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arXiv 2407.04540quant-phcs.DScs.LG
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通过平坦多项式改进量子哈密顿量学习算法

Improved algorithms for learning quantum Hamiltonians, via flat polynomials

Massachusetts Institute of Technology(麻省理工学院)

Shyam Narayanan

AI总结:

本文提出一种改进的量子哈密顿量学习算法,通过构造次数更低的新平坦多项式逼近指数函数,将样本复杂度和运行时间对逆温度参数的依赖从双重指数降至单重指数,并适用于任意温度。

摘要:

我们给出一种改进算法,在给定量子哈密顿量吉布斯态副本的条件下学习该哈密顿量,且可在任意温度下成功。具体而言,我们改进了 Bakshi、Liu、Moitra 和 Tang [BLMT24] 的工作,将样本复杂度和运行时间对逆温度参数的依赖从双重指数降低到单重指数。我们的主要技术贡献是一种新的对指数函数的平坦多项式逼近,其次数显著低于 [BLMT24] 中所用的平坦多项式逼近。

英文摘要:

We give an improved algorithm for learning a quantum Hamiltonian given copies of its Gibbs state, that can succeed at any temperature. Specifically, we improve over the work of Bakshi, Liu, Moitra, and Tang [BLMT24], by reducing the sample complexity and runtime dependence to singly exponential in the inverse-temperature parameter, as opposed to doubly exponential. Our main technical contribution is a new flat polynomial approximation to the exponential function, with significantly lower degree than the flat polynomial approximation used in [BLMT24].

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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