Short-term load forecasting is essential for reliable energy management, but practical deployment on edge devices requires models that remain accurate under limited memory, finite measurement budgets, and hardware noise. This work proposes a hardware-efficient Quantum Reservoir Computing (QRC) framework for energy load forecasting, where a fixed quantum reservoir transforms temporal input windows into high-dimensional features and only a classical Elastic Net readout is trained. To reduce deployment cost, the trained readout is compressed using post-training fixed-point quantization at bit widths from 8 to 2 bits. The framework is evaluated on the Tetouan and Spain energy load datasets under exact statevector simulation, 512-shot finite sampling, and realistic hardware-noise models from IBM FakeTorino and IBM FakeMarrakesh. Results show that 6-bit readout precision preserves full-precision forecasting performance while reducing readout memory by 81.2%. Below this point, degradation becomes dataset dependent, with Tetouan showing stronger sensitivity and Spain degrading more gradually. Hardware-noise validation further shows that the trained readout transfers to noisy reservoir states without retraining. These findings support quantized QRC as a resource-aware forecasting approach for near-term quantum time-series applications.

翻译：短期负荷预测对于可靠能源管理至关重要，但在边缘设备上的实际部署需要模型在有限内存、有限测量预算和硬件噪声条件下仍能保持准确性。本文提出了一种硬件高效的量子储层计算框架用于能源负荷预测，其中固定量子储层将时间输入窗口转换为高维特征，仅训练经典的弹性网络读出层。为降低部署成本，训练后的读出层采用训练后定点量化压缩为8至2比特位宽。该框架在得土安和西班牙能源负荷数据集上进行了评估，测试条件包括精确态矢量模拟、512次有限采样以及来自IBM FakeTorino和IBM FakeMarrakesh的真实硬件噪声模型。结果表明，6比特读出精度可保持全精度预测性能，同时将读出内存减少81.2%。低于该阈值时，性能下降具有数据集依赖性，得土安数据集的敏感性更强，而西班牙数据集下降更为平缓。硬件噪声验证进一步表明，训练后的读出层可迁移至噪声储层状态而无需重新训练。这些发现支持量化QRC作为近期量子时间序列应用中一种资源感知的预测方法。