Hamiltonian Learning in Quantum Field Theories

TL;DR

This paper presents a method for learning Hamiltonians in quantum field theories from experimental data, enabling analysis across energy scales and offering insights into quantum simulation experiments.

Contribution

It introduces a systematic Hamiltonian learning protocol that captures operator content and couplings, connecting quantum simulation data with effective field theories.

Findings

Demonstrated in theoretical studies and quantum gas experiments.

Enables access to field theories at different energy scales.

Reveals a flow of Hamiltonians akin to renormalization group trajectories.

Abstract

We discuss Hamiltonian learning in quantum field theories as a protocol for systematically extracting the operator content and coupling constants of effective field theory Hamiltonians from experimental data. Learning the Hamiltonian for varying spatial measurement resolutions gives access to field theories at different energy scales, and allows to learn a flow of Hamiltonians reminiscent of the renormalization group. Our method, which we demonstrate in both theoretical studies and available data from a quantum gas experiment, promises new ways of addressing the emergence of quantum field theories in quantum simulation experiments.