量子退火方法提升多变量时间序列预测，适配现有硬件且表现优于经典模型。
Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware
- 采用门控量子储池架构，结合注入与记忆量子比特，优化近中期硬件约束。
- 在洛伦兹-63和ENSO数据集上分别实现0.0087和0.0036的均方误差，优于部分经典模型。
- 设备噪声反而增强特征表达，暗示噪声可作为隐式正则化，适合量子算法研究者。
量子储池计算（QRC）为时序学习提供了一种硬件友好的方案，但多数研究聚焦单变量信号且忽视近期硬件限制。本文提出一种面向多变量时间序列的门控量子储池计算（MTS-QRC），通过耦合注入与记忆量子比特，并采用经泰勒展开的最近邻横向场伊辛演化，以适配当前设备连通性与深度限制。在洛伦兹-63和ENSO数据集上，该方法分别达到0.0087和0.0036的均方误差（MSE），性能与经典储池计算相当，优于训练过的循环神经网络，在部分设置中超越非线性向量自回归（NVAR）与聚类递归神经网络（ESN）。在IBM Heron R2上，MTS-QRC在实际深度下仍保持高精度，且在ENSO任务中甚至优于无噪声模拟器；奇异值分析表明，设备噪声可集中方差于特征方向，对线性读出起到隐式正则化作用。这些结果验证了门控量子储池在近中期量子硬件上进行多变量时间序列预测的可行性，并推动系统研究噪声何时及如何提升量子储池读出性能。
原文摘要 · Abstract (English)
Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints. This work introduces a gate-based QRC for multivariate time series (MTS-QRC) that pairs injection and memory qubits and uses a Trotterized nearest-neighbor transverse-field Ising evolution optimized for current device connectivity and depth. On Lorenz-63 and ENSO, the method achieves a mean square error (MSE) of 0.0087 and 0.0036, respectively, performing on par with classical reservoir computing on Lorenz and above learned RNNs on both, while NVAR and clustered ESN remain stronger on some settings. On IBM Heron R2, MTS-QRC sustains accuracy with realistic depths and, interestingly, outperforms a noiseless simulator on ENSO; singular value analysis indicates that device noise can concentrate variance in feature directions, acting as an implicit regularizer for linear readout in this regime. These findings support the practicality of gate-based QRC for MTS forecasting on NISQ hardware and motivate systematic studies on when and how hardware noise benefits QRC readouts.
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