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Meeting 2026 08 03
amirshehataornl edited this page Aug 20, 2026
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- Date: Monday, August 3, 2026
- Time: (Slot #1) 12 pm - 1 pm US Eastern Daylight Time (View Meeting Time in Your Timezone)
- Location: Virtual Meeting.
- Meeting Link: openqse.org/captcha
- Next: Monday, August 10, 2026 @ (Slot #2) 7 pm - 8 pm US Eastern Daylight Time (View Meeting Time in Your Timezone)
- Seminar Series: Doriana Medic (University of Turin) will talk about their work on Quantum workflow tools
| Person | Institution |
|---|---|
| Thomas Naughton | ORNL |
| Michael Ferguson | HPE |
| Francesco Medina | University of Turin |
| Doriana Medic | University of Turin |
| Josh Moles | Snoquera |
| Brandon Neth | HPE |
| Abdur Rahman Hatim | IISc |
| Philipp Seitz | Quantinuum |
| Eun Kyung Lee | IBM |
| Munetaka Ohtani | IBM |
| Bran Radovanovic | ORNL |
| Claudio Siqueira de Carvalho | IBM |
| Ryan Hoffman | Quantinuum |
| Peter Groszkowski | ORNL |
| John Children | Quantinuum |
| Michael Sandoval | ORNL |
| Andy Stone | HPE |
| Dan Holme | Qoro |
| Burns Healy | Tyto Athene |
| Sakshi Chhabra | IISc |
| Yogesh Simmhan | IISc |
| Johannes Blaschke | EIT |
| Sebastian Stern | Amazon |
| Yoonho Park | IBM |
| Gabriella Bettonte | E4 |
| Alain Roy | IonQ |
| Sangram Shivajirao Deshpande | North Carolina State University |
| Narasinga Rao Miniskar | ORNL |
| Smriti Bajaj | Dell Technologies |
| Elaine Wong | ORNL |
| Alex Chernoguzov | Quantinuum |
| Masoud Mohseni | HPE |
| Namit Anand | HPE |
| Simone Rizzo | E4 |
| Muhammad Osama | AMD |
| Matthieu Moreau | PASQAL |
| Aaron Lott | HPE |
| Jeff Heckey | AWS |
| Jonathan Skone | NERSC/LBL |
| Cameron Brunner | Siemens DISW |
- Doriana Medic from the University of Turin presented work on workflow orchestration for heterogeneous HPC, cloud, AI, and quantum computing environments.
- The talk motivated workflows as a way to manage modern applications that are no longer monolithic, but are pipelines of data movement, simulation, machine learning, optimization, visualization, and post-processing steps.
- Doriana described hybrid workflows as a separation between the workflow model and the deployment environment, allowing tasks to be mapped to different locations without rewriting the application logic.
- The University of Turin group developed Streamflow, a CWL-compliant, container-native workflow management system with modular connectors for different execution environments.
- QSplit was presented as a workflow-oriented hybrid quantum-classical optimization framework that decomposes QUBO problems, dispatches subproblems to different backends, and aggregates partial results.
- The group discussed limitations of naive QUBO and circuit-cutting decompositions, especially the need to account for correlations and problem structure rather than only load balancing.
- Doriana also described decentralized orchestration and SWIR, an intermediate representation intended to compile workflow descriptions into distributed execution bundles.
- The Q&A focused on circuit cutting, decomposition strategy, dynamic scheduling, fault-tolerance primitives, backend-specific information, and how quantum calibration events might trigger workflow loops or recompilation.
- NOTICE: Notes generated using AI; please verify accuracy.
- Thomas Naughton opened the seminar and introduced Doriana Medic from the University of Turin.
- Doriana introduced herself as an assistant professor in the Computer Science Department at the University of Turin.
- She said she is part of the Alpha Parallel Computing Group, a heterogeneous team covering HPC, parallel programming, RISC-V and next-generation architectures, system software, data-intensive computing, cloud, federated learning, distributed AI, and quantum computing.
- The talk focused on distributed workflows and the intersection between workflow systems and quantum computing.
- Doriana described a major shift in computing demand:
- HPC is still needed for simulation and engineering.
- AI, foundational models, AI for science, and data-intensive workflows are creating new demand.
- Quantum processors are increasingly available through cloud providers, national infrastructures, and co-location with HPC systems.
- She noted that Europe is investing heavily through EuroHPC, AI factories, quantum initiatives, and national centers such as Italy's National Centre for HPC, Big Data, and Quantum Computing.
