The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the data in a reasonable time. With that in mind information system engineers are required to investigate different approaches to the data processing. The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to investigate their application to real-life computational problem. This field of study is called quantum (information) systems engineering and usually focuses on technical problems with the contemporary devices. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using multispectral image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. To quantify how and explain why the performance of our model changed when ran on a real device, we propose new explainability metrics. These metrics introduce new meaning to the explainable quantum machine learning; the explanation of the performance issue comes from the quantum device behavior. We also analyzed the expected money costs of running similar experiment on contemporary quantum devices using standard market prices.

翻译：大数据的出现改变了我们处理信息系统工程的方式。如今，当我们能够利用遥感技术进行大数据采集时，此类数据所带来的问题依然至关重要。其中之一便是数据处理问题。传统方法往往难以解决该问题，或无法在合理时间内完成数据处理。鉴于此，信息系统工程师需要探索不同的数据处理方法。近期嘈杂中型量子（NISQ）设备实现的进展，使我们能够研究其在实际计算问题中的应用。这一研究领域被称为量子（信息）系统工程，通常聚焦于当代设备的技术难题。然而，硬件挑战并非阻碍我们量子计算能力的唯一因素。软件限制是这枚硬币的另一面，且较少被深入探讨。以多光谱图像分割作为任务示例，我们研究了在真实、公开可用的量子设备上运行混合量子-经典模型的难度。为量化并解释模型在真实设备上运行时性能变化的原因，我们提出了新的可解释性度量指标。这些指标为可解释量子机器学习赋予了新内涵：性能问题的解释源于量子设备的行为特性。我们还基于标准市场价格，分析了在当代量子设备上运行类似实验的预期资金成本。