Neural network enhanced hybrid quantum many-body dynamical distributions

TL;DR

This paper introduces a hybrid neural network and tensor network approach to efficiently compute quantum many-body dynamical distributions, overcoming computational limitations and demonstrating robustness to noise.

Contribution

It presents a novel hybrid algorithm combining machine learning with tensor network techniques to improve the efficiency and noise resilience of quantum many-body dynamics calculations.

Findings

Efficiently extrapolates many-body dynamics from single-particle data.

Reduces computational cost compared to traditional methods.

Shows robustness to numerical noise in simulations.

Abstract

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.