发表机构
Centre for Quantum Technologies (CQT), National University of Singapore 117543, Singapore
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于GHZ态的相干共识原语，通过相位编码和收集，将量子分类器的协作精度提升$\sqrt{n}$倍，并在离子阱量子计算机上验证了其优于多数投票的估计性能。
AI 中文摘要
受量子计量学的启发，我们提出了一种相干共识原语，以增强独立量子分类器在决策边界处的协作性能。其基本思想包括三个步骤：i) 在 $n$ 个量子比特上制备一个格林伯格-霍恩-泽林格（GHZ）态；ii) 对每个量子比特施加一个相对相位，该相位编码一个分类器的输出；iii) 撤销GHZ制备，将所有相位收集到第一个量子比特上。信号被放大 $n$ 倍，相对于非相干多数投票（即直接组合各个分类器的估计），采样误差减少了 $\sqrt{n}$ 倍。相干共识对于一般的相位估计和分类均有效。我们推导了使用该相干共识原语估计相位所需资源的保证，并与多数投票进行了比较。我们在一个囚禁离子量子计算机上，使用给定的不完美相位门集合，实现了这两种技术。我们观察到相干共识在精度上获得了预期的 $\sqrt{n}$ 增益，在最大 $n$ 处存在与设备漂移一致的额外离散。因此，相干共识可以被理解为量子计算机内部的一种传感形式。
英文摘要
Inspired by quantum metrology, we propose a coherent consensus primitive to enhance the collaborative performance of independent quantum classifiers at the decision boundary. The basic idea consists of three steps: i) prepare a Greenberger-Horne-Zeilinger (GHZ) state on $n$ qubits; ii) apply a relative phase to each qubit, which encodes the output of one classifier; and iii) undo the GHZ preparation to collect all phases onto the first qubit. The signal is amplified by a factor of $n$, which reduces the sampling error by a factor of $\sqrt{n}$ relative to incoherent majority voting, that is, the direct combination of estimates from individual classifiers. Coherent consensus is valid for general estimation and classification of phases. We derive guarantees on the resources needed to estimate phases using this coherent consensus primitive as compared to majority voting. We implement both techniques on a trapped-ion quantum computer using a given ensemble of imperfect phase gates. We observe the expected $\sqrt{n}$ gain in precision of coherent consensus, with excess dispersion at the largest $n$ consistent with device drift. Coherent consensus can thus be understood as a form of sensing inside a quantum computer.
Comments17 pages, 5 figures
