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Trapped Ion Quantum Computing Quantum Machine Learning Quantum Simulation

Can machine learning for quantum-gas experiments be explainable?

arXiv

Authors: I. B. Spielman amd J. P. Zwolak

Year

2026

Paper ID

63782

Status

Preprint

Abstract Read

~2 min

Abstract Words

108

Citations

N/A

Abstract

Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale exponentially with system size. Machine learning (ML) methods are already assisting in each of these areas and are poised to become transformative. Here, we focus on two specific applications of ML to cold-atom-based quantum simulators. These devices generally generate data in the form of images; we first showcase denoising of raw images and then identify solitonic waves in Bose-Einstein condensates. In both of these examples, we comment on the interplay between performance, model complexity, and interpretability.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2026 reference point for readers tracking recent quantum research.
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2605.18689 [2] arXiv https://arxiv.org/abs/2605.18689 [3] Publisher https://arxiv.org/abs/2605.18689

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