[{"content":"Within this project we will devise hybrid simulation methods, that combine numerical time evolution of NQS with quantum simulation of the same physical situation. We foresee synergetic advantages of the hybrid approach, as incorporating data from the quantum simulation may render the numerical simulation more efficient and, reversely, the obtained classical state representations may allow the investigation of properties inaccessible in the experiment. This new method will be the basis for us to investigate dynamics in two-dimensional Bose-Hubbard models realized in the experimental labs of the research unit. We will moreover extend the approach to develop a novel scheme for (approximate) many-body state tomography based on time evolution native to the quantum simulation platform.\n","externalUrl":null,"permalink":"/projects/project_01/","section":"Projects","summary":"","title":"Combining NQS and quantum simulation: Hybrid algorithms","type":"projects"},{"content":"","externalUrl":null,"permalink":"/team/aidelsburger/","section":"Team","summary":"","title":"Monika Aidelsburger","type":"team"},{"content":"","externalUrl":null,"permalink":"/team/blieml/","section":"Team","summary":"","title":"Niklas Blieml","type":"team"},{"content":"Annabelle Bohrdt is a theoretical physicist aiming for a microscopic understanding of strongly correlated quantum systems by developing new analysis tools. In her research, she combines numerical methods, intuitive physical pictures, close collaboration with quantum simulation experiments, and machine learning techniques. She obtained her doctoral degree from Technical University Munich (Germany). During her PhD, Annabelle spent two years as an exchange student in the group of Eugene Demler at Harvard. From 2021 to 2023, she was an independent ITAMP postdoctoral fellow at Harvard University. From 2023 to 2025, she was a professor for theoretical physics at the University of Regensburg, Germany. In 2025, she joined the faculty of LMU Munich, Germany.\n","externalUrl":null,"permalink":"/team/bohrdt/","section":"Team","summary":"","title":"Annabelle Bohrdt","type":"team"},{"content":"Itinerant bosonic systems are of great interest but hard to tackle due to the large number of modes. We will use cutting-edge methods in theory and experiment to advance the understanding of these systems. On the one hand, we will advance matter-wave microscopy to image continuous quantum gases with sub-micron effective resolution, enabling us to probe local information spreading. On the other hand, we plan to develop new NQS-based techniques for numerical simulations of bosonic systems in the challenging realms of two spatial dimensions and continuous space.\n","externalUrl":null,"permalink":"/projects/project_02/","section":"Projects","summary":"","title":"Information Dynamics of Strongly-Interacting Bosons","type":"projects"},{"content":"This project applies ML techniques to optimize single- and two-qutrit gates in neutral atom tweezer arrays. We employ Bayesian optimization and reinforcement learning, starting with numerical simulations that incorporate experimentally benchmarked decoherence and loss channels for Ytterbium atoms in optical arrays. Based on these results, we will target experimental implementations in order to demonstrate increased fidelities and enhanced robustness of the various gate implementations.\n","externalUrl":null,"permalink":"/projects/project_03/","section":"Projects","summary":"","title":"Machine learning for qutrit-based quantum computing and simulation with Rydberg atoms","type":"projects"},{"content":"Marin Bukov investigates out-of-equilibrium quantum dynamics, including the design and engineering of dynamical protocols to control interacting quantum systems (e.g., quantum simulators or quantum computers), using techniques from deep reinforcement learning and unsupervised learning: https://www.pks.mpg.de/nqd\n","externalUrl":null,"permalink":"/team/bukov/","section":"Team","summary":"","title":"Marin Bukov","type":"team"},{"content":"Giuseppe Carleo is a professor of computational quantum physics at EPFL, where he leads the Computational Quantum Science Laboratory (CQSL). His research develops principled numerical and machine-learning methods to study strongly correlated quantum systems, spanning equilibrium, non-equilibrium dynamics, and quantum simulation.