Zentrale Lernplattform • LMU München

This course will cover the basics of quantum simulation, with a focus on cold-atom platforms. There will be two major directions: (i) Mapping quantum many-body models onto analog quantum-simulation platforms that are amenable for experimental realization, (ii) exploring the far-from-equilibrium properties of quantum many-body models. Crucial for both directions will be exact diagonalization methods, which will be a main focus of the course and the project component thereof. The course will have a research element to it, as we will be covering the latest in quantum simulation and far-from-equilibrium quantum many-body physics, and it will aim to bring the student up to speed on the state of the art in these fields. Quantum Mechanics I and II are prerequisites.

In this course, different strategies for manipulating light-matter interaction in nanoscale are presented and discussed. Special attention is given to the basics and applications of plasmonics, optical microcavities, nanophotonic biosensing, chiral nanophotonics, quasi-Bound States in the Continuum based- and active metasurfaces, nanophotonics in the quantum regime/single quantum emitters and thermometry in nanoscale.

Few-body physics in ultracold gases (Feshbach resonances, Efimov effect, Rydberg physics, Polaritons), Problems with baths (Bose and Fermi polarons, Fröhlich model, Quantum impurities), Few-body physics in strongly correlated systems (Doped antiferromagnets, Spin-charge separation, emergent gauge theories)

At LMU's Centre for Advanced Laser Applications, we amplify femtosecond laser pulses to exceed 1 petawatt of peak power. Focusing the 30 cm-wide beam onto a 5 µm spot achieves an unprecedented light intensity of 10²² W/cm². This corresponds to an electric field exceeding 10¹⁴ V/m—far beyond many fundamental field strengths in physics. For comparison:

• The electric field binding an electron in hydrogen is ~10¹¹ V/m.

• A field of 10¹² V/m can accelerate a free electron to 70% of the speed of light within half a micron.

• A field of 10¹⁵ V/m can accelerate a proton to 70% of the speed of light within one micron.

• The Schwinger field, which binds virtual electron-positron pairs in the quantum vacuum, reaches 10¹⁸ V/m.

This course explores the rich, complex, and often surprising physics of particle acceleration in relativistic plasmas, with a particular focus on laser-ion (LION) acceleration. We will also discuss real-world applications, including how ultra-short, intense, and mutually synchronous ion, electron, and photon pulses are revolutionizing medical physics research and improve our understanding of interaction of ionizing radiation with matter.

This lecture is designed for Physics Bachelor’s and Master’s students but does not count toward the specialized Medical Physics program.

Coherence is essential for superposition and thus effectively all quantum effects.
This seminar is discussing experiments revealing the different effects of
coherence of a light beam, of single and multiple photons, or of processes, as well as the emerging entanglement and its application in quantum information.

This course covers applications of ultracold neutral atoms for quantum technologies, with the main focus on quantum simulation and quantum computation. Atoms provide many opportunities for the realization of high-fidelity qubits across different energy scales, ranging from the microwave to the optical domain. Laser cooling techniques allow us to efficiently cool the atoms to extremely low temperatures, so that atoms can be trapped in optical potentials generated with laser beams. The high degree of control that has been achieved, for instance, led to the development of the world’s best clocks. In this course we will introduce fundamental concepts and experimental techniques needed to prepare, manipulate and detect cold neutral atoms in optical arrays. We will discuss how interactions between atoms can be engineered to realize few-qubit gates to build a universal quantum computer. Moreover, the interaction between and the dynamics of many particles in optical arrays naturally enable analog quantum simulations of complex many-body systems, ranging from condensed matter to statistical physics and high-energy physics.

Tentative Table of Contents:

• Super Poincaré tranformations and representations
• SUSY quantum field theory
• Superfields
• SUSY Tang Mills Theory
• Local supersymmetry
• Spinning world line
• Pure spinors

In this lecture, we will cover a variety of topics related to the interaction of different quantum degrees of freedom. The lecture builds on the description of the quantized electromagnetic field covered in Quantum Optics 1, but goes significantly beyond the topics covered there. In particular, you will learn about the description and effects occurring when light couples to two- and three-level atoms, and how light propagation is altered through ensembles of particles. Subsequently, we will revisit quantization of the electromagnetic field, and discuss experimentally relevant systems such as cavity QED and quantum optomechanics. We will also venture into the description of open quantum systems and connect this to concepts in quantum information theory. In the end, we will discuss special topics, including quantum-enhanced metrology and quantum networking.
The building blocks discussed in this lecture span a wide range of topics relevant for modern experimental quantum physics, and form the basis of a number of important technological applications including atomic clocks, quantum sensors or quantum computers.

