Machine Learning and the Complexity of Quantum Simulation

B. Jobst, Frank Pollmann, Technische Universität München

Henning Schlömer, Annabelle Bohrdt

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.

Jincheng Zhang

This paper explores the potential of leveraging quantum simulation for machine learning optimization, specifically focusing on the design of a novel algorithm that utilizes quantum-enhanced feature extraction and rapid learning. The core concept revolves around a hybrid approach combining traditional machine learning techniques with a quantum simulation framework. We propose a quantum-guided optimization process, where the algorithm explores the quantum state space to identify optimal features and parameters, ultimately leading to improved model performance and generalization capabilities. This work addresses the limitations of classical machine learning in handling complex, non-linear data distributions, presenting a promising avenue for advancements in machine learning through the incorporation of quantum mechanics. The paper details the algorithm's design, experimental validation, and potential implications for various machine learning tasks.

I. B. Spielman amd J. P. Zwolak

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.

Y.V.R. Naga Pawan, Bhanu Prakash Kolla

Weikang Li, Yixuan Ma, Dong-Ling Deng