabled software stacks.
Journal reference
High Performance Computing: ISC High Performance 2026 International Workshops, Hamburg, Germany, June 22-26, 2026, Revised Selected Papers
With an increasingly diverse portfolio of quantum backends, the adoption of standardized interfaces has become a key prerequisite for scalable access and interoperability within quantum software stacks. The Quantum Device Management Interface (QDMI) addresses this challenge and is emerging as one of the de facto standards for hardware abstraction, enabling the unified management not only of individual Quantum Processing Units (QPUs) but also of complete full-stack cloud services. This paper presents a case study demonstrating the integration of QDMI with Amazon Braket, a quantum computing cloud service that provides a single access point to a wide range of hardware technologies. By treating the cloud service itself as a unified device, the proposed implementation enables management of the complete task lifecycle - ranging from authentication and circuit submission to result retrieval - across Braket's heterogeneous set of simulators and hardware backends. We detail the engineering insights gained from this integration and present a hands-on example workflow, ultimately paving the way for integrated access to cloud-hosted quantum resources from QDMI-enabled software stacks.
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.
Quantum computing is a promising technology that requires a sophisticated software stack to connect end users to the wide range of possible quantum backends. However, current software tools are usually hard-coded for single platforms and lack a dynamic interface that can automatically retrieve and adapt to changing physical characteristics and constraints of different platforms. With new hardware platforms frequently introduced and their performance changing on a daily basis, this constitutes a serious limitation. In this paper, we show-case a concept and a prototypical realization of an interface, called the Quantum Device Management Interface (QDMI), that addresses this problem by explicitly connecting the software and hardware developers, mediating between their competing interests. QDMI allows hardware platforms to provide their physical characteristics in a standardized way, and software tools to query that data to guide the compilation process accordingly. This enables software tools to automatically adapt to different platforms and to optimize the compilation process for the specific hardware constraints. QDMI is a central part of the Munich Quantum Software Stack (MQSS)-a sophisticated software stack to connect end users to the wide range of possible quantum backends. QDMI is publicly available as open source at https://github.com/Munich-Quantum-Software-Stack/QDMI.
Quantum computing systems are increasingly accessed through software stacks that hide hardware-specific details behind higher-level abstractions. This is necessary because current quantum resources differ in their physical realisation, supported program formats, execution parameters, calibration data, status models, and result representations. At the same time, independent software stack initiatives create an interoperability challenge: even systems with similar goals may not communicate directly if their APIs encode different assumptions. This thesis studies this problem for the Munich Quantum Software Stack (MQSS), whose backend abstraction is the Quantum Device Management Interface (QDMI), and QCI Connect, a modular full-stack quantum computing platform developed within the DLR Quantum Computing Initiative. The goal is to connect these systems bidirectionally while preserving their architectural abstractions. In one direction, MQSS resources are exposed as QCI Connect QPUs via a dedicated QPU connector. In the other direction, QCI Connect is exposed as a QDMI-compatible device that translates QDMI session, query, job, status, cancellation, and result calls into QCI Connect API requests. The thesis compares the relevant API concepts and implements mappings for resource discovery, job submission, status handling, cancellation, and result retrieval. The implementation shows that bidirectional communication is feasible through existing extension points in both stacks. It also reveals semantic mismatches, including incomplete metadata equivalence, differences in status granularity, assumptions about circuit formats, and result fields requiring custom handling. These findings show that interoperability between quantum software stacks is possible, but long-term portability requires clearer standardisation of capabilities, execution parameters, status models, and result schemas.
The efficient and scalable integration of quantum resources into high-performance computing (HPC) environments requires standardized mechanisms for resource management, scheduling, and workflow orchestration across diverse and heterogeneous infrastructures. The Quantum Resource Management Interface (QRMI) addresses this challenge through a thin, vendor-agnostic middleware layer that provides standardized APIs for scheduling, executing, and monitoring quantum workloads while exposing quantum resources as first-class schedulable resources alongside CPUs and GPUs. Although previous work demonstrated QRMI integration with the Slurm workload manager, its applicability across other workload managers remained unexamined. This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler. We examine the integration patterns, implementation requirements, and scheduler-specific considerations associated with each environment and compare QRMI with alternative approaches to quantum resource integration. We demonstrate that QRMI provides a portable and flexible abstraction layer that minimizes scheduler-specific modifications while enabling consistent access to heterogeneous quantum resources across both on-premises and cloud environments.
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 computing is rapidly reaching a point in which its application design and engineering aspects have to be seriously considered. However, quantum software engineering is still in its infancy with numerous challenges, especially in dealing with the diversity of quantum programming languages and noisy intermediate-scale quantum systems. To alleviate these challenges, we propose QFaaS, a novel Quantum Function-as-a-Service framework, which leverages the advantages of the serverless model and state-of-the-art techniques to advance practical quantum computing in the NISQ era. Our framework provides essential elements of a serverless quantum system to streamline service-oriented quantum application development in cloud environments, such as combining hybrid quantum-classical computation, automating the backend selection, and adapting quantum DevOps techniques. QFaaS offers the first full-stack and unified quantum serverless platform by integrating multiple well-known quantum software development kits (Qiskit, Q#, Cirq, and Braket), quantum simulators, and cloud providers (IBM Quantum and Amazon Braket). This paper proposes the concept of quantum functions, software life cycle, system design, operation workflows, and implementation of QFaaS. We also present two practical use cases and performance evaluations on today’s quantum computers and simulators to demonstrate our framework’s capability to ease the burden on traditional engineers to expedite the ongoing quantum software transition.