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arXiv 2404.16751quant-phcs.CRmath-phmath.MP
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由置换构造高效酉设计与伪随机酉算子

Efficient unitary designs and pseudorandom unitaries from permutations

Chi-Fang Chen, Adam Bouland, Fernando G. S. L. Brandão, Jordan Docter, Patrick Hayden, Michelle Xu

AI总结:

本文提出将随机置换提升为随机酉算子的高效量子算法,据此构造低门数酉 $k$-设计与并行安全 PRU,并通过大 $N$ 展开和多项式方法证明查询下界。

摘要:

在本工作中,我们利用 $\tilde{O}(k\cdot poly(n))$ 个量子门给出了一种酉 $k$-设计的高效构造,同时给出了一种并行安全伪随机酉算子(PRU)的高效构造。这两项结果均通过给出一种高效量子算法获得:对于 $N=2^n$,该算法将 $S(N)$ 上的随机置换提升为 $U(N)$ 上的随机酉算子。具体而言,我们证明,将带有随机相位的 $S(N)$ 置换的指数化和相乘,可近似匹配 Haar 测度的前 $2^{Ω(n)}$ 阶矩。通过以 $\tilde{O}(k)$-wise independent permutations($\tilde{O}(k)$ 元独立置换)或量子安全伪随机置换(PRP)替代随机置换,我们得到上述结果。证明的核心是随机矩阵理论中的大维度(大 $N$)展开与多项式方法之间的概念性联系,使我们能够通过从简单得多的大 $N$ 极限进行插值,证明有限 $N$ 下的查询下界。关键技术步骤是为分区代数的不可约表示给出一组具有低次大 $N$ 展开的标准正交基。这使我们能够证明区分概率是维度 $N$ 的低次有理多项式。

英文摘要:

In this work we give an efficient construction of unitary $k$-designs using $\tilde{O}(k\cdot poly(n))$ quantum gates, as well as an efficient construction of a parallel-secure pseudorandom unitary (PRU). Both results are obtained by giving an efficient quantum algorithm that lifts random permutations over $S(N)$ to random unitaries over $U(N)$ for $N=2^n$. In particular, we show that products of exponentiated sums of $S(N)$ permutations with random phases approximately match the first $2^{Ω(n)}$ moments of the Haar measure. By substituting either $\tilde{O}(k)$-wise independent permutations, or quantum-secure pseudorandom permutations (PRPs) in place of the random permutations, we obtain the above results. The heart of our proof is a conceptual connection between the large dimension (large-$N$) expansion in random matrix theory and the polynomial method, which allows us to prove query lower bounds at finite-$N$ by interpolating from the much simpler large-$N$ limit. The key technical step is to exhibit an orthonormal basis for irreducible representations of the partition algebra that has a low-degree large-$N$ expansion. This allows us to show that the distinguishing probability is a low-degree rational polynomial of the dimension $N$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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