- The resulting computing landscape is increasingly heterogeneous, combining CPUs, GPUs, cloud resources, AI accelerators, quantum processors, edge resources, and specialized hardware.
- Doriana emphasized that access to these resources does not automatically make applications easier or faster; orchestration becomes the core challenge.
- Modern scientific and industrial applications are often complex pipelines, not single programs.
- Different stages in a pipeline may need different execution environments:
- Simulation on HPC.
- Training on GPUs.
- Data processing on cloud or edge systems.
- Optimization on quantum resources.
- The challenge is transparent, efficient, reproducible, and portable orchestration across that ecosystem.
- Doriana described workflows as collections of interconnected steps with data inputs, outputs, and control dependencies.
- Scientific workflows are often modeled as directed acyclic graphs, though some modern use cases such as federated learning may include cycles or loops.
- Workflow components include:
- Tasks or steps.
- Ports for input/output locations.
- Data.
- Dependencies between tasks.
- A workflow model describes what the application does, but not where it runs.
- Hybrid workflows add deployment information:
- Locations or computing environments.
- Topology between locations.
- Mapping of workflow steps to resources.
- This separation allows a task to be moved from one location to another by changing the mapping rather than rewriting the workflow logic.
- Doriana described centralized orchestration as a model where one orchestration engine manages the whole workflow.
- Advantages include:
- A global view of workflow state.
- More informed scheduling decisions.
- Easier monitoring and logging.
- Reuse of mature workflow frameworks.
- Disadvantages include:
- A single point of failure.
- Communication overhead between the orchestrator and execution sites.
- Less autonomy for individual execution sites.
- The University of Turin group developed Streamflow for this model.
- Streamflow is:
- CWL-compliant.
- Container-native.
- Designed for workflows across heterogeneous environments.
- Modular, with new environments added through connectors rather than core changes.
- Existing and developing connectors include Slurm, Apptainer, Docker, and quantum backends.
- Doriana described the Common Workflow Language as an open standard for describing command-line tools and reproducible workflows.
- CWL describes inputs, outputs, parameters, and dependencies, but does not itself decide deployment placement.
- Streamflow use cases included:
- Cross-facility federated learning across language-specific LLM training environments and cloud aggregation.
- Machine-learning workflows spanning private environments, cloud, and HPC for cadastral image analysis.
- Inclusion in an Italian AI factory environment.
- Doriana presented QSplit as a workflow-oriented hybrid quantum-classical optimization framework built around Streamflow.
- QSplit targets problems expressed in QUBO form.
- Its goal is to address current quantum-device limits and large problem instances by decomposing large optimization problems.
- The QSplit workflow:
- Takes a dataset of QUBO matrices.
- Dispatches each instance into a solver pipeline.
- Splits large QUBO instances into smaller matrices.
- Solves sub-instances on different backends.
- Aggregates partial results into a global solution.
- Backends can include:
- Quantum simulators on HPC.
- HPC CPU solvers.
- HPC GPU solvers.
- Real QPUs, including IQM in the presented work.
- Lightweight dispatch and collect tasks can run on cloud resources.
- More computationally intensive splitting and aggregation tasks run on HPC.
- Doriana said early max-cut cases with sparse matrices gave results reasonably consistent with simulated annealing, while denser or more heavily split problems showed larger deviation.
- She emphasized that QSplit currently demonstrates orchestration of decomposition, dispatch, execution, and aggregation; it is not itself a new optimization algorithm.
- Masoud Mohseni asked how QSplit decomposes problems into subproblems and whether it identifies quantum-prone subproblems or uses a more generic splitting routine.
- Doriana said the current implementation recursively splits the QUBO matrix into smaller blocks until they reach the desired size.
- Masoud characterized that as closer to load-balanced decomposition than structure-aware decomposition.
- He cautioned that QUBO decomposition is highly non-trivial because variables may have strong graph, frustration, or correlation structure.
- Masoud also cautioned against treating QUBO formulation as a neutral representation:
- Embedding higher-order problems into QUBO can change the complexity of the problem.
- Embedding can introduce correlations and alter relevant gaps, not merely add overhead.
- It is better to stay close to the native problem formulation when possible.
- Doriana clarified that QSplit currently uses QUBO because it is the chosen case study, but the workflow could call other scripts or decomposition strategies if provided.
- She said future work includes adding decomposition strategies and making QSplit more parametric with respect to the strategy used.
- Doriana contrasted centralized orchestration with decentralized orchestration.
