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arXiv 2501.02005quant-phhep-th
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机器学习量子复杂度

Machine Learns Quantum Complexity

Dongsu Bak, Su-Hyeong Kim, Sangnam Park, Jeong-Pil Song

AI总结:

本研究探究机器学习如何学习随机哈密顿量量子系统的Krylov传播复杂度,发现基于卷积神经网络的算法在能量本征基或Krylov基中能成功学习各时间尺度的复杂度,还可区分热场双重态的温度特征。

摘要:

我们研究了基于深度学习算法的机器如何在由高斯幺正系综生成的N×N随机哈密顿量的量子系统中学习Krylov传播复杂度。以热场双重态为初始条件,我们证明了基于卷积神经网络的算法能成功学习所有时间尺度下的Krylov传播复杂度,包括状态近乎无特征且随机的晚期平台期。性能强烈依赖于基的选择:在能量本征基或Krylov基中表现良好,但在随机哈密顿量的原始基中失败。该算法还能有效区分热场双重态的温度相关特征。此外,我们表明深度学习预测的状态系统时间变量是无关量,这强化了Krylov传播复杂度即使在晚期也能很好捕捉量子态基本特征的结论。

英文摘要:

We study how a machine based on deep learning algorithms learns Krylov spread complexity in quantum systems with N x N random Hamiltonians drawn from the Gaussian unitary ensemble. Using thermofield double states as initial conditions, we demonstrate that a convolutional neural network-based algorithm successfully learns the Krylov spread complexity across all timescales, including the late-time plateaus where states appear nearly featureless and random. Performance strongly depends on the basis choice, performing well with the energy eigenbasis or the Krylov basis but failing in the original basis of the random Hamiltonian. The algorithm also effectively distinguishes temperature-dependent features of thermofield double states. Furthermore, we show that the system time variable of state predicted by deep learning is an irrelevant quantity, reinforcing that the Krylov spread complexity well captures the essential features of the quantum state, even at late times.

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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