This lecture provides an introduction to quantum computing and quantum error correction. We will cover the basics of quantum computers, starting from the quantum mechanical concepts necessary to understand quantum computing. We will cover elementary examples of quantum operations and quantum circuits and see how quantum algorithms work. We will proceed learning about different experimental platforms suitable for quantum computing and what are the criteria they have to fulfill. We will see that in practice, all quantum computing platforms are prone to errors, and learn how to describe such errors theoretically. We will discuss quantum error correction, which was introduced to detect and correct errors occurring in quantum computation, and see different examples for quantum error correction codes that have been devised in the past years. We will see that due to the existence of the so-called threshold, there is hope that scalable quantum computation is feasible even in the presence of errors.

Advanced Analytics and Machine Learning examines the algorithmic and systems foundations required to realize modern machine learning systems. As many workloads increasingly rely on large datasets and foundation models, performance and reliability are determined not only by learning algorithms, but also by the underlying infrastructure for data management, distributed computation, and operational deployment. The course therefore treats machine learning as an end to end pipeline, spanning data ingestion and storage, large scale processing, model training and evaluation, and inference under resource and latency constraints.

The course develops a principled understanding of scalability through core concepts in high performance computing  and distributed systems, including data locality, communication costs, synchronization, fault tolerance, and scheduling. These principles are connected to practical implementations in widely used platforms for batch and streaming analytics (e.g., Spark, Dask, Ray, Flink, Kafka) and deep learning toolchains (PyTorch and the Transformers ecosystem). Particular attention is given to infrastructure and system issues. The curriculum is complemented by responsible AI considerations and an overview of quantum machine learning as an emerging technology.

Topics: 

• Foundations of scalable analytics and ML systems (parallelism models, distributed abstractions, communication and synchronization costs, fault tolerance, scheduling)
• Data management and large scale processing (data lake architectures, SQL on semi structured data with Hive, Spark SQL, Presto, batch processing with Spark, Dask, Ray)
• Streaming systems and continuous analytics (stream processing with Kafka and Spark Streaming, state, windows, operational semantics)
• Training systems for machine learning (classical ML workflows at scale, deep learning with PyTorch, distributed training principles, checkpointing and performance analysis)
• Inference systems and operational ML (deployment patterns, throughput and latency modeling, efficiency techniques such as batching and quantization basics, monitoring concepts) 
• Emerging technologies (e.g., quantum machine learning).

Dates: 

• Saturday, March 7, 2026 
• Saturday, March 14, 2026 
• Saturday, March 21, 2026
• Saturday, March 28, 2026
• Saturday, April 18, 2026.

Inhalt der Veranstaltung

In den Sitzungen der AG präsentieren und diskutieren wissenschaftliche Mitarbeiter des Lehrstuhls ihren aktuellen Forschungsstand (Progress Report) und wichtige Veröffentlichungen (Journal Club) im Bereich des Quantum Computing.

Im Rahmen dieser AG werden u.a. auch Projekt- und Abschlussarbeitsthemen vergeben.

Contents

The practical course builds on the lecture Introduction to Quantum Computing and is organized with LRZ and DLR.

The topic of the practical course is simulation of quantum circuits. You are going to learn different methods to classically simulate quantum circuits. Based on those you are going to implement and optimize your own simulator in groups of two.

Participation

• Requirements: Successful participation on the lecture Introduction to Quantum Computing at LMU or a similar course at another university.
• Audience: The practical course is aimed at students in the Master's degree program in Computer Science, Media Informatics, Bioinformatics, students in the main study program in Computer Science (Diploma) or Media Informatics (Diploma) as well as students with a minor in Computer Science. Bachelor's students of Computer Science or Media Informatics can specify the lecture as "Vertiefende Themen der Informatik für Bachelor".
• SWS/ECTS: 4 SWS, 6 ECTS according to module description
• Major course assessment: Presentation + Oral Exam
• Participation Restriction: The maximum number of participants is 14.

