Presentation
A Scalability Study of Quantum Algorithms for Dimensionality Reduction of Multidimensional Data
DescriptionQuantum computing promises exponential speedups over classical computing by leveraging quantum-mechanical properties like superposition and entanglement. As quantum algorithms grow in complexity, classical simulation remains essential for evaluating correctness, scalability, and resource demands. This work focuses on studying the scalability of structured quantum algorithms such as the Quantum Haar Transform (QHT), usually used for reducing data dimensionality in signal/image processing and remote-sensing hyperspectral imagery. We simulate QHT circuits on high performance computing (HPC) systems by constructing unitary models that mirror the transform’s hierarchical decomposition. Simulations track performance metrics such as circuit width, circuit depth, and execution time. Our results provide insight into the practical implementation of structured quantum circuits and serve as a reference for validating algorithmic correctness and guiding future quantum algorithm design.
Authors
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
Archive
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