RMACC HPC Symposium 2025

A universal quantum computer can be used as a simulator capable of predicting properties of diverse quantum systems. Electronic structure problems in chemistry offer practical use cases around the hundred-qubit mark. This appears promising since current quantum processors have reached these sizes. However, mapping these use cases onto quantum computers yields deep circuits, and for pre-fault-tolerant quantum processors, the large number of measurements to estimate molecular energies leads to prohibitive runtimes. As a result, realistic chemistry is out of reach of current quantum computers in isolation. A natural question is whether classical distributed computation can relieve quantum processors from parsing all but a core, intrinsically quantum component of a chemistry workflow. Here, we incorporate quantum computations of chemistry in a quantum-centric supercomputing architecture, using up to 6400 nodes of the supercomputer Fugaku to assist a quantum computer with a Heron superconducting processor. We simulate the N2 triple bond breaking in a correlation-consistent cc-pVDZ basis set, and the active-space electronic structure of [2Fe-2S] and [4Fe-4S] clusters, using 58, 45 and 77 qubits respectively, with quantum circuits of up to 10570 (3590 2-qubit) quantum gates. We obtain our results using a class of quantum circuits that approximates molecular eigenstates, and a hybrid estimator. The estimator processes quantum samples, produces upper bounds to the ground-state energy and wavefunctions supported on a polynomial number of states. This guarantees an unconditional quality metric for quantum advantage, certifiable by classical computers at polynomial cost. For current error rates, our results show that classical distributed computing coupled to quantum computers can produce good approximate solutions for practical problems beyond sizes amenable to exact diagonalization.

The University of North Dakota (UND) Genomics Core has launched GenomEX 2.0, the first comprehensive and user-friendly bioinformatics platform powered by Oracle Cloud Infrastructure. This innovative platform enables biologists to seamlessly install over 13,000 bioinformatics tools, generate and execute custom code or command lines, and receive real-time guidance from an AI-based bioinformatics assistant—all through intuitive, one-click processes.
To support the computing requirements for any bioinformatics tools, the platform is powered by the Oracle Cloud Infrastructure that provides fully secured (built-in security features and compliance certifications), personalized (adjustable CPU/GPU numbers & memory/storage capacity), dedicated (resources available 24/7 without any queue) and customizable (users have administrator rights) cloud-based high-performance computing environments at unbeatable pricing.
Through the combined expertise of the UND Genomics Core and Oracle, GenomEX 2.0 emerges as a powerful and unique bioinformatics platform, providing every biologist with the freedom to explore biological data independently, regardless of their coding proficiency.

As research data volumes grow and mandates for data publication become more pervasive, automated means for managing these complex workflows to ensure data integrity have a growing role in modern science. In this session we will introduce Globus Flows, a foundational service for orchestrating secure and reliable data management tasks at scale, and Globus Compute, a service which enables you to execute functions on diverse remote systems. We will describe how Globus Flows and Compute fit into the Globus ecosystem of data and compute management services, and how flows can feed into downstream data portals, science gateways, and data commons, enabling search and discovery of data by the broader community. We will demonstrate how to run various Globus provided flows and discuss initiating flows with triggers and inserting compute tasks into your flows. We will conclude with an interactive tutorial detailing how to build custom flows using Jupyter notebooks and the Globus web app.

How can your research teams leverage and apply high performance computing to supercharge your own efforts for research momentum to get to outcomes faster?

When generative AI (GenAI) was first introduced, our AI team identified the proposal process as a top use case for GenAI augmentation. Our AI experts set out to build a GenAI-powered application to help the Proposal team streamline the complex RFP process. The goal was to build an intelligent assistant that accelerated RFP qualification, helped proposal managers quickly grasp the key aspects of lengthy RFP document sets, and searched multiple databases for relevant information to automatically generate a quality response draft.

The now successful internally developed GenAI solution significantly shortens the Proposal team's response time, allowing them to answer to more proposals and spend more time on response quality and win strategies. This use case will be expanded to support the whole process of winning customer engagement, from drafting Statements of Work (SOWs) to proposal PowerPoint presentations.

In the spirit of exploring the art of the possible, we will share a live demo of capability in use for creating proposals and responses for project funding.

This presentation highlights the rapid growth in Field Effect Transistor (FET) technology, with modern GPUs containing over 200 billion transistors each. As transistor counts soar, so does power consumption—Nvidia’s shipped GPUs alone consumed over 14 TWh in 2023. With global data center energy demands projected to triple, the presentation advocates for transitioning data from power-hungry disk storage to more energy-efficient systems. This shift offers significant power savings, lower carbon emissions, and cost reductions—providing a scalable path to support future computing growth without overloading existing power infrastructure.

Fuzzball, from CIQ, is a new way to access and manage HPC resources, whether they're on-prem, in the cloud, or both. Fuzzball is API-driven, container-forward, and offers co-equal web and command-line interfaces. With its well-defined workflow definitions, including first-class support for container management and data ingress and egress, Fuzzball workflows are portable to both local clusters and cloud resources.

In this session we will give an overview of Fuzzball, describe some of its potential use cases, and highlight some of its unique features, including a visual workflow builder, portable workflows, and its templated workflow catalog.