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DTSTAMP:20260730T152640Z
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UID:dac_DAC 2026_sess306_LBR127@linklings.com
SUMMARY:Late Breaking Results: Hardware-Efficient Quantum Reservoir Comput
 ing via Quantized Readout
DESCRIPTION:Param Pathak (QuantumAI Lab, Fractal Analytics); Mansi Od (Uni
 versity of Greenwich); Nouhaila Innan (New York University Abu Dhabi); and
  Muhammad Shafique (New York University Abu Dhabi (NYUAD))\n\nDue to risin
 g electricity demand, accurate short-term load forecasting is increasingly
  important for grid stability and efficient energy management, particularl
 y in resource-constrained edge settings. We present a hardware-efficient Q
 uantum Reservoir Computing (QRC) framework based on a fixed, untrained qua
 ntum 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 th
 e effect of post-training fixed-point quantization on the classical readou
 t 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 b
 aseline while reducing readout memory by 75\% and 81\%, respectively. Thes
 e results suggest that quantized readout can improve the hardware efficien
 cy and deployment practicality of QRC for memory-constrained energy foreca
 sting.\n\nTrack: Student\n\n
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