Conference Agenda

Quantum Computing (QC) systems are being increasingly explored as the next high-impact extension to the computing spectrum, particularly in terms of their integration into supercomputers and cloud environments. The successful interoperability between classical and quantum systems depends on middleware that can interact with heterogeneous hardware technologies and their associated software stacks and data management methods. Today, approaches to high-level hybrid programming remain limited.

Workflow abstractions and workflow management tools have proved successful in overcoming the challenge of heterogeneity in tasks, data, and computational resources across multiple scientific domains. This opens up an exciting new area at the intersection of workflow research and the integration of QC into classical environments. However, adopting workflow abstractions and workflow management tools in hybrid use cases leads to significant challenges. These include adapting workflow scheduling and management methods to incorporate quantum resources and probabilistic critical paths in hybrid algorithms, modelling and supporting quantum-classical data dependencies, and acquiring and analysing hybrid workflow metadata.

Current works on the integration of QC into existing computing ecosystems focus on the interoperability and performance of algorithms without considering data-oriented optimisations (e.g., data encoding, arrangement, locality, or mapping to high-level data abstractions), and workflow-specific challenges (e.g., task-resource mapping, data partitioning, transfer and placement) are rarely explored, particularly in the context of practical applications and realistic deployments. We hypothesise that a better understanding of the key role of data management in hybrid workflows will improve our ability to effectively and efficiently interoperate QC and HPC environments. To date, few studies have addressed the systematic collection of hybrid workflow motifs, and there is no comprehensive literature covering task profiling, data characterisation, and hybrid workflow behavioural modelling. In addition, no data-oriented methodologies exist to assess the effectiveness of software integration for hybrid workflows, and breakthroughs in hybrid workflow characterisation are required to understand the next steps towards interoperability between QC and HPC systems.

In this short talk we explore pathways for profiling and characterising data access and transfer patterns in hybrid quantum-classical workflows, introducing the fundamental obstacles that can be overcome through collaborations within the JLESC framework.

Dynamic resource management (DMR) offers a promising path to converge High-Performance Computing (HPC) and Quantum Computing (QC) by enabling hybrid applications to adapt their resource usage at runtime. In this work, DMR is integrated with malleable MPI applications to dynamically resize the set of allocated classical resources according to the current phase of a hybrid HPC-QC workflow. During classical phases, the application can expand to exploit multiple nodes, while in quantum phases it shrinks, releasing unused classical resources while waiting for quantum execution. This phase-aware adaptation reduces idle time on HPC nodes and improves overall system utilization in scenarios where quantum resources are scarce and accessed as accelerators. The proposed approach targets transparent integration with existing batch schedulers and MPI codes, paving the way for more efficient execution of hybrid workloads and making HPC-QC convergence practical from the resource management perspective.

As quantum computing hardware rapidly evolves, traditional metrics like gate fidelity and Quantum Volume are insufficient for predicting real-world utility. Researchers and industry users need objective, application-centric benchmarks to gauge when quantum processors will be ready for practical tasks. However, the lack of standardized infrastructure often leads to non-reproducible performance claims and makes it difficult to track progress across different hardware generations and technologies.
We present the JUNIQ Benchmark Suite (https://go.fzj.de/juniq-benchmark-suite-page),
a large collection of benchmark problems and a vendor-agnostic, open-source initiative provided by an independent research lab, designed to track quantum technology readiness over decades. Unlike suites focused on synthetic benchmarks, the JUNIQ Benchmark Suite emphasizes application domains. Examples include computational biology and remote sensing (Quantum SVM), logistics (Tail Assignment, TSP), and hardware diagnostics (Effective Qubit Temperature) etc. By benchmarking the same relevant problems on systems ranging from the earliest publicly available QPU devices (e.g. IBM Q Experience in 2016 and D-Wave 2000Q systems in 2017) to the latest state-of-the-art devices such as Advantage2 (2025), we provide a clear, overview of hardware maturation.
A key innovation of our work is the standardized benchmarking workflow, a CLI-based toolchain along with CI pipelines, developed to ensure strict reproducibility. This setup facilitates the entire benchmark lifecycle (setup, generation, verification and execution) while aiding dependency management and enabling CI testing. This ensures that previously generated problems remain reproducible in the future, solving the "dependency drift" problem common in the fast-moving quantum software ecosystem.
The JUNIQ Benchmark Suite hosts benchmark instances from various domains and is actively tracking performance on the latest analog and digital quantum processors available through the JUNIQ cloud platform and beyond. To foster a transparent ecosystem, we include templates and tooling that lower the barrier for community to contribute new problem instances.
Keywords: Quantum computing, Benchmarking, QPU, Quantum computing applications,

We are conducting the JHPC Quantum project to design and build a quantum–supercomputer hybrid computing platform by integrating multiple on-premises quantum computers — namely, an IBM superconducting quantum computer and a Quantinuum trapped-ion quantum computer—with several supercomputers, including Fugaku and GPU-based systems. Our platform is now in operation and is providing services to test users of our test user program. We expect a wide range of outcomes from QC–HPC hybrid computing using this platform. In this presentation, the overview and current status of the JHPC Quantum project, along with our perspective on quantum–HPC hybrid computing.

This work describes a unified identity and access design for the Quantum–HPC hybrid platform that integrates quantum computers and supercomputers. The platform adopts OAuth2.0-based access tokens to enable workflows to securely access multiple computing systems. Job submission to HPC resources is performed via Slurm REST interfaces under token-based authorization. The user management component enforces identity verification procedures, partially automated through digital credential mechanisms. Separately, user information is subject to screening processes aligned with export control and security compliance requirements. The design supports secure and practical hybrid computational environments.