\n","externalUrl":null,"permalink":"/team/carleo/","section":"Team","summary":"","title":"Giuseppe Carleo","type":"team"},{"content":"This project harnesses ML to develop scalable, feedback‑driven control of complex many-body quantum states. Using information from mid‑circuit measurements it aims to variationally discover interactive circuit architectures that prepare and preserve many‑body states without gradient methods or full classical simulability. We will apply advanced pattern recognition to syndrome and stabilizer measurement data \u0026ndash; pushing the limits of fast, ML‑assisted decoding for deformed quantum memories, teleportation, and GHZ state generation in constant‑depth circuits by identifying patterns and extracting the key information that steer complex quantum systems into desired states. Finally, by combining supervised learning for decoding with RL for real‑time feedback control, the project embodies a unified strategy discovery framework that adapts to device‑specific noise characteristics, integrates with state of the art decoding algorithms, and uncovers optimal protocols for fault‑tolerant quantum information processing.\n","externalUrl":null,"permalink":"/projects/project_04/","section":"Projects","summary":"","title":"Learning feedback control of monitored quantum dynamics","type":"projects"},{"content":"Martin Gärttner\u0026rsquo;s research focuses on using synthetic quantum systems as physical simulation devices to explore quantum many-body phenomena. This requires the development of methods for benchmarking these devices using tools from quantum information theory and machine learning. Specifically, we develop methods for efficiently characterizing quantum states from measurements, for example quantifying their entanglement, and for simulating quantum many-body physics on classical computers, thereby pushing the limits of numerical methods. He is a professor at Jena University. For more information, see https://qiqs-jena.de\n","externalUrl":null,"permalink":"/team/gaerttner/","section":"Team","summary":"","title":"Martin Gärttner","type":"team"},{"content":"This project develops ML-enhanced techniques for improving the processing of data from ultracold atom experiments. We optimize the analysis of fluorescence and absorption images in optical lattice experiments where site-resolved atom numbers and phases are to be reconstructed. Furthermore, we develop methods for the reconstruction of (sub)system states that optimally exploit prior knowledge in the form of physical constraints or ML inspired variational ansatz functions \u0026ndash; a dimensional reduction task. Finally, we develop adaptive measurement strategies for choosing the optimal measurement settings based on previous observations on the system. Here ML methods allow us to overcome the prohibitive numerical complexity of traditional methods.\n","externalUrl":null,"permalink":"/projects/project_05/","section":"Projects","summary":"","title":"Optimal readout of quantum simulators","type":"projects"},{"content":"","externalUrl":null,"permalink":"/team/heyl/","section":"Team","summary":"","title":"Markus Heyl","type":"team"},{"content":"This project is dedicated to advancing the characterization of complex quantum many-body systems beyond current capabilities by means of pattern recognition enabled by ML techniques. Today, quantum simulators and quantum computers have entered a regime of generating large amounts of data by means of snapshot measurements. We aim to address the accompanying challenge of how to extract most information without the typically performed dimensional reduction to low-order correlation functions, which is particularly relevant for the characterization and identification of topological quantum matter such as quantum spin liquids. Concretely, we will introduce weighted wave-function networks as a novel tool enabling not only to extract patterns in an unbiased manner, but also to allow for detecting quantum entanglement of complex quantum many-body systems.