Engineering topics

Gottiparthi, Kalyan [ORNL] (ORCID:0000000213540255)

Publications and source records attributed to Gottiparthi, Kalyan [ORNL] (ORCID:0000000213540255).

4 records

DOE OSTI2026

Quantum Solver Using Singular Value Decomposition for Computational Fluid Dynamics

Numerical solutions for fluid flow problems are challenging and have been focus of Computational Fluid Dynamics (CFD) research for past several decades. The advent of quantum computing promises exponential speedup in comparison to existing classical methods and alleviate computational constraints posed by CFD problems. Although solutions for most problems of interest in fluid dynamics using quantum computing are distant, recent advances in algorithms, software and hardware provide a path towards realizing this goal. Quantum linear solver algorithms (QLSA) such as Harrow–Hassidim–Lloyd (HHL) and Variational Quantum Linear Solver (VQLS) have been successfully implemented to solve for canonical problems such as Hele-Shaw flow. However, these algorithms still suffer to scale and address problems with ill-conditioned Jacobians. In the current paper, we alleviate these restrictions with a new quantum solver based on Singular Value Decomposition (SVD) and simulate flow past a 2D cylinder. The fidelity of the SVD based quantum solver in predicting the flow past 2D cylinder is computed along with an assessment of errors. Classical and quantum solutions for the flow are compared for different resolutions. Finally, we discuss variation in the solutions based on number of shots used.

Gottiparthi, Kalyan [ORNL] (ORCID:0000000213540255↗

DOE OSTI2025

Assessing VQLS for Fluid Dynamics on a Hybrid Quantum-HPC Stack

Recent advances in quantum linear solvers offer a promising direction for accelerating extreme scientific computations such as fluid dynamics. However, the deep and complex circuits required by many quantum algorithms limit their practical use on current quantum hardware. The Variational Quantum Linear Solver (VQLS) presents a viable alternative for near-term quantum devices (NISQ), and initial efforts have explored its application to select fluid dynamics problems. In this work, we evaluate the use of VQLS for canonical fluid dynamics problems, aiming to identify pathways for generalizing its implementation across a broader class of systems. We analyze the impact of various circuit ansatz and classical optimizers on solution quality and convergence behavior. Furthermore, we assess the algorithm's feasibility within a hybrid quantum–high-performance computing (HPC) framework by porting it to QFw, a state-of-the-art quantum-HPC software stack. 11This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US DOE under Contract No. DE-AC05-00OR22725.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗

DOE OSTI2025

Practical Scalability of LuGo: Benchmarking the HHL Algorithm Using an Enhanced QPE Algorithm

The HHL algorithm is a prominent quantum algorithm that offers exponential speedup over its classical counterparts for solving a system of linear equations. However, synthesizing and executing HHL circuits demand significant computational resources from both classical and quantum systems. In this paper, we benchmark the HHL algorithm using the optimized Quantum Phase Estimation (QPE) generation algorithm, LuGo \cite{lu2025lugo}, to enhance its scalability and efficiency. We leverage the National Energy Research Scientific Computing Center's (NERSC) Perlmutter supercomputer to evaluate the scalability of generating HHL circuits and to measure the time to simulate the generated circuits. Additionally, we provide a comprehensive analysis of the algorithm's performance on various state-of-the-art superconducting and trapped-ion quantum devices, including studies on qubit connectivity, fidelity comparisons, and hardware compatibility and robustness. Our results offer preliminary insights into potential practical applications of the HHL algorithm enabled by LuGo and the performance of various types of quantum hardware.

Lu, Chao [ORNL] (ORCID:0000000179346933)↗

DOE OSTI2025

Comparison of Artificial Compressibility Methods for Coupled Laminar Fluid Flow and Heat Transfer

Artificial compressibility (AC) methods for incompressible flows perturb the mass continuity equation by the time derivative of the pressure field to achieve direct coupling between pressure and velocity without the need for a projection method. Several variations of the original AC method have been proposed (e.g., the entropically damped artificial compressibility [EDAC] method) that can, in some scenarios, improve on the original model. In this paper, we methodically compare the results of the original AC model and the EDAC model for both steady-state and transient problems with coupling between the momentum and energy equations. We demonstrate that the EDAC model and the AC model yield similar numerical results, and the EDAC model is 10%-20% faster than the AC model for a given problem.

Oz, Furkan [ORNL] (ORCID:0000000265831724)↗