Augmenting classical HPC with Quantum Computing Capabilities for Computational Fluid Dynamics (CFD)

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software; Quantum Science The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: DTRA has a need to perform high-fidelity CFD modeling of blast and shock phenomenology and associated test and evaluation activities to increase accuracy or reduce the time to solution of air blast predictions for counter weapons of mass destruction (C-WMD) defeat and deny tactics. These simulations are technically and computationally challenging. The objective is to investigate tools and techniques for offload of portions of traditional high fidelity CFD simulations from classical, silicon based High Performance Computing (HPC) systems to use of a Quantum Computer where such offloading will improve fidelity or speed of computation. The use of software that can simulate quantum effects and associated programming environment or use of a performer provided Hybrid Quantum / Classical computer is acceptable. DESCRIPTION: The last decade has seen a tremendous amount of activity and developments in the field of Quantum Computing, Artificial Neural Networks and Hybrid Quantum / Classical computing. While classical HPC systems can be utilized for a broad range of algorithms and programming models and techniques are well understood. Quantum Computing and its programming is less mature and use of Quantum to augment classical HPC is a steppingstone in evaluation of its applicability for portions of DTRA's HPC workloads. For this effort the performer will Solve Partial Differential Equations (PDEs) that are relevant in the field of Computational Fluid Dynamics (CFD) using a Quantum Computer/ Quantum Simulator or replace PDEs and other aspects of a CFD application code by use of a Physics Informed Neural Network with offload to a Quantum Computer/ Quantum Simulator to use Quantum Physics Informed Neural Networks (QPINN)s to increase speed or fidelity of CFD calculations. This research will align with the long-term goal of integrating quantum computing capabilities into the Department of Defense High Performance Computing Modernization Program (DoD HPCMP). In accordance with DoD HPCMP user access requirements, offerors must meet all DoD HPCMP user requirements for access to these systems which includes, but is not limited to, possessing a Security Clearance or having a National Agency Check with Inquiries (NACI). DTRA will provide an allocation of HPC system resources and assistance in obtaining successful offeror's user accounts on DoD HPCMP system(s). All performer provided high-performance computing (HPC) resources and quantum computing systems utilized in support of this project must be located within the United States of America. This includes all hardware, software, data storage, and associated infrastructure. PHASE I: Define and develop Quantum PDEs and /or QPINNs to improve fidelity or accelerate execution of CFD algorithms for use in performing simulations of air blast including effects of terrain and blast impact on structures. PHASE II: Further develop, test and optimize the selected approach to extend the range of applicability. Demonstrate use of Hybrid HPC / Quantum (physical or simulator). Perform detailed comparisons with the same cases on HPC systems. The performer may select the CFD code of their choice. Test cases should include air blast, air blast with terrain, and air blast impacting a structure. Comparison with experimental results or closed form solutions are desirable. Generalize and document for pre-commercial release. Quantum software should be modularized as a library to facilitate incorporation into other application software. PHASE III: In addition to implementing further improvements that would enhance use of the developed product by the sponsoring office, identify and exploit features that would be attractive for commercial or other private sector HPC applications. The software developed should be applicable to other CFD workloads. Investigate commercialization avenues that could include other government agencies, national labs, research institutes, and defense contractors. Develop a plan to enable successful technology transition at the end of this phase. REFERENCES: Syamlal, M., Copen, C., Takahashi, M., & Hall, B. (2024). Computational Fluid Dynamics on Quantum Computers. 29 July-2 August 2024 Aviation Forum, Las Vegas, NV. AIAA AVIATION. https://arxiv.org/pdf/2406.18749 Oz, F., San, O. & Kara, K. An efficient quantum partial differential equation solver with Chebyshev points. Sci Rep 13, 7767 (2023). https://doi.org/10.1038/s41598-023-34966-3 https://www.nature.com/articles/s41598-023-34966-3.pdf George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422 440, 2021. https://www.researchgate.net/profile/Lu-Lu-51/publication/351814752_Physics- informed_machine_learning/links/60ae8f43a6fdcc647ede90f7/Physics-informed-machine-learning.pdf Quantum Physics-Informed Neural Networks Corey Trahan Mark Loveland and Samuel Dent U.S. Army Engineer Research and Development Center, Information and Technology Laboratory, 3909 Halls Ferry Rd., Vicksburg, MS 39180, USA. Entropy 2024, 26(8), 649; https://doi.org/10.3390/e26080649 https://www.mdpi.com/1099-4300/26/8/649/pdf?version=1723206878 Quantum Physics Informed Neural Networks Pratibha Raghupati Hegde, Stefano Markidis ICPP Workshops '24: Workshop Proceedings of the 53rd International Conference on Parallel Processing Pages 114 115 https://doi.org/10.1145/3677333.3678272 Published: 12 August 2024 https://dl.acm.org/doi/pdf/10.1145/3677333.3678272 DoD HPC Getting Started https://centers.hpc.mil/users/index.html#accounts KEYWORDS: Quantum Computing, High Performance Computing; HPC; Artificial Intelligence; Neural Networks; Physics; Partial Differential Equations, Air-Blast, Computational Fluid Dynamics