论文标题

可扩展的伪量子量子状态

Scalable Pseudorandom Quantum States

论文作者

Brakerski, Zvika, Shmueli, Omri

论文摘要

有效地对很难与真正的随机量子状态区分开的量子状态是量子信息理论中具有计算和物理用途的基本任务。通常,这被称为伪(量子)状态发生器或简称PRS发电机。 在PRS发电机的现有构造中,安全量表具有州数量的数量,即\ \ $ n $ qubit PRS的(统计)安全参数大约为$ n $。也许在违反直觉上,即使是$ k <n $,也不知道$ n $ qubit的PR暗示$ k $ qubits。因此,迄今为止,PRS的\ emph {可伸缩性}的问题是开放的:是否可以为所有$ n,λ$构造具有安全参数$λ$的$ n $ qubit PRS发电机。确实,我们认为具有微小(甚至是恒定)$ n $和大$λ$的PR非常有用。 我们解决了这项工作中的问题,表明任何量子安全单向函数都意味着可扩展的PR。我们遵循首先显示\ emph {统计上的}安全结构的范式,当给定对随机函数的访问时,然后用量子安全(经典)pseudorandom函数替换随机函数以实现计算安全性。但是,我们的方法显着偏离先前的工作,因为可伸缩的伪随机状态需要随机化量子状态的幅度,而不仅仅是所有先前工作中的相位。我们展示了如何使用高斯采样来实现这一目标。

Efficiently sampling a quantum state that is hard to distinguish from a truly random quantum state is an elementary task in quantum information theory that has both computational and physical uses. This is often referred to as pseudorandom (quantum) state generator, or PRS generator for short. In existing constructions of PRS generators, security scales with the number of qubits in the states, i.e.\ the (statistical) security parameter for an $n$-qubit PRS is roughly $n$. Perhaps counter-intuitively, $n$-qubit PRS are not known to imply $k$-qubit PRS even for $k<n$. Therefore the question of \emph{scalability} for PRS was thus far open: is it possible to construct $n$-qubit PRS generators with security parameter $λ$ for all $n, λ$. Indeed, we believe that PRS with tiny (even constant) $n$ and large $λ$ can be quite useful. We resolve the problem in this work, showing that any quantum-secure one-way function implies scalable PRS. We follow the paradigm of first showing a \emph{statistically} secure construction when given oracle access to a random function, and then replacing the random function with a quantum-secure (classical) pseudorandom function to achieve computational security. However, our methods deviate significantly from prior works since scalable pseudorandom states require randomizing the amplitudes of the quantum state, and not just the phase as in all prior works. We show how to achieve this using Gaussian sampling.

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