Important:
Attendance is compulsory during the block practical and punctuality is expected.
Failure to attend will be reported to the examination office with a grade of 5 (failed).
Being late multiple times will have a negative effect on your grade.

The course will be held in English.

Vorlesung: Freitag, 14-17 Uhr
Oettingenstr. 67 - B U101
Die erste Vorlesung findet am 21. April 2023 statt.

Übung: Freitag, 10-12 Uhr
Oettingenstr. 67 - B U101
Die erste Übung findet am 28. April 2023 statt.

Der Einschreibeschlüssel ist: FunWithQuantum=)
Bitte schreiben Sie sich bis Freitag, den 28.04.2023, 12:00 Uhr, ein, wenn Sie an der Vorlesung teilnehmen möchten. Sie müssen sich NICHT im LSF oder in uni2work registrieren.

This seminar offers a variety of topics from High Performance Computing, Quantum Computing, Virtual Reality and Cryptography for students to work on.

Each participant will work on one topic throughout the semester (master students on their own, bachelor students in pairs). The goal is to write a paper, submit it to an imaginary conference committee, review each other's work and present their findings at an end-of-term "conference" in the seminar.

The task is supported by a lecture about scientific writing and presentations and by an individual supervisor for each topic.

The mandatory lectures will take place on Wednesdays, 12-14 h c.t., room 061 in Oettingenstr. 67.
The exam date will likely be a Saturday in July (update expected soon).

This lecture explains the basics of quantum computing, including:

• Mathematical foundations
  
• Quantum bits (qubits) and quantum circuits
  
• Superposition, entanglement and interference
• Quantum oracle algorithms and variational algorithms
  
• Complexity of quantum algorithms and the need for new complexity classes
• Shor's algorithm and the implications for modern cryptography
• Quantum communication and cryptography
• Hardware limitations and error correction
  

This seminar offers a selection of topics in the field of Quantum Computing/Quantum Communication. A successful participation in the lecture "Introduction to Quantum Computing" or similar lectures at other faculties/TUM is highly recommended for this seminar.

Goal: We study the fundamental mathematical concepts of quantum mechanics. In particular, we will discuss principles of quantum mechanics, self-adjoint operators, quadratic forms and Friedrichs extension, spectral theorems, Schrödinger operators, quantum dynamics, scattering theory, semiclassical analysis, and quantum entropy.

Audience : TMP-Master, Master students of Mathematics and Physics. Bachelor students will get "Schein" if pass the course.

Course homepage: https://www.math.lmu.de/~nam/MQM2526.php

Kurzbeschreibung

Dieses Praktikum, das einen Umfang von 12 ECTS (!) hat, vermittelt die Fähigkeit, Anwendungsfälle aus den Bereichen der Optimierung und dem maschinellen Lernen für Quantencomputer zu modellieren und darüber hinaus einen Einstieg in die praktische Arbeit mit existierenden Quantencomputern. Dafür stehen im QAR-Lab derzeit vier Rechner zur Verfügung: Das IBM Q System Two, der IonQ Aria, der Fujitsu DAU und der D-Wave Advantage. In Kooperation mit namhaften Partnern aus der Industrie werden Aufgabenstellungen mit starker Relevanz für praktische Anwendungen vergeben. Studierende haben in Gruppen die Möglichkeit, je eine Aufgabenstellung auf verschiedenen Rechnern auszuführen und zu vergleichen. Das Praktikum schließt mit einer Präsentation der Ergebnisse vor unseren Industriepartnern ab.

Inhalt des Praktikums

Quantencomputing ermöglicht effizientere Ansätze zur Lösung zentraler Probleme der Informatik durch die Nutzung quantenmechanischer Effekte. Mit der zunehmenden Größe und Qualität aktueller Quantencomputer ist es bereits heute möglich diesen Quantenvorteil in der Praxis nachzuweisen. Die Herausforderung besteht im Allgemeinen darin mit den im Quantencomputing zusätzlich zur Verfügung stehenden algorithmischen Bausteinen Lösungsverfahren zu entwickeln, die einen anwendungsrelevanten Quantenvorteil ermöglichen.

Dieses Praktikum stellt eine Einführung in den anwendungsorientierten Einsatz von Quantencomputing dar. Hierbei werden Ansätze aus dem Bereich Quantenoptimierung zur Lösung praxisrelevanter Probleme konzipiert, implementiert und analysiert. Dabei kommt „echte“ Quantenhardware der Hersteller IBM, IonQ, Fujitsu und D-Wave Systems zu Einsatz.

