论文标题
使用量子计算机加速地理空间数据处理的方法
Methods for Accelerating Geospatial Data Processing Using Quantum Computers
论文作者
论文摘要
量子计算是一种变革性技术，具有通过加速优化和机器学习过程来增强空间行业的运营。机器学习过程可以在地理空间数据中进行自动图像分类。新的量子算法提供了解决这些问题的新颖方法，并且比当前的经典技术具有潜在的未来优势。目前通过Rigetti Computing和其他提供商正在开发的通用量子计算机使能够执行完全通用的量子算法，并且在某些情况下，理论上证明了对经典算法的速度。本文介绍了一种使用通用量子增强到卷积神经网络的卫星图像分类方法：Quanvolutional神经网络。使用精致的方法，我们发现了该域中先前量子努力的性能改善，并确定了可能导致最终量子优势的潜在改进。我们使用SAT-4卫星图像数据集对这些网络进行基准测试，以证明机器学习技术在太空行业中的实用性以及量子机器学习可以提供的潜在优势。
Quantum computing is a transformative technology with the potential to enhance operations in the space industry through the acceleration of optimization and machine learning processes. Machine learning processes enable automated image classification in geospatial data. New quantum algorithms provide novel approaches for solving these problems and a potential future advantage over current, classical techniques. Universal Quantum Computers, currently under development by Rigetti Computing and other providers, enable fully general quantum algorithms to be executed, with theoretically proven speed-up over classical algorithms in certain cases. This paper describes an approach to satellite image classification using a universal quantum enhancement to convolutional neural networks: the quanvolutional neural network. Using a refined method, we found a performance improvement over previous quantum efforts in this domain and identified potential refinements that could lead to an eventual quantum advantage. We benchmark these networks using the SAT-4 satellite imagery data set in order to demonstrate the utility of machine learning techniques in the space industry and the potential advantages that quantum machine learning can offer.