\n","externalUrl":null,"permalink":"/projects/project_06/","section":"Projects","summary":"","title":"Wave function networks for correlated quantum matter","type":"projects"},{"content":"","externalUrl":null,"permalink":"/team/marquardt/","section":"Team","summary":"","title":"Florian Marquardt","type":"team"},{"content":"This project combines recent quantum information-theoretic methods from quantum communication and cryptography with ML techniques to develop entanglement measures suitable for many-body systems. For this we take an operational approach, quantifying entanglement relative to specific tasks, where entanglement can be exploited for enhanced efficiency. This operational approach also addresses computational considerations: our measures account only for entanglement that can be feasibly extracted. Ultimately, we aim to apply these novel entanglement measures to characterize entanglement strucutres in many-body states realized in present-day quantum simulators.\n","externalUrl":null,"permalink":"/projects/project_07/","section":"Projects","summary":"","title":"Operationally meaningful entanglement measures","type":"projects"},{"content":"Renato Renner is a theoretical physicist working in quantum information theory and quantum foundations. His research interests range from quantum cryptography and quantum thermodynamics to the foundations of quantum theory and gravity. He studied physics at EPFL Lausanne and ETH Zürich. He then moved to the Computer Science Department, ETH Zürich, to work on a thesis in the area of quantum cryptography. From 2005 to 2007, he held an HP Research Fellowship at the Department for Applied Mathematics and Theoretical Physics, University of Cambridge, U.K. Since 2007, he has been with the Physics Department, ETH Zürich, where he is a Professor of theoretical physics.\n","externalUrl":null,"permalink":"/team/renner/","section":"Team","summary":"","title":"Renato Renner","type":"team"},{"content":"Markus Schmitt is a theoretical physicist with a focus on computational quantum many-body physics. He investigates correlated quantum systems in and out of equilibrium, targeting research questions that range from the characterization of non-equilibrium quantum matter to the optimal implementation of quantum logical operations. For this purpose, he develops novel computational approaches, that incorporate machine learning tools to address central challenges. Markus pursues his research as a group leader at the Regensburg University and Forschungszentrum Jülich. For more details, see the group website.\n","externalUrl":null,"permalink":"/team/schmitt/","section":"Team","summary":"","title":"Markus Schmitt","type":"team"},{"content":"Simon Trebst applies computational many-body approaches to study entangled states of matter, often from a conceptual perspective. His interests oscillate between exploring long-range entangled quantum ground states, such as spin liquids, in the context of frustrated magnets and Kitaev materials and understanding highly entangled mixed states arising quantum circuit dynamics. Recent themes include monitored quantum criticality, decoding transitions, and quantum chaos.\n","externalUrl":null,"permalink":"/team/trebst/","section":"Team","summary":"","title":"Simon Trebst","type":"team"},{"content":"Christof Weitenberg is an experimental physicist working with ultracold atoms. His research focus is on quantum many-body phases in optical lattices, in particular topological phases induced by Floquet engineering. Another focus are microscopy techniques including new matter-wave microscopy schemes. Christof Weitenberg obtained his PhD from LMU Munich, worked at ENS Paris and University of Hamburg and currently leads a research group at TU Dortmund University (https://ucqg.physik.tu-dortmund.de/).\n","externalUrl":null,"permalink":"/team/weitenberg/","section":"Team","summary":"","title":"Christof Weitenberg","type":"team"},{"content":"FOR 5919 \u0026ldquo;Machine Learning for Complex Quantum States\u0026rdquo; is a DFG Research Unit. We develop and employ tools of machine learning to investigate composite quantum systems with a focus on artificial quantum systems.