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arXiv 2505.08069quant-ph
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对于Clifford单元的量子过程反演

Process Tomography for Clifford Unitaries

Cornell University(康奈尔大学)

Timothy Skaras, Paul Ginsparg

AI总结:

该研究提出了一种无需查询C†即可高效反演未知Clifford单元的算法,同时具备对扰动鲁棒性和对非Clifford单元的近似能力。

摘要:

我们提出了一种算法,用于对未知的n-量子比特单元ary C(属于Clifford群)进行量子过程反演。我们的算法利用贝尔基测量,以确定性的方式通过4n+3次查询来学习C,这在渐近最优的查询复杂度下是可行的。与之前需要访问C†才能达到最优查询复杂度的算法不同,我们的算法在不查询C†的情况下实现了相同的表现。此外,我们展示了该算法对扰动具有鲁棒性,并且可以高效地学习未知非Clifford单元ary U的最近Clifford,其查询开销与量子比特数量对数成正比。

英文摘要:

We present an algorithm for performing quantum process tomography on an unknown $n$-qubit unitary $C$ from the Clifford group. Our algorithm uses Bell basis measurements to deterministically learn $C$ with $4n + 3$ queries, which is the asymptotically optimal query complexity. In contrast to previous algorithms that required access to $C^\dagger$ to achieve optimal query complexity, our algorithm achieves the same performance without querying $C^\dagger$. Additionally, we show the algorithm is robust to perturbations and can efficiently learn the closest Clifford to an unknown non-Clifford unitary $U$ using query overhead that is logarithmic in the number of 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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