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arXiv 2402.05552quant-phcs.LGmath.OC
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在多项式时间内利用切比雪夫与比特复杂度学习任意温度下的量子哈密顿量

Learning quantum Hamiltonians at any temperature in polynomial time with Chebyshev and bit complexity

Ales Wodecki, Jakub Marecek

AI总结:

本文提出基于切比雪夫展开的平坦多项式近似,将学习量子哈密顿量转化为多项式优化问题,并证明在温和假设下可在多项式时间内学习有界度k-局部哈密顿量。

摘要:

我们考虑在已知逆温度下,给定吉布斯态的副本,学习局部量子哈密顿量的问题,遵循Haah等人[2108.04842]和Bakshi等人[arXiv:2310.02243]的工作。我们的主要技术贡献是一种基于切比雪夫展开的新的平坦多项式近似指数函数的方法,这使得将学习量子哈密顿量问题表述为多项式优化问题成为可能。这反过来又可以受益于矩/SOS松弛的使用,其多项式比特复杂度需要仔细分析[O'Donnell, ITCS 2017]。最后,我们证明在温和假设下,学习一个对偶交互图有界度的k-局部哈密顿量可以在多项式时间内完成。

英文摘要:

We consider the problem of learning local quantum Hamiltonians given copies of their Gibbs state at a known inverse temperature, following Haah et al. [2108.04842] and Bakshi et al. [arXiv:2310.02243]. Our main technical contribution is a new flat polynomial approximation of the exponential function based on the Chebyshev expansion, which enables the formulation of learning quantum Hamiltonians as a polynomial optimization problem. This, in turn, can benefit from the use of moment/SOS relaxations, whose polynomial bit complexity requires careful analysis [O'Donnell, ITCS 2017]. Finally, we show that learning a $k$-local Hamiltonian, whose dual interaction graph is of bounded degree, runs in polynomial time under mild assumptions.

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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