Coherent Floquet quantum reservoirs for molecular property prediction

2026-09-10

AI Summary

研究通过构建基于离散时间晶体的量子储层架构，利用相干Floquet演化处理分子图事件和表面跳跃帧，并使用经典解码器预测分子性质。

Abstract

Quantum reservoir computing (QRC) uses quantum dynamics to represent input histories for prediction through a trained classical readout. Discrete time crystals (DTCs) exhibit robust subharmonic responses under periodic driving, and previous work has used their dynamics to construct DTC-QRC. Here we construct a DTC-based reservoir architecture to predict molecular properties from structural and dynamical observations. Coherent Floquet evolution processes local molecular graph events and surface-hopping frames, while controlled reset regulates the contribution of earlier inputs. Measurements at the end of each input sequence yield a feature vector of fixed dimension. Trained classical decoders use this vector for inhibitor-activity and blood--brain-barrier permeability classification and electronic-gap forecasting, while the reservoir parameters remain fixed during training. With matched input lengths and output widths, DTC-QRC outperforms echo-state networks on long-prefix graph classification and the studied ethene gap forecasting tasks. Dephasing lowers performance in both applications, consistent with a role for coherent propagation. Experiments on the Quafu superconducting quantum cloud platform show that pair observables retain task information under device noise. The architecture provides a common framework for molecular screening and time-resolved property prediction using quantum reservoir computing.

Problem

• Quantum reservoir computing
• Molecular property prediction
• Discrete time crystals

Innovation

• Discrete Time Crystals
• Quantum Reservoir Computing
• Coherent Floquet Evolution
• Molecular Property Prediction
• Quafu Superconducting Quantum Cloud