Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

• URL: http://arxiv.org/abs/2101.10657v2
• Date: Thu, 28 Jan 2021 09:31:29 GMT
• Title: Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
• Authors: Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Silvia Liberata Ullo
• Abstract summary: This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.
• Score: 63.69764116066747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.

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