\n","date":"20 August 2026","externalUrl":null,"permalink":"/","section":"","summary":"","title":"","type":"page"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/authors/hongzheng-zhao/","section":"Authors","summary":"","title":"Hongzheng Zhao","type":"authors"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/authors/marin-bukov/","section":"Authors","summary":"","title":"Marin Bukov","type":"authors"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/authors/markus-heyl/","section":"Authors","summary":"","title":"Markus Heyl","type":"authors"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/publications/","section":"Publications","summary":"","title":"Publications","type":"publications"},{"content":"Accurate digital quantum simulation at long times is limited by the accumulation of errors inherent to approximate simulation. Here we introduce RL-Trotter, a reinforcement-learning framework that treats unavoidable approximation errors as resources for error correction rather than merely imperfections to suppress. We show that low-dimensional information from conservation laws, such as the energy and energy variance, provides a sufficient learning signal to guide the agent, which learns to adapt a single scalar — the next Trotter step size — without access to the target wave function. By optimizing the entire long-time evolution rather than individual steps, RL-Trotter discovers self-correcting sequences in which later errors compensate for those accumulated earlier, increasing the accuracy of the long-time dynamics. The learned policies are intrinsically robust to measurement noise, substantially reducing measurement overhead. They also generalize to previously unseen, physically similar initial states and transfer from small, classically simulable systems to systems an order of magnitude larger. This enables a practical protocol based on classical pretraining followed by direct deployment or limited fine-tuning on quantum hardware. Our results establish a broader perspective for quantum algorithms: errors in approximate evolution can be orchestrated into resources for accurate and resource-efficient quantum dynamics.\nRead the paper on arXiv · Download PDF\n","date":"20 August 2026","externalUrl":null,"permalink":"/publications/2608.20139_rl_trotter/","section":"Publications","summary":"","title":"Reinforcement Learning to Harness Approximation Errors for Long-Time Quantum Simulation","type":"publications"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/authors/roderich-moessner/","section":"Authors","summary":"","title":"Roderich Moessner","type":"authors"},{"content":"","date":"20 August 2026","externalUrl":null,"permalink":"/authors/yu-bo-shi/","section":"Authors","summary":"","title":"Yu-Bo Shi","type":"authors"},{"content":"","date":"31 July 2026","externalUrl":null,"permalink":"/authors/misha-yutushui/","section":"Authors","summary":"","title":"Misha Yutushui","type":"authors"},{"content":"The meticulous preparation of macroscopic Greenberger-Horne-Zeilinger (GHZ) states provides a foundational resource for quantum technologies such as metrology, cryptography, and fault-tolerant codes. While state-of-the-art measurement-based protocols offer efficient low-depth execution, their performance can be bottlenecked by conventional decoders, such as minimum weight perfect matching (MWPM) or even maximum-likelihood decoding (MLD), which optimize for binary logical recovery and fail to maximize the continuous long-range order characteristic of a GHZ state for two-dimensional geometries. Here we overcome this limitation by framing the decoding problem as minimum Bayesian risk inference, introducing a general paradigm that maximizes the expected utility of the decoded state. Implementing this maximum-utility approach, we construct an algorithm that achieves the highest possible per-shot decoded quantum order and thereby establish an optimal decoding strategy for measurement-based GHZ state preparation. To improve its computational efficiency, we design a scalable two-stage decoder, which first encodes the syndromes into the edge weights of MWPM and then refines the result with a convolutional neural network trained to maximize the expected utility, at a fraction of the cost of the optimal decoder. Remarkably, we find that the first stage alone — which makes the matching aware of the gauge choice at no cost beyond bare MWPM — already performs near-optimally up to the largest sizes we study, N = 256 × 256, closing up to 87% of the gap between the bare-MWPM and optimal decoding thresholds. Generalizing MWPM and MLD, the maximum-utility decoder (MUD) establishes a versatile framework that can be explicitly tailored to the operational demands of specific experiments by redefining the utility function.