Analytic continuation via “domain-knowledge free” machine learning

We present a machine-learning (ML) approach to a long-standing issue in quantum many-body physics, namely, analytic continuation. This notorious ill-conditioned problem of obtaining spectral function from Green’s function has been a focus of new method developments for past decades. Here we demonstrate the usefulness of modern ML techniques including convolutional neural networks and the variants of stochastic gradient descent optimizer. ML 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 ML-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 [1]. Furthermore, its computation speed is 10⁴–10⁵ times faster than maximum entropy method.

[1] H. Yoon, J.-H. Sim, and M. J. Han, ArXiv:1806.03841.