Quantum matching pursuit: A quantum algorithm for sparse representations
نویسندگان
چکیده
Representing signals with sparse vectors has a wide range of applications that from image and video coding to shape representation health monitoring. In many real-time requirements, or deal high-dimensional signals, the computational complexity encoder finds plays an important role. Quantum computing recently shown promising speed-ups in learning tasks. this work, we propose quantum version well-known matching pursuit algorithm. Assuming availability fault-tolerant random access memory, our lowers its classical counterpart polynomial factor, at cost some error computation inner products, enabling signals. Besides proving new algorithm, provide numerical experiments show is negligible practice. This work opens path further research on algorithms for finding representations, showing suitable signal processing.
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ژورنال
عنوان ژورنال: Physical review
سال: 2022
ISSN: ['0556-2813', '1538-4497', '1089-490X']
DOI: https://doi.org/10.1103/physreva.105.022414