Autoencoder-based analytic continuation method for strongly correlated quantum systems

Abstract

The single particle Green's function provides valuable information on the momentum and energy-resolved spectral properties for a strongly correlated system. In large-scale numerical calculations using quantum Monte Carlo (QMC), dynamical mean field theory (DMFT), including cluster-DMFT, one usually obtains the Green's function in imaginary-time $G(\tau )$. The process of inverting a Laplace transform to obtain the spectral function $A(\omega )$ in real-frequency is an ill-posed problem and forms the core of the analytic continuation problem. In this Letter, we propose to use a completely unsupervised autoencoder-type neural network to solve the analytic continuation problem. We introduce an encoder-decoder approach that, together with only minor physical assumptions, can extract a high-quality frequency response from the imaginary time domain. With a deeply tunable architecture, this method can, in principle, locate sharp features of spectral functions that might normally be lost using already well-established methods, such as maximum entropy (MaxEnt) methods. We demonstrate the strength of the autoencoder approach by applying it to QMC results of $G(\tau )$ for a single-band Hubbard model. The proposed method is general and can also be applied to other ill-posed inverse problems.

7 pages, 5 figures, supplement

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• Thermalization and its mechanism for generic isolated quantum systems
• Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations
• Stochastic pole expansion method
• TRIQS/Nevanlinna: Implementation of the Nevanlinna Analytic Continuation method for noise-free data
• Denoising of Imaginary Time Response Functions with Hankel projections

Cited by in corpus (1)

• High-temperature series expansion of the dynamic Matsubara spin correlator