\nRead the paper on arXiv · Download PDF\n","date":"31 July 2026","externalUrl":null,"permalink":"/publications/2608.00160_ghz_max_utility_decoder/","section":"Publications","summary":"","title":"Optimal Decoding for Measurement-Based GHZ State Preparation: The Maximum-Utility Decoder","type":"publications"},{"content":"","date":"31 July 2026","externalUrl":null,"permalink":"/authors/simon-trebst/","section":"Authors","summary":"","title":"Simon Trebst","type":"authors"},{"content":"","date":"31 July 2026","externalUrl":null,"permalink":"/authors/theo-haas/","section":"Authors","summary":"","title":"Theo Haas","type":"authors"},{"content":"","date":"20 July 2026","externalUrl":null,"permalink":"/news/","section":"News","summary":"","title":"News","type":"news"},{"content":"Today, the team of Project 1 gathered at LMU Munich for a first task force meeting to discuss recent progress and opportunities for joint experimental and theoretical efforts to leverage synergies by combining quantum simulation with numerical simulation based on neural quantum states.\nBuilding on recent work by research unit members Anka van de Walle, Markus Schmitt, and Annabelle Bohrdt the project aims to incorporate data obtained from quantum simulation experiments to improve the efficiency of numerical simulations or to infer additional information prepared on the quantum simulator.\n","date":"20 July 2026","externalUrl":null,"permalink":"/news/05_p1_task_force_meeting/","section":"News","summary":"Today, the team of Project 1 gathered at LMU Munich for a first task force meeting to discuss recent progress and opportunities for joint experimental and theoretical efforts to leverage synergies by combining quantum simulation with numerical simulation based on neural quantum states.\n","title":"Task force meeting: Combining quantum simulation with NQS","type":"news"},{"content":"As part of our activities, we regularly organize workshops, schools, and conferences.\nUpcoming activities # ","date":"17 June 2026","externalUrl":null,"permalink":"/upcoming_events/","section":"Events","summary":"","title":"Events","type":"upcoming_events"},{"content":"Mark your calendars: On Jan 18-21, 2027, we are co-organizing the \u0026ldquo;875th WEH Seminar - Generative modeling in quantum science\u0026rdquo; at the Physikzentrum in Bad Honnef, Germany.\nRecent years have witnessed remarkable progress in artificial intelligence driven by the advancement of generative modeling (GM) techniques. These developments have a transformative impact across application areas — including scientific research, where AlphaFold3\u0026rsquo;s diffusion-based protein structure prediction is a striking example. This workshop will address the particularly compelling intersection of GM with quantum science. On the one hand, GM techniques expand the scientific toolbox to investigate physics that is based on the fundamentally probabilistic laws of quantum mechanics. Conversely, the intrinsically probabilistic nature of quantum computation itself offers a new computational resource for generative modeling, potentially enabling quantum algorithms to tackle generative tasks with advantages over classical approaches. Developing a solid theoretical understanding remains an outstanding challenge in either case and statistical physics has proven an insightful tool to investigate the underlying principles of learning processes. Within this workshop we plan to discuss the bidirectional relationship between generative modeling and quantum science from the different perspectives of quantum many-body physics, quantum computing, and statistical physics. It will provide an opportunity for multi-disciplinary dialogue to explore emerging synergies and the transformative potential of GM for both fundamental research and technological applications.\nMore details are available online.\n","date":"17 June 2026","externalUrl":null,"permalink":"/upcoming_events/27-01-weh_gmqs/","section":"Events","summary":"Mark your calendars: On Jan 18-21, 2027, we are co-organizing the “875th WEH Seminar - Generative modeling in quantum science” at the Physikzentrum in Bad Honnef, Germany.\n","title":"Jan 2027: WEH Seminar and annual meeting","type":"upcoming_events"},{"content":"On June 15 2026 we officially kicked off our research unit \u0026ldquo;FOR 5919: Machine learning for complex quantum states\u0026rdquo; with a two-day-long workshop.