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.

翻译：量子储层计算为时序学习提供了一种硬件友好的方法，但现有研究大多针对单变量信号且忽略了近期硬件约束。本文提出一种用于多元时间序列的门电路量子储层计算方法，该方法通过配对注入量子位与记忆量子位，并采用基于Trotter化的最近邻横向场伊辛演化，该演化针对当前设备的连接性与深度进行了优化。在Lorenz-63和ENSO数据集上，该方法分别取得了0.0087和0.0036的均方误差，在Lorenz数据集上性能与经典储层计算相当，在两个数据集上均优于经过学习的循环神经网络，而NVAR和聚类回声状态网络在某些设定下仍表现更强。在IBM Heron R2硬件上，MTS-QRC在实际深度下保持了预测精度，有趣的是，在ENSO数据集上其表现甚至超越了无噪声模拟器；奇异值分析表明，设备噪声可将方差集中于特征方向，在此机制下充当了线性读出层的隐式正则化器。这些发现支持了基于门电路的量子储层计算在NISQ硬件上实现多元时间序列预测的实用性，并激励了对硬件噪声何时及如何有益于量子储层计算读出层的系统性研究。