The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly, as in other research communities, also in remote sensing (RS), it is not yet defined how its applications can benefit from the usage of quantum computing (QC). This letter proposes a formulation of the support vector regression (SVR) algorithm that can be executed by D-Wave quantum computers. Specifically, the SVR is mapped to a quadratic unconstrained binary optimization (QUBO) problem that is solved with quantum annealing (QA). The algorithm is tested on two different types of computing environments offered by D-Wave: the advantage system, which directly embeds the problem into the quantum processing unit (QPU), and a hybrid solver that employs both classical and QC resources. For the evaluation, we considered a biophysical variable estimation problem with RS data. The experimental results show that the proposed quantum SVR implementation can achieve comparable or, in some cases, better results than the classical implementation. This work is one of the first attempts to provide insight into how QA could be exploited and integrated in future RS workflows based on machine learning (ML) algorithms.

中文翻译：

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用遥感方法估计水中叶绿素浓度的量子 SVR

量子计算机的日益普及促使人们研究它们在提高数据分析算法性能方面的潜在能力。同样，与其他研究社区一样，在遥感 (RS) 领域，也尚未定义其应用如何从量子计算 (QC) 的使用中受益。这封信提出了可以由 D-Wave 量子计算机执行的支持向量回归 (SVR) 算法的公式。具体来说，SVR 被映射到通过量子退火 (QA) 解决的二次无约束二元优化 (QUBO) 问题。该算法在 D-Wave 提供的两种不同类型的计算环境中进行了测试：优势系统，将问题直接嵌入到量子处理单元 (QPU) 中，以及采用经典和 QC 资源的混合求解器。对于评估，我们考虑了 RS 数据的生物物理变量估计问题。实验结果表明，所提出的量子 SVR 实现可以实现与经典实现相当或在某些情况下更好的结果。这项工作是深入了解如何在基于机器学习 (ML) 算法的未来 RS 工作流中利用和集成 QA 的首次尝试之一。