Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems

2026-06-10

AI Summary

This work addresses the challenge of achieving high-accuracy short-term electric load forecasting on edge devices under constraints of limited memory, restricted measurement budgets, and hardware-induced noise. The authors propose a hybrid architecture combining a fixed quantum reservoir with a classical Elastic Net readout layer and, for the first time, integrate post-training low-bitwidth fixed-point quantization (2–8 bits) into the readout stage of quantum reservoir computing. Experimental results on the Tetouan and Spain datasets demonstrate that 6-bit quantization preserves full-precision performance while reducing readout memory usage by 81.2%. Although lower bitwidths induce slight performance degradation—exhibiting dataset-dependent behavior—the model maintains strong transferability under IBM’s quantum noise model without requiring retraining.

Abstract

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.

Problem

• Short-term load forecasting
• Resource-constrained systems
• Hardware noise
• Memory efficiency
• Quantum computing

Innovation

• Quantum Reservoir Computing
• Post-training Quantization
• Short-term Load Forecasting
• Hardware-efficient AI
• Noise-robust Quantum Models