quantum workflow integration

This work proposes the first cloud-native scheduling framework systematically designed for hybrid quantum-classical computing to address the challenge of efficiently orchestrating heterogeneous computational resources at scale. Built upon Kubernetes, Argo Workflows, and Kueue, the framework enables unified, resource-aware dynamic scheduling across CPUs, GPUs, and quantum processing units (QPUs), supporting multi-stage, reproducible, and observable hybrid workflows. The effectiveness of the framework is demonstrated through end-to-end collaborative experiments on distributed quantum circuit cutting tasks, which highlight its significant advantages in scalability, flexibility, and reproducibility.

Hybrid classical-quantum workflows suffer from high quantum resource idle time and prolonged end-to-end execution latency. Method: This paper proposes a lightweight co-scheduling approach leveraging SLURM’s native heterogeneous job mechanism—requiring no kernel modification or dedicated scheduler deployment. It achieves tight coupling between classical computation and quantum hardware invocation via modular workflow decomposition, explicit dataflow management, and shell-script-based orchestration. Contribution/Results: To our knowledge, this is the first systematic extension of SLURM’s heterogeneous job capabilities to quantum computing. The method significantly reduces quantum device idle time and overall wall-clock execution time. Experimental evaluation demonstrates that the solution is both concise and efficient, enabling plug-and-play integration of quantum computing capabilities into existing HPC centers. It provides a practical, low-overhead pathway for supercomputing facilities to rapidly adopt quantum acceleration without infrastructure overhaul.

This work addresses key challenges in quantum software development—namely, multi-language integration complexity and intricate quantum-classical co-processing. We propose a DevOps-driven hybrid full-stack iterative model that introduces the first “quantum-agnostic” development paradigm. Leveraging a unified API gateway, hardware abstraction layer, and cross-platform compilation/execution framework, the model seamlessly integrates major quantum toolchains (e.g., Qiskit, Cirq) and cloud platforms (e.g., AWS Braket, Azure Quantum). It supports the entire quantum software lifecycle—including coding, testing, deployment, orchestration, translation, and interpretation—thereby enabling conventional software engineers without quantum physics expertise to build maintainable, scalable quantum-classical hybrid systems. The work delivers the first industry-oriented Quantum Software Engineering (QSE) implementation roadmap, significantly improving cross-stack collaboration efficiency and system maintainability. This advances QSE from laboratory research toward rigorous, production-ready engineering practice.

Addressing the challenges of heterogeneous resource scheduling, cross-paradigm data movement, and unified access to both NISQ and fault-tolerant quantum devices in deep quantum–HPC integration, this paper proposes the first hardware-agnostic quantum–supercomputing fusion software stack. Methodologically, it introduces (1) a quantum–classical unified resource management framework; (2) a hardware-abstracted programming interface and a cross-platform Quantum Platform Manager API; and (3) a quantum gateway supporting REST/gRPC, a hybrid scheduler, and a quantum-circuit co-optimization toolchain. The stack seamlessly integrates with mainstream HPC job schedulers (e.g., Slurm, PBS). Empirical evaluation on hybrid algorithms—including the variational quantum linear solver—on real supercomputing systems demonstrates significant improvements in quantum–classical resource utilization and task throughput. The design is both practically deployable and inherently scalable, establishing a foundational infrastructure for large-scale quantum–HPC convergence.

This work addresses the lack of interoperability among current quantum compilation tools, which hinders the development of a unified software stack. It presents the first systematic integration of PennyLane and the Munich Quantum Toolkit (MQT) through the MLIR framework, leveraging MLIR’s intermediate representation and LLVM infrastructure to establish reusable integration pathways and best practices. By lowering the barrier to entry for quantum software engineers adopting MLIR, this approach enables seamless interoperability between distinct quantum toolchains. The study provides both a practical roadmap and actionable strategies for constructing a modular, interoperable quantum software ecosystem, thereby advancing the standardization and composability of quantum programming tools.

This work addresses the challenge of identifying suitable quantum computing candidates and expressing solution strategies within hybrid quantum-classical workflows, which is often hindered by highly specialized expertise and a lack of unified abstractions. To overcome these limitations, the paper introduces the first domain-specific language (DSL) tailored for hybrid quantum-classical meta-solving strategies. This DSL enables technology-agnostic, problem-level specification of strategies and is accompanied by a dedicated execution framework. By integrating with the ProvideQ toolkit for workflow orchestration, the proposed open-source solution significantly lowers the barrier to entry, enhances strategy reusability, and improves automation—thereby facilitating the identification and deployment of quantum-suitable problems.

Quantum software and artificial intelligence development face significant challenges, including a scarcity of skilled personnel, low development efficiency, and complex deployment decisions in hybrid systems. This study presents the first systematic literature review on automated software engineering and AI methods specifically tailored for quantum and hybrid quantum-classical systems. It synthesizes existing techniques, tools, and application strategies, while identifying key automation approaches and critical research gaps. By addressing the lack of comprehensive reviews in this interdisciplinary domain, the work establishes a theoretical foundation and offers practical pathways to enhance the development efficiency and deployment intelligence of quantum–AI integrated systems.

This study addresses the adaptation of core software engineering principles—such as quality assurance, testing, abstraction, and governance—to the quantum computing paradigm to advance the emerging discipline of quantum software engineering. By organizing the first QuantumX workshop at a major software engineering conference, the project brought together leading research teams from Spain and Ibero-America to systematically investigate key areas including quantum service engineering, hybrid quantum-classical architectures, quantum circuit optimization, quality models, and quantum machine learning. This initiative not only identified common research themes and open challenges but also fostered collaboration through networks such as RIPAISC and QSpain, establishing Spain’s leadership in the European and global quantum software ecosystem and catalyzing a sustainable, interdisciplinary research community.

Integrating quantum computers into high-performance computing (HPC) centers is often hindered by reliance on vendor-specific adapters, impeding production-level deployment. This work proposes and implements a standardized hardware-software boundary based on the Quantum Device Management Interface (QDMI), using an IQM superconducting quantum system as a case study to seamlessly integrate with the Slurm job scheduler and Qiskit user workflows. The implementation demonstrates that QDMI provides a unified interface for coordinating HPC resources and quantum hardware across different vendors and deployment models, thereby eliminating redundant custom development. The authors have open-sourced their QDMI-on-IQM integration, significantly simplifying the deployment of quantum-HPC hybrid systems in today’s heterogeneous computing environments.

Quantum software testing presents unique challenges not addressed by classical software engineering practices, yet its professional scope and competency requirements remain poorly defined. Method: This study systematically analyzes job postings from 110 quantum hardware and software enterprises, integrating semantic role labeling, latent Dirichlet allocation (LDA)-based topic modeling, and cross-domain skill mapping to empirically characterize the profession. Contribution/Results: We propose the first empirically grounded occupational definition of quantum software testing, formalized as a three-dimensional competency framework—“calibration, control, and hybrid verification.” The framework identifies three core competencies: programming automation, quantum device literacy, and interdisciplinary collaboration. This work bridges the epistemic gap between industry demand and academic training, providing empirically validated foundations for curriculum design, workforce standardization, and career pathway development in quantum software engineering.