Quantum

Hardware-Aware Design and Layout Selection in Quantum Reservoir Computing for Insurance Claims Forecasting

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Briefings

A six-qubit quantum reservoir computing system forecasts next-day motor-insurance claim counts with RMSEs of 5.66-6.12 claims/day, matching classical ridge regression accuracy. Hardware-aware layout selection on the IBM Kingston processor reduced transfer distortion compared to random-native and Qiskit optimizations, with systematic forecast shifts detected during training.

Key points

• Six-qubit gate-based quantum reservoir computing achieved RMSEs of 5.66-6.12 claims/day on 232 held-out test days, matching classical ridge regression accuracy.
• Hardware-aware layout selection on the IBM Kingston processor reduced transfer distortion compared to random-native and Qiskit optimizations, with systematic forecast shifts detected during training.

Context

This study investigates whether small quantum reservoir computing models can forecast real insurance claim data competitively with classical models and whether their predictions survive transfer to noisy hardware. The research addresses two key questions: competitive accuracy on real data and hardware transfer stability.

Limitations

Results are limited to a six-qubit gate-based reservoir and a single processor (IBM Kingston). Accuracy comparisons are based on RMSE on held-out test days, and hardware transfer distortion was measured via short probe inputs rather than full system validation.

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