Computing dynamical distributions in quantum many-body systems represents one of the paradigmatic open problems in theoretical condensed matter physics. Despite the existence of different techniques both in real-time and frequency space, computational limitations often dramatically constrain the physical regimes in which quantum many-body dynamics can be efficiently solved. Here we show that the combination of machine-learning methods and complementary many-body tensor network techniques substantially decreases the computational cost of quantum many-body dynamics. We demonstrate that combining kernel polynomial techniques and real-time evolution, together with deep neural networks, allows to compute dynamical quantities faithfully. Focusing on many-body dynamical distributions, we show that this hybrid neural-network many-body algorithm, trained with single-particle data only, can efficiently extrapolate dynamics for many-body systems without prior knowledge. Importantly, this algorithm is shown to be substantially resilient to numerical noise, a feature of major importance when using this algorithm together with noisy many-body methods. Ultimately, our results provide a starting point towards neural-network powered algorithms to support a variety of quantum many-body dynamical methods, that could potentially solve computationally expensive many-body systems in a more efficient manner.

中文翻译：

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神经网络增强混合量子多体动力学分布

计算量子多体系统中的动力学分布代表了理论凝聚态物理学中的典型开放问题之一。尽管在实时和频率空间中存在不同的技术，但计算限制通常极大地限制了可以有效解决量子多体动力学的物理机制。在这里，我们表明机器学习方法和互补的多体张量网络技术的结合大大降低了量子多体动力学的计算成本。我们证明，将核多项式技术和实时演化与深度神经网络相结合，可以忠实地计算动态量。专注于多体动力学分布，我们展示了这种混合神经网络多体算法，仅使用单粒子数据进行训练，可以在没有先验知识的情况下有效地推断多体系统的动力学。重要的是，该算法被证明对数值噪声具有很强的弹性，当将此算法与嘈杂的多体方法结合使用时，这是一个非常重要的特征。最终，我们的结果为神经网络驱动的算法提供了一个起点，以支持各种量子多体动力学方法，这可能以更有效的方式解决计算成本高昂的多体系统。