\nThe whole team gathered in Würzburg to discuss recent developments and ideas for advancements the areas of quantum simulation, quantum computing, condensed matter, and quantum information facilitated by machine learning techniques. During two intense days it was great to get to know all research unit members and to learn about first results that have already been achieved on the different projects. The program was enriched with external contributions by Juan Carrasquilla, Gorka Muños-Gil and Evert van Nieuwenburg. Upon this highly energetic get-together we\u0026rsquo;re now looking forward to numerous fruitful collaborations during the upcoming four years.\n","date":"16 June 2026","externalUrl":null,"permalink":"/news/04_kick_off/","section":"News","summary":"On June 15 2026 we officially kicked off our research unit “FOR 5919: Machine learning for complex quantum states” with a two-day-long workshop.\n","title":"FOR 5919 kick-off meeting","type":"news"},{"content":"In the week of 29 November – 4 December 2026 we are going to hold the 1st Winter School for PhD and Master Students at the Max Planck Institute for the Science of Light, Erlangen.\nApplication Details # Deadline: September 30, 2026 Registration Fee: Free, but approval is required. Website \u0026amp; Application: https://indico.mlcqs.de/event/1/ Contact: For inquiries, contact Ms. Julia Stier at julia.stier@verwaltung.uni-regensburg.de Accommodation # Participants are expected to book their own travel and accommodation. Do NOT book your travel and accommodation before your application has been approved!\nThe following list of hotels may serve as a guideline:\nStadthaus Erlangen Hotel Luise Hotel Bayerischer Hof The niu About the School \u0026amp; Tracks # Are you fascinated by the intersection of artificial intelligence and quantum physics? This specialized school invites PhD and Master students to explore cutting-edge machine learning techniques applied to complex quantum systems. Organized as part of the Research Unit FOR 5919, the program combines theoretical lectures with comprehensive hands-on coding tutorials to bridge foundational concepts with active research.\nParticipants choose one of two specialized tracks during Days 2–4:\nTrack 1 (Neural Quantum States): Focuses on representing complex many-body wave functions using neural network architectures, variational Monte Carlo, and ground state searches. No prior NQS knowledge is required! Track 2 (Reinforcement Learning for Quantum Tech): Covers RL fundamentals, strategies for quantum control optimization, quantum error correction protocols, and quantum circuit dynamics. No prior RL knowledge is required! Program Schedule \u0026amp; Logistics # Phase 1 (Nov 30, 1:30 PM Kick-off): Introduction to deep learning, neural network architectures, and optimization. Phase 2 (Dec 1–3): Intensive track-specific lectures and interactive track-oriented hands-on programming tutorials. Phase 3 (Dec 4, Ends 12:30 PM): Joint scientific session featuring contemporary research talks. Prerequisites: A basic understanding of quantum mechanics and Python proficiency are expected; no prior machine learning experience is needed.\nEquipment Required: Participants must bring their own laptops for the hands-on numerical and coding sessions.\nLecturers # NQS Track\nAnnabelle Bohrdt (LMU Munich) Markus Heyl (Univ. of Augsburg) RL Track\nFlorian Marquardt (MPL Erlangen) Marin Bukov (MPI-PKS Dresden) \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ","date":"15 June 2026","externalUrl":null,"permalink":"/upcoming_events/26-11-school/","section":"Events","summary":"In the week of 29 November – 4 December 2026 we are going to hold the 1st Winter School for PhD and Master Students at the Max Planck Institute for the Science of Light, Erlangen.\n","title":"Nov 2026: 1st MLCQS Winter School","type":"upcoming_events"},{"content":"Mark your calendars: On Jan 18-21, 2027, we are co-organizing the \u0026ldquo;875th WEH Seminar - Generative modeling in quantum science\u0026rdquo; at the Physikzentrum in Bad Honnef, Germany.