Effect of Nonunital Noise on Random-Circuit Sampling

Bill Fefferman, Soumik Ghosh, Michael Gullans, Kohdai Kuroiwa, Kunal Sharma
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Abstract

In this work, drawing inspiration from the type of noise present in real hardware, we study the output distribution of random quantum circuits under practical nonunital noise sources with constant noise rates. We show that even in the presence of unital sources such as the depolarizing channel, the distribution, under the combined noise channel, never resembles a maximally entropic distribution at any depth. To show this, we prove that the output distribution of such circuits never anticoncentrates—meaning that it is never too “flat”—regardless of the depth of the circuit. This is in stark contrast to the behavior of noiseless random quantum circuits or those with only unital noise, both of which anticoncentrate at sufficiently large depths. As a consequence, our results shows that the complexity of random-circuit sampling under realistic noise is still an open question, since anticoncentration is a critical property exploited by both state-of-the-art classical hardness and easiness results.

Abstract Image

非数字噪声对随机电路采样的影响
在这项工作中,我们从实际硬件中存在的噪声类型中汲取灵感,研究了在具有恒定噪声率的实用非数字噪声源下随机量子电路的输出分布。我们证明,即使存在去极化信道等非数字源,在组合噪声信道下,输出分布在任何深度都不会类似于最大熵分布。为了证明这一点,我们证明,无论电路的深度如何,这种电路的输出分布永远不会反集中--也就是说,它永远不会过于 "平坦"。这与无噪声随机量子电路或仅有单数噪声的量子电路的行为形成了鲜明对比,这两种电路在足够大的深度时都会出现反同心现象。因此,我们的研究结果表明,现实噪声下随机电路采样的复杂性仍然是一个未决问题,因为反集中是最先进的经典硬度和简易性结果所利用的关键特性。
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