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arXiv 2403.00152quant-phcond-mat.stat-mech
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利用正张量网络实现含噪量子电路的大深度模拟

Enabling large-depth simulation of noisy quantum circuits with positive tensor networks

Ambroise Müller, Thomas Ayral, Corentin Bertrand

AI总结:

针对MPDO模拟含噪量子电路时键维数指数增长的问题,提出通过DMRG式局域基优化解纠缠纯化态以压缩MPDO,实现有界内存下的大深度随机电路仿真。

摘要:

矩阵乘积密度算符(MPDO)是局域纯化密度矩阵的张量网络表示,其中每个物理自由度都与一个环境自由度相关联。MPDO在混合态表示方面具有引人关注的性质:构造上保证正定性,能够高效守恒迹并计算局域可观测量。然而,它们一直难以用于含噪量子电路模拟,因为噪声的施加会增大环境Hilbert空间的维数,导致键维数呈指数增长。MPDO还缺乏唯一的规范形式,原因在于环境Hilbert空间的基选择具有自由度,这会造成键维数的巨大差异。在本工作中,我们提出了一种通过解纠缠纯化态来降低MPDO键维数的系统化方法。我们通过执行类似密度矩阵重整化群(DMRG)的局域2量子比特基优化扫描,来优化环境Hilbert空间的基。有趣的是,我们发现仅以纯化态的解纠缠为目标就会带来环境维数的降低。换言之,紧凑的MPDO表示需要低纠缠的纯化态。我们将压缩方法应用于含噪随机量子电路的仿真。与以往关于MPDO的工作相反,我们的技术使我们能够保持有界的键维数,从而保持有界的内存,同时保持合理的截断保真度。

英文摘要:

Matrix product density operators (MPDOs) are tensor network representations of locally purified density matrices where each physical degree of freedom is associated to an environment degree of freedom. MPDOs have interesting properties for mixed state representations: guaranteed positivity by construction, efficient conservation of the trace and computation of local observables. However, they have been challenging to use for noisy quantum circuit simulation, as the application of noise increases the dimension of the environment Hilbert space, leading to an exponential growth of bond dimensions. MPDOs also lack a unique canonical form, due to the freedom in the choice of basis for the environment Hilbert space, which leads to a vast variation of bond dimensions. In this work, we present a systematic way to reduce the bond dimensions of MPDOs by disentangling the purified state. We optimize the basis for the environment Hilbert space by performing density matrix renormalization group (DMRG)-like sweeps of local 2-qubit basis optimization. Interestingly, we find that targeting only the disentanglement of the purified state leads to a reduction of the environment dimension. In other words, a compact MPDO representation requires a low-entanglement purified state. We apply our compression method to the emulation of noisy random quantum circuits. Our technique allows us to keep bounded bond dimensions, and thus bounded memory, contrary to previous works on MPDOs, while keeping reasonable truncation fidelities.

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