Jörg Schmiedmayer

Thorsten Schumm, Thorsten Schumm, S. Hofferberth, L. Andersson, S. Wildermuth, S. Groth, S. Groth, I. Bar-Joseph, J. Schmiedmayer, P. Krüger

Matter-wave interference experiments enable us to study matter at its most basic, quantum level and form the basis of high-precision sensors for applications such as inertial and gravitational field sensing. Success in both of these pursuits requires the development of atom-optical elements that can manipulate matter waves at the same time as preserving their coherence and phase. Here, we present an integrated interferometer based on a simple, coherent matter-wave beam splitter constructed on an atom chip. Through the use of radio-frequency-induced adiabatic double-well potentials, we demonstrate the splitting of Bose–Einstein condensates into two clouds separated by distances ranging from 3 to 80 μm, enabling access to both tunnelling and isolated regimes. Moreover, by analysing the interference patterns formed by combining two clouds of ultracold atoms originating from a single condensate, we measure the deterministic phase evolution throughout the splitting process. We show that we can control the relative phase between the two fully separated samples and that our beam splitter is phase-preserving.

T. Langen, R. Geiger, J. Schmiedmayer

The relaxation of isolated quantum many-body systems is a major unsolved problem connecting statistical and quantum physics. Studying such relaxation processes remains a challenge despite considerable efforts. Experimentally, it requires the creation and manipulation of well-controlled and truly isolated quantum systems. In this context, ultracold neutral atoms provide unique opportunities to understand nonequilibrium phenomena because of the large set of available methods to isolate, manipulate, and probe these systems. Here, we give an overview of the rapid experimental progress that has been made in the field over the past few years and highlight some of the questions that may be explored in the future.

Benjamin Doyon, S. Gopalakrishnan, F. Møller, Jorg Schmiedmayer, Romain Vasseur

Conventional hydrodynamics describes systems with few long-lived excitations. In one dimension, however, many experimentally relevant systems feature a large number of long-lived excitations and conserved quantities even at high temperature, because they are proximate to integrable limits. Such models cannot be treated using conventional hydrodynamics. The framework of generalized hydrodynamics (GHD) was recently developed to treat the dynamics of one-dimensional models: It combines ideas from integrability, hydrodynamics, and kinetic theory to come up with a quantitative theory of transport. GHD has successfully settled several long-standing questions about one-dimensional transport; it has also been leveraged to study dynamical questions beyond the transport of conserved quantities and to systems that are not integrable. In this article, we introduce the main ideas and predictions of GHD, survey some of the most recent theoretical extensions and experimental tests of the GHD framework, and discuss some open questions in transport that the GHD perspective might elucidate. Published by the American Physical Society 2025

G. Acampora, A. Ambainis, N. Ares, L. Banchi, P. Bhardwaj, D. Binosi, G. Briggs, T. Calarco, V. Dunjko, J. Eisert, Olivier Ezratty, P. Erker, F. Fedele, Elies Gil-Fuster, M. Garttner, Mats Granath, Markus Heyl, Iordanis Kerenidis, Matthias Klusch, A. F. Kockum, R. Kueng, Mario Krenn, Jorg Lassig, A. Macaluso, Sabrina Maniscalco, Florian Marquardt, K. Michielsen, G. Muñoz-Gil, Daniel Mussig, Hendrik Poulsen Nautrup, E. V. Nieuwenburg, Román Orús, J. Schmiedmayer, Markus Schmitt, Philipp Slusallek, Filippo Vicentini, C. Weitenberg, Frank K. Wilhelm

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

F. Møller, Gabriel Fern'andez-Fern'andez, T. Schweigler, Paulin de Schoulepnikoff, Jorg Schmiedmayer, G. Muñoz-Gil

Analog quantum simulators provide access to many-body dynamics beyond the reach of classical computation. However, extracting physical insights from experimental data is often hindered by measurement noise, limited observables, and incomplete knowledge of the underlying microscopic model. Here, we develop a machine learning approach based on a variational autoencoder (VAE) to analyze interference measurements of tunnel-coupled one-dimensional Bose gases, which realize the sine-Gordon quantum field theory. Trained in an unsupervised manner, the VAE learns a minimal latent representation that strongly correlates with the equilibrium control parameter of the system. Applied to non-equilibrium protocols, the latent space uncovers signatures of frozen-in solitons following rapid cooling, and reveals anomalous post-quench dynamics not captured by conventional correlation-based methods. These results demonstrate that generative models can extract physically interpretable variables directly from noisy and sparse experimental data, providing complementary probes of equilibrium and non-equilibrium physics in quantum simulators. More broadly, our work highlights how machine learning can supplement established field-theoretical techniques, paving the way for scalable, data-driven discovery in quantum many-body systems.