arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.05209quant-phcs.DS
收藏

通过 Pauli 分解学习 k 量子比特量子算子

Learning k-qubit Quantum Operators via Pauli Decomposition

Mohsen Heidari, Wojciech Szpankowski

AI总结:

本文在 QPAC 模型下研究 k 量子比特算子学习,提出基于 Pauli 分解与 QSS 的量子算法,证明其样本复杂度可与经典 junta 学习相当。

摘要:

受当前量子系统有限量子比特容量的推动,我们研究 k 量子比特量子算子的量子样本复杂度,即仅作用于 d 个量子比特中 k 个量子比特的操作。该问题依据量子概率近似正确(QPAC)模型进行研究,并遵守不可克隆、态坍缩和测量不相容等量子力学定律。由于量子样本的脆弱性以及量子操作的丰富性,人们预期其量子样本复杂度会显著更高。本文证明了相反的结论。我们表明,k 量子比特量子操作的量子样本复杂度与其经典对应物(junta)的经典样本复杂度相当,至少在 k/d≪1 时如此。这一点令人惊讶,尤其是因为样本复制被禁止,而且使用标准方法时,测量不相容会导致样本复杂度呈指数级增长。我们的方法基于量子算子的 Pauli 分解以及一种我们命名为 Quantum Shadow Sampling(QSS,量子阴影采样)的技术,用以将样本复杂度指数级降低。这些结果通过以下工作得到证明:(i)建立学习损失与 Pauli 分解之间的联系;(ii)构建用于估计 Pauli 系数的可扩展 QSS 电路;以及(iii)提出一种学习 k 量子比特算子的量子算法,其样本复杂度为 O((k4^k)/(ε^2) log d)。

英文摘要:

Motivated by the limited qubit capacity of current quantum systems, we study the quantum sample complexity of $k$-qubit quantum operators, i.e., operations applicable on only $k$ out of $d$ qubits. The problem is studied according to the quantum probably approximately correct (QPAC) model abiding by quantum mechanical laws such as no-cloning, state collapse, and measurement incompatibility. With the delicacy of quantum samples and the richness of quantum operations, one expects a significantly larger quantum sample complexity. This paper proves the contrary. We show that the quantum sample complexity of $k$-qubit quantum operations is comparable to the classical sample complexity of their counterparts (juntas), at least when $\frac{k}{d}\ll 1$. This is surprising, especially since sample duplication is prohibited, and measurement incompatibility would lead to an exponentially larger sample complexity with standard methods. Our approach is based on the Pauli decomposition of quantum operators and a technique that we name Quantum Shadow Sampling (QSS) to reduce the sample complexity exponentially. The results are proved by developing (i) a connection between the learning loss and the Pauli decomposition; (ii) a scalable QSS circuit for estimating the Pauli coefficients; and (iii) a quantum algorithm for learning $k$-qubit operators with sample complexity $O(\frac{k4^k}{ε^2}\log d)$.

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。

↑