这篇论文搞了个 SUSA 方法，用储备池做波动率预测，在美股 ETF 上测了效果比 GARCH 好，还能跟 HARQ 组合更牛。
论文提出 Susceptible Architectures (SUSA) 储备池设计原则，包括复数开链和周期两种实现，并通过状态条件专家区分平静、爆发、恢复和持续压力状态。在 Qiskit 中实现了开放系统 q-qubit 版本，使用 AR-Ridge 锚点和 QLIKE 残差校正。在 16 个美国股票和 ETF 上，用 12 个观测输入窗口预测未来 5 个观测值。SUSA 在 IWM 和 XLP 上取得比 GARCH 更优的 QLIKE，与 HARQ 集成后平均 QLIKE 提升 0.0116，在 75% 测试场景中获胜。
Susceptible Reservoir Architectures for Regime-Conditional Volatility Forecasting
Volatility forecasting is dominated by persistence and measurement noise, leaving limited residual structure for nonlinear models to exploit. We introduce Susceptible Architectures (SUSA), a reservoir-design principle for volatility forecasting, and its two concrete implementations, based on complex-valued open-chain and periodic reservoirs and regime-conditioned experts to interpret reservoir features across calm, onset, recovery, and persistent-stress states. We also implement open-system $q$-qubit counterparts in Qiskit while retaining a common AR-Ridge anchor and a bounded residual correction trained under QLIKE. We evaluate models on 16 U.S. equity and exchange-traded-fund series using three disjoint chronological training, validation, and test folds, a 12-observation input window, and a five-observation forecast horizon. The proposed models perform competitively with GARCH, achieving statistically significant QLIKE improvements for specific assets (IWM, XLP). Also models' forecasts complement HARQ-style predictions: a stacked ensemble improves mean QLIKE by 0.0116 over its strongest constituent and wins in 75% of test scenarios.