Analytic continuation via domain-knowledge free machine learning

TL;DR

This paper introduces a machine learning approach for analytic continuation in quantum physics that does not rely on domain knowledge, achieving higher accuracy and robustness with significantly faster computation than traditional methods.

Contribution

The authors develop a domain-knowledge free neural network-based method for analytic continuation, outperforming conventional techniques in accuracy, noise robustness, and speed.

Findings

Achieves more accurate spectral functions than traditional methods.

Demonstrates robustness against noise in the data.

Speeds up computation by 10^4 to 10^5 times.

Abstract

We present a machine-learning approach to a long-standing issue in quantum many-body physics, namely, analytic continuation. This notorious ill-conditioned problem of obtaining spectral function from imaginary time Green's function has been a focus of new method developments for past decades. Here we demonstrate the usefulness of modern machine-learning techniques including convolutional neural networks and the variants of stochastic gradient descent optimiser. Machine-learning continuation kernel is successfully realized without any 'domain-knowledge', which means that any physical 'prior' is not utilized in the kernel construction and the neural networks 'learn' the knowledge solely from 'training'. The outstanding performance is achieved for both insulating and metallic band structure. Our machine-learning-based approach not only provides the more accurate spectrum than the conventional methods in terms of peak positions and heights, but is also more robust against the noise which is the required key feature for any continuation technique to be successful. Furthermore, its computation speed is 10$^4$-10$^5$ times faster than maximum entropy method.