The 2nd International Workshop on Software Frameworks and Workload Management on Quantum and HPC Ecosystems

Organizers:

In-Saeng Suh, Oak Ridge National Laboratory (ORNL), USA

Esam El-Araby, University of Kansas (KU), USA

Edoardo Giusto, University of Naples Federico II, Italy

Katherine Klymko, Lawrence Berkeley National Laboratory (LBNL), USA

Frank Mueller, North Carolina State University (NCSU), USA

Jorge Echavarria, Munich Quantum Valley (MQV), Germany

Amir Shehata, Oak Ridge National Laboratory (ORNL), USA

Date: November 2026

Time:

Location:

Short Description of the Proposed Workshop

The integration of quantum computing (QC) with high-performance computing (HPC) is emerging as a transformative paradigm for addressing complex scientific and engineering challenges. Given current quantum hardware limitations, such as noise, limited qubit counts, and constrained circuit depth, hybrid Quantum–HPC (Q-HPC) ecosystems provide a practical path forward by combining complementary strengths. This workshop focuses on software frameworks, AI-enabled orchestration, and workload management strategies essential for scalable Q-HPC integration. Topics include architecture co-design, heterogeneous resource management, networking, fault tolerance, error mitigation, programming models, middleware, benchmarking, and large-scale distributed Q-HPC systems. Emphasis will be placed on open, interoperable ecosystems such as openQSE and on ML/AI techniques for adaptive workflow orchestration, topology-aware scheduling, performance prediction, and autonomous runtime control. Through invited talks, technical presentations, panels, and special sessions, the workshop will foster collaboration across academia, industry, national laboratories, and funding agencies to define research priorities and accelerate AI-driven Q-HPC ecosystems.

Workshop Scope

The integration of quantum computing (QC) with high-performance computing (HPC) is emerging as a critical paradigm for tackling complex scientific and engineering challenges [1]. Current QC technology is constrained by the number of qubits, noise, and limited circuit depths, making hybrid Quantum-HPC (Q-HPC) ecosystems a practical approach to leveraging the strengths of both computing paradigms [2]. However, realizing the full potential of Q-HPC systems requires advanced software tools/frameworks that can efficiently manage heterogeneous resources [3,4], decompose large-scale problems [5,6], optimize execution workflows [7], validate programming models [8,9], and mitigate errors [10,11]. As quantum–HPC (Q-HPC) environments scale in complexity, open and interoperable software ecosystems such as the open Quantum–HPC Software Ecosystem (openQSE) [12] are increasingly needed to integrate quantum computers, simulators, and software stacks as first-class HPC components. At the same time, machine learning (ML)/artificial intelligence (AI) is emerging as a foundational enabler of intelligent and adaptive quantum software [13], providing capabilities for resource orchestration, topology-aware scheduling, workflow optimization, performance prediction, and closed-loop control across the Q-HPC stack.

This workshop will bring together experts from academia, industry, national laboratories, and research funding agencies to discuss and advance the development of architecture co-design, fault tolerance, software stacks, workload management strategies for integrated Q-HPC ecosystems. Topics of interest include, but are not limited to:

• AI-Driven Workload Management in Q-HPC Systems: AI-based efficient scheduling, job allocation, and workload balancing across quantum and classical processors.
• Q-HPC Software Frameworks and AI-Assisted Quantum Compilation: Learning-based circuit transpilation, leyout selection, Middleware, and runtime systems that enable seamless execution of hybrid quantum-classical workloads.
• Scalability and Decomposition Strategies: Techniques for partitioning and distributing large-scale optimization and simulation problems.
• Error Mitigation and Noise Reduction Strategies: Software-based approaches for improving computational accuracy on NISQ devices.
• Programming Models and Abstractions: Development of high-level frameworks for interfacing quantum algorithms with HPC infrastructure.
• Benchmarking and Performance Metrics: Evaluation strategies for measuring the efficiency of Q-HPC workflows.
• Digital Twins and AI Surrogates for Q-HPC: AI-based performance models for quantum devices and HPC systems to support predictive scheduling and what-if analysis.
• Q-HPC Workflows: Design patterns specifying compute, data and control flow characteristics of hybrid HPC and QC computational workloads
• Workflow Management and Automation: Tools for optimizing computational workflows across heterogeneous quantum and HPC platforms. Design patterns specifying compute, data and control flow characteristics of hybrid HPC and QC computational workloads

By fostering discussions on these topics, the workshop aims to bridge the gap between quantum software development and classical HPC infrastructure, ultimately accelerating the practical deployment of AI-Enabled Q-HPC applications.

Workshop Goals

The workshop will aims to achieve the following topics:

• Ideation: Encourage innovative ideas for improving Q-HPC workload management and software development.
• Early Work Socialization: Provide a venue for researchers to present preliminary results, obtain feedback, and refine their approaches.
• Interdisciplinary Collaboration: Foster engagement between quantum computing researchers, HPC experts, software developers, and domain scientists.
• Professional Development: Offer learning opportunities for early-career researchers and students interested in Q-HPC software frameworks and AI-driven techniques across heterogeneous Q-HPC platforms.
• Artifact Development: Promote the sharing of open-source tools, datasets, and benchmarking methodologies to standardize Q-HPC software research.

Expected Outcomes

• Identification of key research challenges, opportunities, and future directions for integrating AI capabilities into Q-HPC software frameworks, workflow orchestration, and autonomous workload management.
• Establishment of new collaborations between researchers in QC, HPC, AI, and software engineering to advance intelligent, automated, and scalable hybrid computing ecosystems.
• Increased adoption of best practices and standardized AI-driven Q-HPC frameworks and workflows.
• Compilation of presented research works into workshop proceedings, open-source repositories, or a community-driven report.

Relevance and Impact to SC Attendees

The SC (Supercomputing) Conference is the premier global venue for advancing HPC technologies, and the growing convergence of quantum computing, AI, and HPC makes this workshop particularly timely and impactful. Building on the success of the the 1st SFWM workshop in SC25 [14] (103 average attendance; 22 submitted papers; 9 accepted/presented papers; 1 invited speaker; and panel discussions; https://sfwqhe.github.io/sfwm-qhpce), this workshop will deliver tangible value to SC attendees - including students, researchers, developers, and industry professionals - through the following benefits:

• Exposure to state-of-the-art software frameworks enabling Q-HPC integration.
• Experience on cutting-edge AI techniques across heterogeneous Q-HPC resources.
• Insights into workload management strategies that enhance the efficiency of hybrid quantum-classical computing.
• Connection of the HPC, QC, and AI communities, to engage with technical leaders in the field, explore joint research directions, and establish potential collaborations around intelligent hybrid computing ecosystems.
• A better understanding of how to prepare AI-enabled Q-HPC ecosystems and software stacks for practical quantum acceleration in the near future.

Workshop Format

The workshop will consist of:

• Invited Talks (30 min each): Leading experts will discuss the state of Q-HPC software, workload, and future directions.
• Paper Presentations (15 min each): Selected contributions from researchers in academia and industry.
• Special sessions (45 min each): Research priorities and opportunities on quantum systems and HPC integration and openQSE project.
• Panel Discussion and Q&A (60 min): Experts will debate the challenges and opportunities in AI-enabled Q-HPC software frameworks.
• Networking Session (15 min): Dedicated time for informal discussions and collaboration-building.

This format ensures an interactive and engaging environment where attendees can exchange ideas, learn from experts, and explore cutting-edge solutions in AI-enabled Q-HPC software development.

Workshop Schedule

Plan Diversity:

• The workshop will prioritize diversity and inclusion by ensuring a balanced representation of speakers from different backgrounds, including women and underrepresented groups in QC, AI, and HPC.
• We will implement a hybrid (in-person and virtual) format to allow participation from those unable to attend in person.
• A code of conduct will be established to maintain a respectful and welcoming environment for all attendees.

Advertising Plan:

• Promotion through SC conference channels, including website, email lists, and social media.
• Outreach to major QC, AI, and HPC research groups, industry partners, and national labs.
• Collaboration with professional organizations such as ACM, IEEE, and QED-C.
• Cross-promotion with related workshops and conferences (e.g., ICS, QCE, and ADAC) in QC, AI, and HPC.

Proceedings Plan

Proceedings will be published in an open-access format, ensuring wide dissemination of research contributions. Authors will be encouraged to submit extended versions of their papers to relevant journals. Additionally, workshop materials (e.g., keynote slides, panel discussions) will be made available online for future reference.

Planned Timeline Including Paper Deadlines, Notification, etc.

Paper Submission Deadline:

Notification of Acceptance:

Proceedings Submission Deadline:

Workshop Date: