Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems
For energy system operators deploying forecasting on resource-constrained edge devices, this work demonstrates a practical quantum-classical hybrid approach that maintains accuracy under limited memory and hardware noise.
This work proposes a quantum reservoir computing framework for short-term power load forecasting that uses a fixed quantum reservoir and a compressed classical readout. With 6-bit readout precision, it preserves full-precision performance while reducing memory by 81.2%, validated under hardware noise.
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.