Late Breaking Results: Hardware-Efficient Quantum Reservoir Computing via Quantized Readout

2026-04-07

AI Summary

This work addresses the challenge of achieving high-accuracy short-term electric load forecasting on resource-constrained edge devices by proposing a fixed-structure quantum reservoir computing approach. The method integrates Chebyshev feature encoding, a brickwall-type entangling circuit, and Pauli measurements to circumvent the need for quantum backpropagation. Innovatively, post-training fixed-point quantization is introduced in the readout layer, and a genetic algorithm is employed to optimize the overall architecture. Evaluated on the Tetouan city dataset, 8-bit and 6-bit quantization schemes reduce memory usage by 75% and 81%, respectively, while limiting prediction accuracy degradation to within 1% of the FP32 baseline, thereby significantly enhancing feasibility for edge deployment.

Abstract

Due to rising electricity demand, accurate short-term load forecasting is increasingly important for grid stability and efficient energy management, particularly in resource-constrained edge settings. We present a hardware-efficient Quantum Reservoir Computing (QRC) framework based on a fixed, untrained quantum circuit with Chebyshev feature encoding, brickwork entanglement, and single- and two-qubit Pauli measurements, avoiding quantum backpropagation entirely. Using the Tetouan City Power Consumption dataset, we examine the effect of post-training fixed-point quantization on the classical readout layer, with the reservoir architecture selected through a genetic search over 18 candidate configurations. Under finite-shot evaluation, 8-bit and 6-bit quantization maintain forecasting accuracy within 1% of the FP32 baseline while reducing readout memory by 75% and 81%, respectively. These results suggest that quantized readout can improve the hardware efficiency and deployment practicality of QRC for memory-constrained energy forecasting.

Problem

• Quantum Reservoir Computing
• short-term load forecasting
• hardware efficiency
• quantized readout
• edge computing

Innovation

• Quantum Reservoir Computing
• Fixed-point quantization
• Hardware efficiency
• Chebyshev feature encoding
• Genetic architecture search