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Full-Text Articles in Physical Sciences and Mathematics
Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides
Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides
The Graduate School Posters
Future particle accelerators will exceed by far the current data size (1015) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …
Scalable Quantum Edge Detection Method For D-Nisq Imaging Simulations: Use Cases From Nuclear Physics And Medical Image Computing, Emmanuel Billias, Nikos Chrisochoides
Scalable Quantum Edge Detection Method For D-Nisq Imaging Simulations: Use Cases From Nuclear Physics And Medical Image Computing, Emmanuel Billias, Nikos Chrisochoides
The Graduate School Posters
Edge Detection is one of the computationally intensive modules in image analysis. It is used to find important landmarks by identifying a significant change (or “edge”) between pixels and voxels. We present a hybrid Quantum Edge Detection method by improving three aspects of an existing widely referenced implementation, which for our use cases generates incomprehensible results for the type and size of images we are required to process. Our contributions are in the pre- and post-processing (i.e., classical phase) and a quantum edge detection circuit: (1) we use space- filling curves to eliminate image artifacts introduced by the image decomposition, …
Hydrodynamics And Sediment Transport In The Tidally Influenced James River, Ollie Gilchrest, Rip Hale
Hydrodynamics And Sediment Transport In The Tidally Influenced James River, Ollie Gilchrest, Rip Hale
The Graduate School Posters
The tidally influenced James River is an important economic, ecologic, and cultural resource for VA residents. Tidal rivers have been historically understudied, however they are critical transition zones, the dynamics of which have implications for freshwater supply and sediment trapping. Globally, estimates suggest that >30% of fluvial sediment is trapped in the tidal zone, the location and dynamics of which are actively changing due to sea level rise and saltwater encroachment. In addition, analysis of historical water levels on the James River has shown a decrease in the tidal range since 1940. The present study combines >1-year’s worth of hydrographic …
Point Cloud-Based Mapper For Qcd Analysis, Tareq Alghamdi, Yasir Alanazi, Manal Almaeen, Nobuo Sato, Yaohang Li
Point Cloud-Based Mapper For Qcd Analysis, Tareq Alghamdi, Yasir Alanazi, Manal Almaeen, Nobuo Sato, Yaohang Li
The Graduate School Posters
In many scientific applications, Inverse problems are challenging. An inverse problem is the process of inferring unknown parameters from observable ones. In this poster, we present our prototype using Point Cloud-based Variational Autoencoder mapping. Data that connects parameters to detector level events is used to train the proposed model. A point cloud is used to describe a series of events that keeps the permutation invariant property and geometric correlations of the events while being flexible with the number of events in the input. The trained Point Cloud-based Variational Autoencoder functions as an effective inverse function from detector level events to …