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محاسبات کوانتومی - Quantum-Computing
عنوان انگلیسی مقاله:
Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data
ترجمه فارسی عنوان مقاله:
کاهش ابعاد کوانتومی برای تشخیص الگو در داده های فضایی-طیفی با وضوح بالا
ieee - IEEE TRANSACTIONS ON COMPUTERS, VOL: 71, NO: 1, JANUARY 2022
Naveed Mahmud , Student Member, IEEE, Bennett Haase-Divine, Student Member, IEEE, Andrew MacGillivray , and Esam El-Araby , Senior Member, IEEE
The promises of advanced quantum computing technology have driven research in the simulation of quantum computers on
classical hardware, where the feasibility of quantum algorithms for real-world problems can be investigated. In domains such as High
Energy Physics (HEP) and Remote Sensing Hyperspectral Imagery, classical computing systems are held back by enormous readouts
of high-resolution data. Due to the multi-dimensionality of the readout data, processing and performing pattern recognition operations
for this enormous data are both computationally intensive and time-consuming. In this article, we propose a methodology that utilizes
Quantum Haar Transform (QHT) and a modified Grover’s search algorithm for time-efficient dimension reduction and dynamic pattern
recognition in data sets that are characterized by high spatial resolution and high dimensionality. QHT is performed on the data to
reduce its dimensionality at preserved spatial locality, while the modified Grover’s search algorithm is used to search for dynamically
changing multiple patterns in the reduced data set. By performing search operations on the reduced data set, processing overheads
are minimized. Moreover, quantum techniques produce results in less time than classical dimension reduction and search methods.
The feasibility of the proposed methodology is verified by emulating the quantum algorithms on classical hardware based on field
programmable gate arrays (FPGAs). We present designs of the quantum circuits for multi-dimensional QHT and multi-pattern Grover’s
search. We also present two emulation techniques and the corresponding hardware architectures for this methodology. A high
performance reconfigurable computer (HPRC) was used for the experimental evaluation, and high-resolution images were used as the
input data set. Analysis of the methods and implications of the experimental results are discussed.
Index Terms— Quantum computing | field-programmable gate arrays (FPGAs)