Quantum Reservoir Computing with 8-Bit Quantized Readout Matches FP32 Accuracy for Power Grid Forecasting

Quantum + Classical = Practical: 8-Bit Quantized Readout Maintains Full Accuracy in Quantum Reservoir Computing

Researchers have demonstrated that post-training quantization of the classical readout layer in Quantum Reservoir Computing (QRC) systems can reduce memory usage by 75-81% while maintaining forecasting accuracy within 1% of full precision.

The Framework

The QRC system uses:

• Fixed, untrained quantum circuit — No quantum backpropagation needed
• Chebyshev feature encoding — Maps time-series data to quantum states
• Brickwork entanglement — Creates quantum correlations
• Pauli measurements — Extracts information from quantum states
• Classical readout — Single trained layer that maps quantum measurements to predictions

Results on Tetouan City Power Consumption Dataset

Why This Matters

1. Practical quantum advantage — No quantum backpropagation needed, making it deployable with current quantum hardware
2. Edge deployment — 75% memory reduction enables QRC on resource-constrained devices
3. Energy grid applications — Accurate short-term load forecasting is critical as AI data centers surge electricity demand
4. Quantum-classical hybrid — Shows the hybrid approach (quantum reservoir + classical readout) is immediately practical