5 papers
Quantum Lattice Boltzmann Solutions for Transport under 3D Spatially Varying Advection on Trapped Ion Hardware
Sayonee Ray, Jezer Jojo, Jason Iaconis +5
The Quantum Lattice Boltzmann Method (QLBM) has emerged as one of the most promising quantum computing approaches for the numerical simulation of problems in computational fluid dy…
Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware
Carlos Flores-Garrigós, Anton Simen, Qi Zhang +6
We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies…
Measuring what matters: A scalable framework for application-level quantum benchmarking
Willie Aboumrad, Claudio Girotto, Joshua Goings +16
As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metri…
Algorithmic Advances Towards a Realizable Quantum Lattice Boltzmann Method
Apurva Tiwari, Jason Iaconis, Jezer Jojo +4
The Quantum Lattice Boltzmann Method (QLBM) is one of the most promising approaches for realizing the potential of quantum computing in simulating computational fluid dynamics. Man…
End-to-End Demonstration of Quantum Generative Adversarial Networks for Steel Microstructure Image Augmentation on a Trapped-Ion Quantum Computer
Samwel Sekwao, Jason Iaconis, Claudio Girotto +6
Generative adversarial networks (GANs) are a machine learning technique capable of producing high-quality synthetic images. In the field of materials science, when a crystallograph…