Eine Auswahl der behandelten Themen lautet:

• Grundlagen des Quantencomputings
• Mathematische Modellierung
• Optimierung
• Quantum Annealing
• Quantenoptimierungsalgorithmen
• Einführung in verschiedene QC-Plattform SDKs

Ablauf & Prüfung

Das Praktikum gliedert sich in zwei Phasen: In der dreiwöchigen Theoriephase werden Grundlagenkenntnisse vermittelt, während in der Praxisphase (startend ab der vierten Woche) in Gruppen an jeweils einer Aufgabenstallung gearbeitet wird. Die Gruppeneinteilung und Themenvergabe findet voraussichtlich Ende der 3. Semesterwoche statt. 

Im Rahmen der Projektphase wird pro Gruppe eine ca. zehnseitige wissenschaftliche Arbeit erstellt, die insbesondere die eigene Methodik und erzielte Ergebnisse beinhaltet. Das Praktikum schließt mit einer benoteten Präsentation der Ergebnisse ab.

Termine

Wöchentlich Dienstags, 10-12 Uhr, Oettingenstr. 67, U133, und Donnerstags, 14-16 Uhr, Oettingenstr. 67, 131, sowie Zusatztermine mit den Betreuern bei Bedarf, remote / in den Räumen des Lehrstuhls. Bei einer großen Anzahl der Termine besteht Anwesenheitspflicht. (Details folgen auf der Veranstaltungswebseite des Lehrstuhls.) (Beginn: 22.04.2025, Ende: 24.07.2025, bis auf ggf. später stattfindende mündliche Prüfung.)

Ort: 169 Oettingenstr. 67
Zeit: Do 14:00-16:00 (wöchentlich)

Zentralanmeldung

Dieses Praktikum vermittelt die Fähigkeit, Anwedungsfälle aus den Bereichen der Optimierung un dem maschinellen Lernen für Quantencomputer zu modellieren und darüber hinaus einen Einstieg in die praktische Arbeit mit existierenden Quantencomputern. Dafür stehen im QAR-Lab derzeit vier Rechner zur Verfügung: Das IBM Q System Two, der IonQ Aria, der Fujitsu DAU und der D-Wave Advantage. In Kooperation mit namhaften Partnern aus der Industrie (BASF, BMW, SAP und Siemens) werden Aufgabenstellungen mit starker Relevanz für praktische Anwendungen vergeben. Bis zu 24 Studierende haben in Gruppen die Möglichkeit, je eine Aufgabenstellung auf verschiedenen Rechnern auszuführen und zu vergleichen. Das Praktikum schließt mit einer Präsentation der Ergebnisse vor unseren Industriepartnern ab.

Diese Veranstaltung findet in Kooperation mit dem BMWK geförderten Projekt QCHALLenge statt.

We will cover among other topics:

• Review of Quantum Computing Basics
• Quantum Optimization
  • Quantum Annealing (QUBO, Ising)
  • QAOA
  • Variational Quantum Eigensolver
  • Applications (MaxClique, Flight Gate Assignment, Robot Movement, Portfolio Optimization)
• Quantum Machine Learning
  • Quantum Classifier and its Applications
  • Variational Classifiers
  • Quantum Neural Networks

This seminar covers a selection of current topics from the areas of High Performance Computing, Quantum Computing, Virtual Reality and Cryptography for students to work on.

Participants will work on one topic throughout the semester (master students on their own, bachelor students in teams of two). The goal is to write a paper, submit it to a fictitious conference committee, review each other's work and present the findings at an end-of-term "conference" in the seminar.

The task is supported by a lecture about scientific writing and presentations and by an individual supervisor for each topic.

The final seminar presentations will take place in block from at the of the semester.

