论文标题
估计量子电路的随机性最多50 QUAT
Estimating the randomness of quantum circuit ensembles up to 50 qubits
论文作者
论文摘要
随机量子电路已在量子至上演示的背景下使用，化学和机器学习的变分量子算法以及黑洞信息。随机电路近似任何随机单位的能力会对它们的复杂性，表现性和训练性产生影响。为了研究随机电路的这种属性，我们开发了用于估计框架电势的数值协议，给定集合和确切的随机性之间的距离。我们的基于张量的基于网络的算法具有针对浅回路的多项式复杂性，并且使用CPU和GPU并行性表现出色。我们研究1。局部和平行的随机电路，以验证棕色 - 类之猜想所述的复杂性的线性生长，以及； 2.硬件有效的Ansätze阐明了其在变化算法的背景下的表达性和贫瘠的高原问题。我们的工作表明，大规模张量网络模拟可以为量子信息科学中的开放问题提供重要的提示。
Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown-Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.
