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[bibtex]@InProceedings{Poduval_2024_CVPR, author = {Poduval, Prathyush Prasanth and Zou, Zhuowen and Imani, Mohsen}, title = {HDQMF: Holographic Feature Decomposition Using Quantum Algorithms}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {10978-10987} }
HDQMF: Holographic Feature Decomposition Using Quantum Algorithms
Abstract
This paper addresses the decomposition of holographic feature vectors in Hyperdimensional Computing (HDC) aka Vector Symbolic Architectures (VSA). HDC uses high-dimensional vectors with brain-like properties to represent symbolic information and leverages efficient operators to construct and manipulate complexly structured data in a cognitive fashion. Existing models face challenges in decomposing these structures a process crucial for understanding and interpreting a composite hypervector. We address this challenge by proposing the HDC Memorized-Factorization Problem that captures the common patterns of construction in HDC models. To solve this problem efficiently we introduce HDQMF a HyperDimensional Quantum Memorized-Factorization algorithm. HDQMF is unique in its approach utilizing quantum computing to offer efficient solutions. It modifies crucial steps in Grover's algorithm to achieve hypervector decomposition achieving quadratic speed-up.
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