\nRecent years have witnessed remarkable progress in artificial intelligence driven by the advancement of generative modeling (GM) techniques. These developments have a transformative impact across application areas — including scientific research, where AlphaFold3\u0026rsquo;s diffusion-based protein structure prediction is a striking example. This workshop will address the particularly compelling intersection of GM with quantum science. On the one hand, GM techniques expand the scientific toolbox to investigate physics that is based on the fundamentally probabilistic laws of quantum mechanics. Conversely, the intrinsically probabilistic nature of quantum computation itself offers a new computational resource for generative modeling, potentially enabling quantum algorithms to tackle generative tasks with advantages over classical approaches. Developing a solid theoretical understanding remains an outstanding challenge in either case and statistical physics has proven an insightful tool to investigate the underlying principles of learning processes. Within this workshop we plan to discuss the bidirectional relationship between generative modeling and quantum science from the different perspectives of quantum many-body physics, quantum computing, and statistical physics. It will provide an opportunity for multi-disciplinary dialogue to explore emerging synergies and the transformative potential of GM for both fundamental research and technological applications.\nMore details are available online.\n","date":"28 April 2026","externalUrl":null,"permalink":"/news/03_weh_seminar_2027/","section":"News","summary":"Mark your calendars: On Jan 18-21, 2027, we are co-organizing the “875th WEH Seminar - Generative modeling in quantum science” at the Physikzentrum in Bad Honnef, Germany.\n","title":"875th WEH Seminar: Generative modeling in quantum science","type":"news"},{"content":"On June 15/16, 2026, we will hold our kick-off meeting.\nBesides our internal kick-off program, we will hold a symposium on \u0026ldquo;Machine Learning for Complex Quantum States\u0026rdquo; with invited speakers\nJuan Carrasquilla (ETH Zürich) Gorka Muñoz Gil (University of Innsbruck) Evert van Nieuwenburg (Leiden University) More details are available on the event webpage.\n","date":"28 January 2026","externalUrl":null,"permalink":"/upcoming_events/26-06-kick_off/","section":"Events","summary":"On June 15/16, 2026, we will hold our kick-off meeting.\nBesides our internal kick-off program, we will hold a symposium on “Machine Learning for Complex Quantum States” with invited speakers\nJuan Carrasquilla (ETH Zürich) Gorka Muñoz Gil (University of Innsbruck) Evert van Nieuwenburg (Leiden University) More details are available on the event webpage.\n","title":"June 2026: Kick-off meeting","type":"upcoming_events"},{"content":"Are you looking for an opportunity to dive into experimental or theoretical research at the intersection of quantum matter, quantum information, and machine learning? Then don’t hesitate to get in touch - we are always looking for motivated and curious minds to join our expedition through this fascinating world.\n","date":"19 January 2026","externalUrl":null,"permalink":"/joinus/","section":"Join us","summary":"","title":"Join us","type":"joinus"},{"content":"Our recently established collaborative research initiative “Machine Learning for Complex Quantum States” (MLCQS) – a consortium spanning eleven different institutions across Germany and Switzerland – is seeking to fill several PhD positions.\nMLCQS will develop and employ tools of machine learning to investigate composite quantum systems with a focus on artificial quantum systems. Advanced capabilities of modern experimental techniques to manipulate and probe many-body quantum systems have made quantum simulation and quantum computing with substantial numbers of qubits a reality. But at the same time, fully leveraging their potential poses new challenges related to high-dimensional state representations and data as well as optimal control strategies. MLCQS will tackle these challenges by developing machine-learning-enhanced techniques for simulation, data analysis, and control. Our goals range from gaining theoretical insight to advancing experiments with ultracold atoms. Thereby, we will enable new insights into complex quantum states and dynamics.\nWe are looking for highly motivated candidates with a background in quantum many-body physics, quantum optics, or quantum information to work on the following projects:\nCombining neural quantum states and quantum simulation: hybrid algorithms [theory]\nAdvisors: Annabelle Bohrdt (LMU Munich), Markus Schmitt (Regensburg University) Information dynamics of strongly interacting Bosons [theory/experiment]\nAdvisors: Giuseppe Carleo (EPFL), Christof Weitenberg (TU Dortmund) Machine learning for qutrit-based quantum computing and simulation with Rydberg atoms [experiment/theory]\nAdvisors: Monika Aidelsburger (MPQ, Garching), Annabelle Bohrdt (LMU Munich) Learning feedback control of monitored quantum dynamics [theory]\nAdvisors: Marin Bukov (MPI PKS, Dresden), Markus Schmitt (Regensburg University) Optimal readout of quantum simulators [experiment]\nAdvisors: Monika Aidelsburger (MPQ, Garching), Christof Weitenberg (TU Dortmund) Wave function networks for correlated quantum matter [theory]\nAdvisor: Markus Heyl (Augsburg University) To make one of these your PhD project, send your CV and a motivation letter to the e-mail address stated below. Applications will be reviewed starting Feb 27, 2026.