Eigenvector continuation for emulating and extrapolating two-body resonances

Abstract

The study of open quantum systems (OQSs), i.e., systems interacting with an environment, impacts our understanding of exotic nuclei in low-energy nuclear physics, hadrons, cold-atom systems, or even noisy intermediate-scale quantum computers. Such systems often exhibit resonance states characterized by energy positions and dispersions (or decay widths), the properties of which can be difficult to predict theoretically due to their coupling to the continuum of scattering states. Dealing with this phenomenon poses challenges both conceptually and numerically. For that reason, we investigate how the reduced basis method known as eigenvector continuation (EC), which has emerged as a powerful tool to emulate bound and scattering states in closed quantum systems, can be used to study resonance properties. In particular, we present a generalization of EC that we call conjugate-augmented eigenvector continuation, which is based on the complex-scaling method and designed to predict Gamow-Siegert states, and thus resonant properties of OQSs, using only bound-state wave functions as input.

arXiv comment12 pages, 10 figures, published version, Python code provided as ancillary files

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• Towards scalable bound-to-resonance extrapolations for few- and many-body systems
• Interpolating many-body wave functions for accelerated molecular dynamics on the near-exact electronic surface
• Complex scaling in finite volume
• A Vision for the Science of Rare Isotopes
• IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group
• Non-Hermitian quantum mechanics approach for extracting and emulating continuum physics based on bound-state-like calculations: Detailed description
• Non-Hermitian Quantum Mechanics Approach for Extracting and Emulating Continuum Physics Based on Bound-State-Like Calculations
• Complex-energy eigenvector continuation for nuclear many-body broad resonances
• Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation