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arXiv:2605.18689

Can machine learning for quantum-gas experiments be explainable?

I. B. Spielman amd J. P. Zwolak

cond-mat.quant-gascs.LGphysics.atom-phquant-phSubmitted May 18, 2026• Announced May 19, 2026• 0 citations
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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.

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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.
As ultracold atom experiments become highly controlled and scalable quantum simulators, they require sophisticated control over high-dimensional parameter spaces and generate increasingly complex measurement data that need to be analyzed and interpreted efficiently. Machine learning (ML) techniques have been established as versatile tools for addressing these challenges, offering strategies for data interpretation, experimental control, and theoretical modeling. In this review, we provide a perspective on how machine learning is being applied across various aspects of quantum simulation, with a focus on cold atomic systems. Emphasis is placed on practical use cases -- from classifying many-body phases to optimizing experimental protocols and representing quantum states -- highlighting the specific contexts in which different ML approaches prove effective. Rather than presenting algorithmic details, we focus on the physical insights enabled by ML and the kinds of problems in quantum simulation where these methods offer tangible benefits.
There is no doubt that quantum computing has opened up new horizons and perspectives in many fields, including scientific research. With the advancement of technology and quantum computers, we can now conduct new kinds of scientific experiments: observing quantum properties of individual systems, atoms, electrons and photons as well as influencing and controlling quantum systems. Quantum mechanics describes the properties of quantum systems by their quantum states. Unlike in classical mechanics, quantum states themselves cannot be directly observed in experiments, so many-body dynamics mathematical simulations are useful. Even though many-body dynamics problems can be solved for limited cases, but when we consider real-world pieces of matter this leads to computation-intensive calculations that are beyond classic digital computers. The memory and time needed to describe the quantum state of a many-body system scales exponentially with the size of the system. This has led physics communities to focus their attention on the algorithms underlying modern machine learning with the goal of making progress in quantum matter research. As a consequence, the successful application of machine learning requires effective and informative data representation. This paper discusses some novel aspects of applying classical machine learning algorithms to quantum data derived from measurements associated with quantum systems in many-body simulation problems. In this paper, we investigate and experimentally demonstrate one of the effective methods of the quantum data representation suited for the classical machine learning applications based on quantum classical shadows.
Abstract We establish a dataset of over <?CDATA 1.6×1041.6\times10^41.6×104?> 1.6 × 10 4 experimental images of Bose–Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About <?CDATA 333333?> 33 % of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet—an implementation of a physics-informed ML data analysis framework—consisting of a convolutional-neural-network-based classifier and object detector as well as a statistically motivated physics-informed classifier and a quality metric. This technical note constitutes the definitive reference of the dataset, providing an opportunity for the data science community to develop more sophisticated analysis tools, to further understand nonlinear many-body physics, and even advance cold atom experiments.
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well-defined case of detecting dark solitons-appearing as local density depletions in a Bose-Einstein condensate (BEC)-using a methodology that is extensible to the general task of pattern recognition in images of cold atoms. Studying soliton dynamics over a wide range of parameters requires the analysis of large datasets, making the existing human-inspection-based methodology a significant bottleneck. Here we describe an automated classification and positioning system for identifying localized excitations in atomic BECs utilizing deep convolutional neural networks to eliminate the need for human image examination. Furthermore, we openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.
The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the data in a reasonable time. With that in mind information system engineers are required to investigate different approaches to the data processing. The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to investigate their application to real-life computational problem. This field of study is called quantum (information) systems engineering and usually focuses on technical problems with the contemporary devices. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using multispectral image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. To quantify how and explain why the performance of our model changed when ran on a real device, we propose new explainability metrics. These metrics introduce new meaning to the explainable quantum machine learning; the explanation of the performance issue comes from the quantum device behavior. We also analyzed the expected money costs of running similar experiment on contemporary quantum devices using standard market prices.
Quantum many-body (QMB) systems are generally computationally hard: the computing resources necessary to simulate them exactly can often exceed the existing computation resources by orders of magnitude. For this reason, Richard Feynman proposed the concept of a quantum simulator: quantum systems engineered to obey a prescribed evolution equation and repeating the experiment multiple times. Experimentally, however, as we explain below, the vast majority of observables describing the system are inaccessible. Thus, while Feynman's idea addresses the problem of simulating quantum dynamics, it leaves unsolved the equally fundamental problem of inferring the underlying physics from the limited observables accessible in experiments. Indeed, many complex phenomena associated with QMB systems remain elusive. Perhaps, the most important example is identifying phase transitions in QMB systems when no simple order-parameter exists, which poses major challenges to this day. Complicating the problem further is the fact that, in most cases, it is impossible to learn from numerical simulations, as the underlying systems are often too large to be computable, and small QMB can show strong finite size effects, masking the presence of the transition. Here, we present an unsupervised machine learning approach to study QMB experiments, specifically aimed at detecting phase transitions and crossovers directly from raw experimental measurements. We demonstrate our methodology on systems undergoing Many-Body Localization cross-over and Mott-to-Superfluid phase-transition, showing that it reveals collective phenomena from the very partial experimental data and without any model-specific prior knowledge of the system. This approach offers a general and scalable route for data-driven discovery of emergent phenomena in complex quantum many-body systems.