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arXiv 1909.05500quant-phcs.NAmath.NA
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基于时间最优绝热量子计算和量子近似优化算法的量子线性系统求解器

Quantum linear system solver based on time-optimal adiabatic quantum computing and quantum approximate optimization algorithm

Dong An, Lin Lin

摘要:

我们证明了,与优化调整的调度功能,绝热量子计算(AQC)可以很容易地解决量子线性系统问题(QLSP)与$\mathcal{O}(κ~\text{poly}(\log(κ/ε)))$运行时,其中$κ$是条件数,和$ε$是目标精度。这对于$κ$和$ε$两者来说都是接近最优的。我们的方法适用于一般的非厄米矩阵,并且当限制为厄米矩阵,进而限制为厄米正定矩阵时,可以减少量子比特的数量和代价.时间最优AQC的成功意味着具有最优控制协议的量子近似优化算法(QAOA)也可以在运行时间方面实现相同的复杂性。数值结果表明,QAOA可以产生最低的运行时间相比,时间最优的AQC,香草AQC,和最近提出的随机化方法。

英文摘要:

We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can readily solve a quantum linear system problem (QLSP) with $\mathcal{O}(κ~\text{poly}(\log(κ/ε)))$ runtime, where $κ$ is the condition number, and $ε$ is the target accuracy. This is near optimal with respect to both $κ$ and $ε$. Our method is applicable to general non-Hermitian matrices, and the cost as well as the number of qubits can be reduced when restricted to Hermitian matrices, and further to Hermitian positive definite matrices. The success of the time-optimal AQC implies that the quantum approximate optimization algorithm (QAOA) with an optimal control protocol can also achieve the same complexity in terms of the runtime. Numerical results indicate that QAOA can yield the lowest runtime compared to the time-optimal AQC, vanilla AQC, and the recently proposed randomization method.

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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