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

TL;DR

This paper introduces a hardware-efficient quantum reservoir computing framework with quantized readout, achieving near-baseline accuracy while significantly reducing memory requirements for energy load forecasting.

Contribution

It demonstrates that fixed-point quantization of the readout layer preserves accuracy and enhances hardware efficiency in quantum reservoir computing for energy forecasting.

Findings

8-bit quantization maintains accuracy within 1% of FP32 baseline.

6-bit quantization reduces readout memory by 81%.

The framework uses a fixed, untrained quantum circuit with Chebyshev encoding.

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.