论文标题
基于抽样的嘈杂量子电路的模拟成本的比较研究
Comparative Study of Sampling-Based Simulation Costs of Noisy Quantum Circuits
论文作者
论文摘要
量子操作中的噪声通常会否定量子计算的优势。但是，量子计算机的大多数经典模拟都计算出存储完整状态向量或使用复杂的张量网络收缩的理想概率幅度。在这里，我们研究了基于抽样的经典仿真方法的嘈杂量子电路。具体而言，我们表征了两个主要方案的仿真成本，魔术状态和海森堡传播的稳定剂状态采样，用于量子电路受到随机Pauli噪声的约束，例如去极化和消除噪声。为此，我们介绍了稳定器状态采样的几种技术，以降低这种噪声下的模拟成本。它表明，在低噪声状态下，稳定剂采样会导致较小的采样成本，而海森堡在高噪声方面的传播更好。此外，对于高去极化噪声率$ \ sim 10 \％$，与低级别稳定器分解相比，这些方法提供了更好的缩放。我们认为，这些经典模拟成本的知识对于在近期嘈杂的量子设备以及有效的经典仿真方法中挤压可能的量子优势是有用的。
Noise in quantum operations often negates the advantage of quantum computation. However, most classical simulations of quantum computers calculate the ideal probability amplitudes either storing full state vectors or using sophisticated tensor network contractions. Here, we investigate sampling-based classical simulation methods for noisy quantum circuits. Specifically, we characterize the simulation costs of two major schemes, stabilizer-state sampling of magic states and Heisenberg propagation, for quantum circuits being subject to stochastic Pauli noise, such as depolarizing and dephasing noise. To this end, we introduce several techniques for the stabilizer-state sampling to reduce the simulation costs under such noise. It revealed that in the low noise regime, stabilizer-state sampling results in a smaller sampling cost, while Heisenberg propagation is better in the high noise regime. Furthermore, for a high depolarizing noise rate $\sim 10\%$, these methods provide better scaling compared to that given by the low-rank stabilizer decomposition. We believe that these knowledge of classical simulation costs is useful to squeeze possible quantum advantage on near-term noisy quantum devices as well as efficient classical simulation methods.
