迈向预测量子算法性能：大规模时间相关噪声建模
Towards Predictive Quantum Algorithmic Performance: Modeling Time-Correlated Noise at Scale
AI总结：
本文提出通过张量网络与量子自回归移动平均模型结合，量化时间相关噪声对量子算法性能的影响，并提出预测基准测试协议以提升大规模模拟的准确性。
摘要：
通过结合张量网络技术与量子自回归移动平均模型，我们量化了时间相关噪声对量子算法的影响，并预测其在大规模下的性能。作为典型测试案例，我们检查了量子傅里叶变换。基于我们第一个技术结果，即随机张量网络计算如何捕捉频率相关性，我们的第二个结果揭示了退相干指数（从扩散到超扩散）由噪声的频谱特征决定。这一数值结果严格量化了普遍认为时间相关尺度是噪声对多量子比特电路破坏性影响的关键预测特征的信念。为了突出预测算法性能的前景，我们的第三个结果量化了退相干指数——由中等规模（40-80个量子比特）训练数据确定的拟合——如何用于预测更计算昂贵的模拟在更大规模（100-128个量子比特）下的表现。除了突出我们方法的可扩展性，这一工作流程还引导我们最后的结果，即提出预测基准测试协议，将模拟连接到实验。我们的工作为在受现实设备特性影响的硬件相关噪声条件下进行大规模算法模拟和性能预测铺平了道路。
英文摘要：
Combining tensor network techniques with quantum autoregressive moving average models, we quantify the effects of time-correlated noise on quantum algorithms and predict their performance at scale. As a paradigmatic test case, we examine the quantum Fourier transformation. Building on our first technical result, which shows how stochastic tensor network calculations capture frequency correlations, our second result is the revelation that infidelity exponents (scaling from diffuse, to superdiffuse) are determined by the spectral features of the noise. This numerical result rigorously quantifies the common belief that the temporal correlation scale is a key predictive feature of noise's deleterious impact on multi-qubit circuits. To highlight prospects for predicting algorithmic performance, our third result quantifies how infidelity scaling exponents -- which are fits determined by training data at moderate scales (40-80 qubits) -- can be used to predict more computationally expensive simulation at larger scales (100-128 qubits). Aside from highlighting the scalability of our methods, this workflow feeds into our last result, which is the proposal of predictive benchmarking protocols connecting simulations to experiments. Our work paves the way for large-scale algorithmic simulations and performance prediction under hardware-relevant noise conditions informed by realistic device characteristics.
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