- In decentralized orchestration, workflow control and decisions are distributed across multiple components rather than managed by one central orchestrator.
- Advantages include:
- Less dependence on a central controller.
- Lower communication overhead.
- Faster local decisions.
- Greater autonomy for execution sites.
- Better fit for edge or highly distributed environments.
- Challenges include:
- Harder global optimization.
- Harder coordination.
- More difficult fault tolerance because no single component has the full workflow state.
- The group developed SWIR, a scientific workflow intermediate representation, to bridge high-level workflow descriptions and low-level execution environments.
- The compiler currently translates Pegasus workflows, with CWL support under development.
- SWIR represents each location's trace using send/receive-style actions and can be optimized.
- It can be compiled into a distributed executable bundle tailored to the target environment.
- The bundle contains the information each location needs, avoiding the need for a central controller during execution.
- The group is also working on agent-based workflow control using policy constraints and formal semantics.
- Doriana said adding AI agents to workflow management raises verification questions: autonomous decisions must preserve the user's intent and policies across the full workflow lifecycle.
- Doriana listed several future directions:
- Add new backends, including Quantinuum and PASQAL.
- Add new decomposition strategies.
- Extend QSplit to additional use cases beyond QUBO optimization.
- Explore circuit cutting as a workflow-orchestration use case.
- Apply the framework to multi-QPU and multi-HPC scenarios.
- Explore quantum machine learning and quantum optimization workflows.
- Extend SWIR and complete CWL-to-SWIR translation.
- She described circuit cutting as analogous to the existing QSplit pattern:
- Cut the circuit.
- Dispatch pieces to different backends.
- Aggregate results.
- Namit Anand asked about the group's quantum circuit-cutting strategy.
- Doriana said the circuit-cutting work is still early; QSplit currently targets QUBO problems, but the group is interested in expanding it to circuit-cutting pipelines.
- Namit said HPE thinks about circuit cutting often, including using entanglement as a guiding metric, and offered to discuss further.
- Doriana said the group is especially interested in orchestrating the full circuit-cutting pipeline, including multi-QPU and multi-HPC execution.
- Masoud emphasized that circuit cutting should not be done with naive load balancing or gate-by-gate heuristics.
- He said structure-aware cutting can produce large improvements compared with naive cutting:
- Quadratic reductions in exponents for some electronic-structure cases.
- Quadratic reductions in quantum-interconnect requirements.
- Potentially exponential improvements for transverse-Ising-style models.
- Masoud said cuts must account for classical and quantum correlations; cutting strongly communicating blocks can impose exponential costs.
- He also noted that some cuts may need to be adaptive and performed in real time.
- Thomas Naughton asked whether the decentralized workflow system needs new primitives such as consensus, or whether those can be represented as normal workflow nodes.
- Doriana said that for now they are represented as nodes, but she expects additional mechanisms may be needed.
- Thomas asked how the system surfaces dynamic behavior from resources, such as slowdowns, changing availability, or connectivity changes, and how that feeds scheduling decisions.
- Doriana said Streamflow already has a scheduler that can react to some dynamic conditions:
- If a location is down.
- If queue conditions make another location preferable.
- If topology constrains data movement between locations.
- She said the current quantum integration is less mature:
- New quantum connectors and scheduling policies were added.
- Current assumptions are closer to "everything is connected."
- Backend-specific information from Quantinuum, PASQAL, and other systems needs better integration.
- Thomas asked whether quantum calibration events could trigger revisiting earlier workflow steps such as compilation.
- Doriana said QSplit/Streamflow does not currently have a primitive for calibration events, but such behavior could be modeled as a loop triggered by events.
- Thomas noted that quantum-specific dynamic events could be an interesting use case for workflow systems.
- Workflow orchestration is a central systems problem for quantum-HPC integration because applications increasingly span multiple heterogeneous resources.
- Separating workflow logic from deployment mapping improves portability and makes it easier to move tasks across resources.
- Streamflow provides a CWL-compliant, connector-based centralized orchestration model for heterogeneous workflows.
- QSplit demonstrates a workflow-oriented approach to decomposing and dispatching hybrid quantum-classical optimization tasks.
- QUBO splitting and circuit cutting must become structure-aware; naive load-balanced decomposition can miss important correlations and create large hidden costs.
- Decentralized orchestration and intermediate representations such as SWIR may reduce overhead and improve autonomy, but introduce hard coordination and fault-tolerance questions.
- Quantum-specific events such as calibration changes and backend availability need to be integrated into workflow scheduling models.