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arXiv:2605.24252

Hybrid Quantum-Classical Machine Learning Algorithms for Multi-Output Time-Series Forecasting at Utility Scale

Mackenson Polché, Varun Puram, Aditi Lal, Weronika Golletz, Joan Étude Arrow, Vardaan Sahgal, Kumar Ghosh, Giorgio Cortiana, Corey O'Meara

quant-phSubmitted May 22, 2026• Announced May 26, 2026• 0 citations
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Abstract

Multi-output time-series forecasting in energy systems is challenging because of nonlinear dynamics, multi-scale seasonality, and strong dependencies across correlated series. In this work, we investigate two hybrid quantum-classical frameworks for multi-stream time-series forecasting on a real Smart Meter dataset comprising 103 household electricity consumption time-series, with experiments executed on the $ibm\_marrakesh$ superconducting quantum processor. The first model, Kernelized Quantum Reservoir Computing with Repeated Measurement (KQRC-RM), combines coupled quantum reservoirs, ancilla-assisted repeated measurement, and kernelized readouts to model temporal dynamics and cross-stream correlations jointly. For a 3-stream time-series input and output, the KQRC-RM model using 114 qubits achieves an MAE of 0.0811 on MPS simulator (36.92\% improvement over its classical analog) whereas performance degrades to an MAE of 0.1524 on hardware. The second, a Projected Quantum Kernel Gaussian Process (QGP), replaces fidelity-based kernels with projected kernels constructed from local reduced-state statistics. Using a topology-aware 100-qubit QGP model to predict 100 multi-output time-series values, we observe 49\% of time-series outputs achieve high-accuracy predictions (MAE $<0.15$), with an average MAE of $0.082$ for this low-error group. The medium-error regime (MAE $0.15$-$0.35$) has an average MAE of $0.229$, while the high-error regime (MAE $>0.35$) has an average MAE of $0.664$. Overall, this reduces the average MAE relative to the classical GP baseline by 62.01\% on MPS simulator and 40.37\% on hardware. Together, these results demonstrate the feasibility of hybrid quantum machine learning for multi-input, multi-output time-series forecasting at the 100+ qubit scale on NISQ devices.

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15 pages, 13 figures

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Multi-output time-series forecasting in energy systems is challenging because of nonlinear dynamics, multi-scale seasonality, and strong dependencies across correlated series. In this work, we investigate two hybrid quantum-classical frameworks for multi-stream time-series forecasting on a real Smart Meter dataset comprising 103 household electricity consumption time-series, with experiments executed on the ibm_marrakeshibm\_marrakeshibm_marrakesh superconducting quantum processor. The first model, Kernelized Quantum Reservoir Computing with Repeated Measurement (KQRC-RM), combines coupled quantum reservoirs, ancilla-assisted repeated measurement, and kernelized readouts to model temporal dynamics and cross-stream correlations jointly. For a 3-stream time-series input and output, the KQRC-RM model using 114 qubits achieves an MAE of 0.0811 on MPS simulator (36.92% improvement over its classical analog) whereas performance degrades to an MAE of 0.1524 on hardware. The second, a Projected Quantum Kernel Gaussian Process (QGP), replaces fidelity-based kernels with projected kernels constructed from local reduced-state statistics. Using a topology-aware 100-qubit QGP model to predict 100 multi-output time-series values, we observe 49% of time-series outputs achieve high-accuracy predictions (MAE <0.15<0.15<0.15), with an average MAE of 0.0820.0820.082 for this low-error group. The medium-error regime (MAE 0.150.150.15-0.350.350.35) has an average MAE of 0.2290.2290.229, while the high-error regime (MAE >0.35>0.35>0.35) has an average MAE of 0.6640.6640.664. Overall, this reduces the average MAE relative to the classical GP baseline by 62.01% on MPS simulator and 40.37% on hardware. Together, these results demonstrate the feasibility of hybrid quantum machine learning for multi-input, multi-output time-series forecasting at the 100+ qubit scale on NISQ devices.
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