Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP). AD employs a predefined procedure to decompose the target state into a series of gates, whereas VQSP iteratively tunes ansatz parameters to approximate target state. VQSP is particularly apt for Noisy-Intermediate Scale Quantum (NISQ) machines due to its shorter circuits. However, achieving noise-robust parameter optimization still remains challenging. We present RobustState, a novel VQSP training methodology that combines high robustness with high training efficiency. The core idea involves utilizing measurement outcomes from real machines to perform back-propagation through classical simulators, thus incorporating real quantum noise into gradient calculations. RobustState serves as a versatile, plug-and-play technique applicable for training parameters from scratch or fine-tuning existing parameters to enhance fidelity on target machines. It is adaptable to various ansatzes at both gate and pulse levels and can even benefit other variational algorithms, such as variational unitary synthesis. Comprehensive evaluation of RobustState on state preparation tasks for 4 distinct quantum algorithms using 10 real quantum machines demonstrates a coherent error reduction of up to 7.1 $\times$ and state fidelity improvement of up to 96\% and 81\% for 4-Q and 5-Q states, respectively. On average, RobustState improves fidelity by 50\% and 72\% for 4-Q and 5-Q states compared to baseline approaches.

翻译：量子态制备作为量子计算中的关键子程序，涉及从初始化量子比特生成目标量子态。任意态制备算法可大致分为算术分解（AD）与变分量子态制备（VQSP）两类。AD通过预定义流程将目标态分解为门序列，而VQSP通过迭代优化拟设参数逼近目标态。VQSP因其电路深度较浅，特别适用于含噪中等规模量子（NISQ）设备。然而，实现抗噪参数优化仍具挑战性。我们提出RobustState——一种兼具高鲁棒性与高训练效率的新型VQSP训练方法。其核心思想是利用真实量子计算机的测量结果，通过经典模拟器进行反向传播，从而将真实量子噪声纳入梯度计算。RobustState作为通用即插即用技术，既可用于从头训练参数，也可微调现有参数以提升目标设备的保真度。该方法适配门级与脉冲级多种拟设结构，甚至可惠及其他变分算法（如变分酉综合）。在10台真实量子计算机上对4种不同量子算法进行态制备任务的全面评估表明，RobustState可分别实现4-Q与5-Q态相干错误率降低高达7.1倍，保真度提升达96%与81%。与基准方法相比，RobustState平均使4-Q与5-Q态保真度分别提升50%与72%。