论文标题
部分可观测时空混沌系统的无模型预测
Stochastic Approach For Simulating Quantum Noise Using Tensor Networks
论文作者
论文摘要
嘈杂的量子模拟是具有挑战性的，因为必须考虑过程的随机性质。它的主导方法是密度矩阵方法。在本文中，我们评估了该方法不如基本简单的仿真方法的条件。我们的方法使用随机的量子电路集合，将随机的kraus操作员应用于原始量子门，以表示建模量子通道的随机误差。我们表明，即使对于大量Qubits，我们的随机仿真误差相对较低。我们将这种方法作为QTENSOR软件包的一部分实现。虽然通常的密度矩阵仿真平均在$ N> 15 $处有挑战，但我们表明，对于最多$ n \ lyssim 30 $，可以使用$ <1 \％$ $误差进行令人尴尬的并行模拟。通过使用Tensor切片技术，我们可以使用超级计算机模拟高达100 QUIT QAOA电路。
Noisy quantum simulation is challenging since one has to take into account the stochastic nature of the process. The dominating method for it is the density matrix approach. In this paper, we evaluate conditions for which this method is inferior to a substantially simpler way of simulation. Our approach uses stochastic ensembles of quantum circuits, where random Kraus operators are applied to original quantum gates to represent random errors for modeling quantum channels. We show that our stochastic simulation error is relatively low, even for large numbers of qubits. We implemented this approach as a part of the QTensor package. While usual density matrix simulations on average hardware are challenging at $n>15$, we show that for up to $n\lesssim 30$, it is possible to run embarrassingly parallel simulations with $<1\%$ error. By using the tensor slicing technique, we can simulate up to 100 qubit QAOA circuits with high depth using supercomputers.
