# Hamiltonian Learning for Quantum Error Correction

## TL;DR

This paper presents a neural network-based method for Hamiltonian learning in quantum devices, enabling scalable validation and error assessment crucial for quantum error correction and topological codes.

## Contribution

The authors introduce a neural net approach trained on solvable models that generalizes to real experimental errors in Hamiltonian inference.

## Key findings

- The method scales effectively with system size.
- It demonstrates resilience against various noise sources.
- It accurately infers Hamiltonians in quantum error correction contexts.

## Abstract

The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processing as well as for reliable quantum memories. Inferring the experimentally realized Hamiltonian through a scalable number of measurements constitutes the challenging task of Hamiltonian learning. In particular, assessing the quality of the implementation of topological codes is essential for quantum error correction. Here, we introduce a neural net based approach to this challenge. We capitalize on a family of exactly solvable models to train our algorithm and generalize to a broad class of experimentally relevant sources of errors. We discuss how our algorithm scales with system size and analyze its resilience towards various noise sources.