论文标题
绝热状态准备的快速数字方法
Fast digital methods for adiabatic state preparation
论文作者
论文摘要
我们在基于门的量子计算机上提出了一种用于绝热状态制备的量子算法,在反向误差中具有复杂性的聚类。我们的算法通过数字模拟了两个自我接合操作员$ H_0 $和$ H_1 $之间的绝热演变,并通过利用准绝热延续的理论概念作为算法工具,从而呈指数级抑制了糖尿病错误。 Given an upper bound $α$ on $\|H_0\|$ and $\|H_1\|$ along with the promise that the $k$th eigenstate $|ψ_k(s)\rangle$ of $H(s) \equiv (1-s)H_0 + sH_1$ is separated from the rest of the spectrum by a gap of at least $γ> 0$ for all $s \in [0,1] $,此算法实现了操作员$ \ wideTilde {u} $,以至于$ \ ||ψ_k(1)\ rangle- \ widetilde {u} |ψ_k(s) \leqε$使用$ o(α^2/γ^2)\ text {polylog}(α/γε)$查询$ h_0 $和$ h_1 $的块编码。此外,我们开发了一种仅适用于接地状态的算法,并且需要对准备$ |ψ_0(0)\ rangle $的Oracle进行多个查询,但在所有参数中都具有更好的缩放。我们还表明,在某些合理条件下,可以进一步降低两种算法的成本,例如$ \ | h_1 -H_0 \ | $与$α$相比,或当有关$ h(s)$的差距的更多信息时。对于某些问题,甚至可以将缩放率提高到$ \ | h_1 -H_0 \ |/γ$中的线性,从而将缩放率提高到polyrogarithmic因子。
We present a quantum algorithm for adiabatic state preparation on a gate-based quantum computer, with complexity polylogarithmic in the inverse error. Our algorithm digitally simulates the adiabatic evolution between two self-adjoint operators $H_0$ and $H_1$, exponentially suppressing the diabatic error by harnessing the theoretical concept of quasi-adiabatic continuation as an algorithmic tool. Given an upper bound $α$ on $\|H_0\|$ and $\|H_1\|$ along with the promise that the $k$th eigenstate $|ψ_k(s)\rangle$ of $H(s) \equiv (1-s)H_0 + sH_1$ is separated from the rest of the spectrum by a gap of at least $γ> 0$ for all $s \in [0,1]$, this algorithm implements an operator $\widetilde{U}$ such that $\||ψ_k(1)\rangle - \widetilde{U}|ψ_k(s)\rangle\| \leq ε$ using $O(α^2/γ^2)\text{polylog}(α/γε)$ queries to block-encodings of $H_0$ and $H_1$. In addition, we develop an algorithm that is applicable only to ground states and requires multiple queries to an oracle that prepares $|ψ_0(0)\rangle$, but has slightly better scaling in all parameters. We also show that the costs of both algorithms can be further reduced under certain reasonable conditions, such as when $\|H_1 - H_0\|$ is small compared to $α$, or when more information about the gap of $H(s)$ is available. For certain problems, the scaling can even be improved to linear in $\|H_1 - H_0\|/γ$ up to polylogarithmic factors.