RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training

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.

量子态制备是量子计算中的关键亚例程，涉及从初始化量子位生成目标量子状态。任意状态制备算法可以广泛分为算术分解（AD）和变分量子状态制备（VQSP）。 AD采用预定义的程序将目标状态分解为一系列大门，而VQSP迭代调节ANSATZ参数近似于目标状态。 VQSP特别适合嘈杂的中间尺度量子（NISQ）机器，这是由于其较短的电路。但是，实现噪声射击参数优化仍然具有挑战性。我们提出了Robustate，这是一种新型的VQSP培训方法，将高鲁棒性与高训练效率相结合。核心思想涉及利用真实机器的测量结果通过经典模拟器执行反向传播，从而将实际量子噪声纳入梯度计算中。 RobustState用作一种用于训练参数或微调现有参数的多功能插件技术，以增强目标机器上的保真度。它适用于栅极和脉冲水平的各种Ansatzes，甚至可以使其他变化算法（例如变化统一合成）受益。使用10台实际量子机的4种不同量子算法对国家准备任务进行鲁棒状态的全面评估，这表明，对于4-Q和4-Q，最高7.1 $ \ times $的连贯误差降低了7.1 $ \ times $，最高为96 \％和81 \％分别为5-Q州。与基线方法相比，4-Q和5-Q状态的鲁棒状态平均提高了50 \％和72 \％。