\n","date":"19 January 2026","externalUrl":null,"permalink":"/joinus/2025_joint_call/","section":"Join us","summary":"Our recently established collaborative research initiative “Machine Learning for Complex Quantum States” (MLCQS) – a consortium spanning eleven different institutions across Germany and Switzerland – is seeking to fill several PhD positions.\n","title":"Multiple PhD positions available: call for applications","type":"joinus"},{"content":"We are seeking to fill several PhD positions.\nFollow this link for further details.\n","date":"19 January 2026","externalUrl":null,"permalink":"/news/02_2025_joint_call/","section":"News","summary":"We are seeking to fill several PhD positions.\n","title":"Multiple PhD positions available: call for applications","type":"news"},{"content":"After a very energetic on-site review, the DFG together with the SNSF has taken the decision to fund our new Research Unit \u0026ldquo;Machine Learning for Complex Quantum States\u0026rdquo; (FOR 5919).\nFunding is granted to support our research for a period of four years.\n","date":"11 December 2025","externalUrl":null,"permalink":"/news/01_funding_granted/","section":"News","summary":"After a very energetic on-site review, the DFG together with the SNSF has taken the decision to fund our new Research Unit “Machine Learning for Complex Quantum States” (FOR 5919).\n","title":"Funding granted","type":"news"},{"content":"FOR 5919 is a DFG Research Unit.\n","externalUrl":null,"permalink":"/about/","section":"","summary":"","title":"","type":"page"},{"content":"","externalUrl":null,"permalink":"/authors/aidelsburger/","section":"Authors","summary":"","title":"Aidelsburger","type":"authors"},{"content":"","externalUrl":null,"permalink":"/old_authors/annabelle_bohrdt/","section":"Old_authors","summary":"","title":"Annabelle Bohrdt","type":"old_authors"},{"content":"","externalUrl":null,"permalink":"/authors/blieml/","section":"Authors","summary":"","title":"Blieml","type":"authors"},{"content":"","externalUrl":null,"permalink":"/authors/bohrdt/","section":"Authors","summary":"","title":"Bohrdt","type":"authors"},{"content":"","externalUrl":null,"permalink":"/authors/bukov/","section":"Authors","summary":"","title":"Bukov","type":"authors"},{"content":"","externalUrl":null,"permalink":"/authors/carleo/","section":"Authors","summary":"","title":"Carleo","type":"authors"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"","externalUrl":null,"permalink":"/old_authors/christof_weitenberg/","section":"Old_authors","summary":"","title":"Christof Weitenberg","type":"old_authors"},{"content":"","externalUrl":null,"permalink":"/old_authors/florian_marquardt/","section":"Old_authors","summary":"","title":"Florian 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Heyl","type":"old_authors"},{"content":"","externalUrl":null,"permalink":"/authors/marquardt/","section":"Authors","summary":"","title":"Marquardt","type":"authors"},{"content":"","externalUrl":null,"permalink":"/old_authors/martin_gaerttner/","section":"Old_authors","summary":"","title":"Martin Gärttner","type":"old_authors"},{"content":"","externalUrl":null,"permalink":"/old_authors/monika_aidelsburger/","section":"Old_authors","summary":"","title":"Monika Aidelsburger","type":"old_authors"},{"content":"","externalUrl":null,"permalink":"/old_authors/","section":"Old_authors","summary":"","title":"Old_authors","type":"old_authors"},{"content":"Our research is organized in seven collaborative projects described below.\n","externalUrl":null,"permalink":"/projects/","section":"Projects","summary":"","title":"Projects","type":"projects"},{"content":"","externalUrl":null,"permalink":"/old_authors/renato_renner/","section":"Old_authors","summary":"","title":"Renato 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