论文标题
噪声量子电路的低等级密度矩阵演化
Low Rank Density Matrix Evolution for Noisy Quantum Circuits
论文作者
论文摘要
在这项工作中，我们提出了一种有效的秩压缩方法，用于在嘈杂的量子电路中对kraus脱干通道进行经典模拟。通过在每个仿真步骤中基于其领先的特征性矩阵对密度矩阵的迭代压缩来实现近似值，而无需存储，操纵或对角度化完整矩阵。我们在内部模拟器中实现了该算法，并表明，在现有的全等级模拟器的现有实现中，低等级算法将模拟加速了两个以上的数量级，并且目标噪声和最终可观察到的误差可忽略不计。最后，我们通过使用该算法来加快Grover的搜索算法和量子化学求解器的噪声模拟来证明低级方法的实用性，以应用于感兴趣的代表性问题。
In this work, we present an efficient rank-compression approach for the classical simulation of Kraus decoherence channels in noisy quantum circuits. The approximation is achieved through iterative compression of the density matrix based on its leading eigenbasis during each simulation step without the need to store, manipulate, or diagonalize the full matrix. We implement this algorithm in an in-house simulator, and show that the low rank algorithm speeds up simulations by more than two orders of magnitude over an existing implementation of full rank simulator, and with negligible error in the target noise and final observables. Finally, we demonstrate the utility of the low rank method as applied to representative problems of interest by using the algorithm to speed-up noisy simulations of Grover's search algorithm and quantum chemistry solvers.
