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arXiv 2212.04471quant-phcs.LG

通过泡利转移矩阵学习量子过程与哈密顿量

Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix

Institute for Quantum Information and Matter, Caltech(加州理工学院量子信息与物质研究所)

Matthias C. Caro

AI总结:

该研究针对量子过程学习问题,基于泡利转移矩阵与量子存储器确立了n量子比特量子过程学习的指数级量子优势,还结合多项式插值实现了含非局域相互作用的任意哈密顿量的短时动力学学习。

摘要:

依托量子存储器和量子处理的量子增强实验来学习物理系统,其性能能够优于仅使用经典存储器和经典处理的实验学习方式。尽管多种态学习任务已被证实具备量子优势,但量子过程学习只有经过严谨的问题构建才能实现可比的优势,目前相关研究仍不够充分。我们针对未知的n量子比特量子过程𝒩,确立了指数级的量子优势。研究表明,量子存储器能够高效解决以下任务:(a)学习任意𝒩的泡利转移矩阵;(b)当输入为泡利稀疏态时,预测任意𝒩输出上有界泡利稀疏可观测量的期望值;(c)当未知𝒩具有稀疏泡利转移矩阵且输入为任意态时,预测其输出上任意有界可观测量的期望值。借助量子存储器,这些任务仅需使用𝒩的Choi态的n线性份拷贝即可解决,其中任务(b)还具备时间高效性。相比之下,任何不具备量子存储器的学习器都需要指数级于n的查询次数,即便学习器可以在自适应选择的态的子系统上查询𝒩,并执行自适应选择的测量也无法改变这一结论。在证明这一分离性的过程中,我们通过Choi-Jamiolkowski同构,将现有的影子层析(shadow tomography)上下界从量子态推广到量子信道。此外,我们将泡利转移矩阵学习与多项式插值技术相结合,提出了一种从短时动力学中学习任意哈密顿量的方法,这类哈密顿量可以具备非局域的全连接相互作用。我们的研究结果凸显了量子增强实验在学习高度复杂量子动力学方面的强大能力。

英文摘要:

Learning about physical systems from quantum-enhanced experiments, relying on a quantum memory and quantum processing, can outperform learning from experiments in which only classical memory and processing are available. Whereas quantum advantages have been established for a variety of state learning tasks, quantum process learning allows for comparable advantages only with a careful problem formulation and is less understood. We establish an exponential quantum advantage for learning an unknown $n$-qubit quantum process $\mathcal{N}$. We show that a quantum memory allows to efficiently solve the following tasks: (a) learning the Pauli transfer matrix of an arbitrary $\mathcal{N}$, (b) predicting expectation values of bounded Pauli-sparse observables measured on the output of an arbitrary $\mathcal{N}$ upon input of a Pauli-sparse state, and (c) predicting expectation values of arbitrary bounded observables measured on the output of an unknown $\mathcal{N}$ with sparse Pauli transfer matrix upon input of an arbitrary state. With quantum memory, these tasks can be solved using linearly-in-$n$ many copies of the Choi state of $\mathcal{N}$, and even time-efficiently in the case of (b). In contrast, any learner without quantum memory requires exponentially-in-$n$ many queries, even when querying $\mathcal{N}$ on subsystems of adaptively chosen states and performing adaptively chosen measurements. In proving this separation, we extend existing shadow tomography upper and lower bounds from states to channels via the Choi-Jamiolkowski isomorphism. Moreover, we combine Pauli transfer matrix learning with polynomial interpolation techniques to develop a procedure for learning arbitrary Hamiltonians, which may have non-local all-to-all interactions, from short-time dynamics. Our results highlight the power of quantum-enhanced experiments for learning highly complex quantum dynamics.

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