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arXiv 2208.00567quant-ph
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量子计算机上精确且高效的Lanczos方法

Exact and efficient Lanczos method on a quantum computer

William Kirby, Mario Motta, Antonio Mezzacapo

AI总结:

提出基于块编码的量子Lanczos方法,在多项式时间内精确构造Krylov空间以估计哈密顿量极值特征值,克服经典方法的指数成本且无需时间演化模拟。

摘要:

我们提出一种在量子计算机上使用块编码来精确构造Krylov空间的算法,该空间可作为Lanczos方法的基础,用于估计哈密顿量的极值特征值。虽然经典Lanczos方法在表示量子系统的Krylov状态时,其成本随系统大小呈指数增长,但我们高效的量子算法能在多项式时间和内存内实现这一点。该构造是精确的,因为所得的Krylov空间与Lanczos方法的Krylov空间完全相同,因此相对于精确方法的唯一近似仅源于有限采样噪声。这是可能的,因为与以往的量子Krylov方法不同,我们的算法不需要模拟实或虚时间演化。我们提供了在噪声存在的情况下,所得基态能量估计的显式误差界限。为使我们的方法高效成功,对输入问题的唯一要求是,初始状态与真实基态的重叠度对于n个量子比特必须为Ω(1/poly(n))。

英文摘要:

We present an algorithm that uses block encoding on a quantum computer to exactly construct a Krylov space, which can be used as the basis for the Lanczos method to estimate extremal eigenvalues of Hamiltonians. While the classical Lanczos method has exponential cost in the system size to represent the Krylov states for quantum systems, our efficient quantum algorithm achieves this in polynomial time and memory. The construction presented is exact in the sense that the resulting Krylov space is identical to that of the Lanczos method, so the only approximation with respect to the exact method is due to finite sample noise. This is possible because, unlike previous quantum Krylov methods, our algorithm does not require simulating real or imaginary time evolution. We provide an explicit error bound for the resulting ground state energy estimate in the presence of noise. For our method to be successful efficiently, the only requirement on the input problem is that the overlap of the initial state with the true ground state must be $Ω(1/\text{poly}(n))$ for $n$ qubits.

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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