We investigate a digital quantum reservoir computing (QRC) framework for multi-step forecasting of automated teller machine (ATM) cash demand time series on near-term quantum devices. The proposed approach uses parametrized four-qubit reservoirs with a fixed structure exploiting partial measurement and reset, where temporal data is encoded in rotation angles. Training is restricted to a classical Ridge-regression readout. We systematically analyze the impact of the circuit ansatzë, reservoir memory, measurement-derived observables, and the execution backend on the forecasting performance. Experiments are performed with noiseless simulation, noise-aware emulation, and a real IQM Spark quantum processor. Although the QRC models do not outperform the classical Prophet benchmark in terms of Mean Absolute Error and Normalized Mean Squared Error metrics, they achieve more competitive results in Dynamic Time Warping metric, indicating a partial ability to capture temporal structure. These findings provide an empirical assessment of digital QRC for realistic financial forecasting and highlight both its current limitations and its potential on near-term quantum hardware.

翻译：我们研究了一种数字量子储层计算（QRC）框架，用于在近期量子设备上实现自动柜员机（ATM）现金需求时间序列的多步预测。所提出的方法采用固定结构的参数化四量子比特储层，利用部分测量与重置，将时间数据编码为旋转角度。训练仅限于经典岭回归（Ridge-regression）读出。我们系统分析了电路ansatz、储层记忆、测量衍生可观测量以及执行后端对预测性能的影响。实验通过无噪声模拟、噪声感知仿真以及真实的IQM Spark量子处理器进行。尽管QRC模型在平均绝对误差（Mean Absolute Error）和归一化均方误差（Normalized Mean Squared Error）指标上未超越经典的Prophet基准模型，但在动态时间规整（Dynamic Time Warping）指标上取得了更具竞争力的结果，表明其具备部分捕捉时间结构的能力。这些发现为数字QRC在现实金融预测中的应用提供了实证评估，并揭示了其当前局限性以及在近期量子硬件上的潜力。