Late Breaking Results: Hardware-Efficient Quantum Reservoir Computing via Quantized Readout
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910109339222016 |
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| author | Pathak, Param Od, Mansi Innan, Nouhaila Shafique, Muhammad |
| author_facet | Pathak, Param Od, Mansi Innan, Nouhaila Shafique, Muhammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06075 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Late Breaking Results: Hardware-Efficient Quantum Reservoir Computing via Quantized Readout Pathak, Param Od, Mansi Innan, Nouhaila Shafique, Muhammad Emerging Technologies Quantum Physics 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. |
| title | Late Breaking Results: Hardware-Efficient Quantum Reservoir Computing via Quantized Readout |
| topic | Emerging Technologies Quantum Physics |
| url | https://arxiv.org/abs/2604.06075 |