Here is a tentative list of topics covered in the seminar:

• CPU vs. GPU Architectures - Contrasting the Latency-Optimized design of CPUs (complex branching) with the Throughput-Optimized design of GPUs (massive parallelism)
• Evolution of GPU Hardware - Tracing the path from early fixed-function graphics chips to the general-purpose GPUs used in modern AI
• TPUs (Tensor Processing Units) - Google’s custom ASICs that use Systolic Array designs to pass data through a grid of processors without constant memory access
• Vector and Matrix Engines (AVX/SVE/AMX) - Specialized units within a CPU designed to perform vector and matrix operations to accelerate deep learning
• Neuromorphic Computing - Computer chips that mimic how the brain uses "spiking neurons" to process information
• Using the Cerebras AI Chip for Scientific Computing - Strategies for developing scientific computing applications on this non-traditional hardware platform
• Julia for HPC - Utilizing the Julia programming language for high performance numerical and scientific computing
• Domain-Specific Languages (DSLs) - Specialized languages like Halide that simplify writing high performance code for specific hardware targets
• Lightweight Virtualization (Firecracker) - Using microVMs to provide the isolation of traditional virtual machines with the speed and efficiency of containers
• Mixed-Precision Computing - Utilizing lower-bit formats (such as BF16) to accelerate scientific simulations while maintaining accuracy
• In-Situ Analysis and Visualization - Processing and visualizing data in real-time while it is still in the memory of the supercomputer, avoiding the bottleneck of writing to disk
• DNA Data Storage - The experimental use of synthetic DNA to store massive amounts of data in a biological format that can last for centuries
• In-Network Computing with SmartNICs - Offloading computational tasks (like data reduction or encryption) directly to smart Network Interface Cards to improve HPC efficiency
• Ultra Ethernet - An effort to evolve standard Ethernet into a high-speed, reliable fabric suitable for massive AI training clusters and HPC
• Zero Trust Networking - A security philosophy where no device, user, or connection is trusted by default
• Trusted Execution Environments (TEEs) - Hardware vaults such as Intel SGX that protect sensitive data and code even from the computer’s own OS
• Digital Sovereignty - The movement by nations to build independent hardware and cloud infrastructures to ensure data remains within their physical borders
• Post-Quantum Cryptography Hardware - New chip designs built to run the complex math needed to stay safe from future quantum computer attacks
• Processing-in-Memory (PIM) - While TPUs use systolic arrays to minimize memory access, PIM goes a step further by integrating logic directly into the memory chips (DRAM or SRAM)
• Quantum Accelerators for HPC - Hybrid classical-quantum computing models and how emerging quantum processors may be integrated into traditional supercomputing workflows

This seminar covers a selection of current topics from the areas of High Performance Computing, Quantum Computing, Virtual Reality and Cryptography for students to work on.

Each participant will work on one topic throughout the semester (master students on their own, bachelor students in pairs of two). The goal is to write a paper, submit it to a fictitious conference committee, review each other's work and present the findings at an end-of-term "conference" in the seminar.

The task is supported by a lecture about scientific writing and presentations and by an individual supervisor for each topic.

The mandatory lectures will take place on Wednesdays, 12-14 h c.t., room 027 in Oettingenstr. 67.
The final presentations will take place in block from.

This seminar offers a selection of topics in the field of Quantum Computing/Quantum Communication. A successful participation in the lecture "Introduction to Quantum Computing" or similar lectures at other faculties/TUM is highly recommended for this seminar.

Machine Learning (ML) and Artificial Intelligence (AI) are transformational technologies that will have a significant impact on science and business. The aim of this seminar is to give the student an overview of the topics of data & compute infrastructures, machine learning and AI. The aim is to develop a technical understanding of large-scale systems and infrastructures for data infrastructures and advanced analytics. The students deepen their computer science knowledge in a practice-oriented way and with methods, techniques, procedures, tools and infrastructures for the processing and analysis of large data:

• Machine Learning (Methods & Tools: Tensorflow and Pytorch)
• Deep Learning: Convolutional Neural Networks (ResNet, Yolo, SSD)
• Natural Language Processing: Large Language Models, Knowledge Graphs
• Scalable Machine Learning: Distributed Training, AI Hardware
• Quantum Machine Learning: Variational Algorithms, Optimization
• Emerging Machine Learning Applications in Computer Systems, Cybersecurity and Fault Tolerance
• Responsible AI: AI Ethics, Robust AI

Im Rahmen dieses Seminars werden ausgewählte Themen aus dem Bereich der Mobilen und Verteilten Systeme behandelt, die insbesondere aus den Forschungsschwerpunkten des Lehrstuhls stammen. In den letzten Semestern führte das zu einem Fokus auf Themen aus dem Bereich des Maschinellen Lernens und Quantum Computing.

Im Rahmen dieses Seminars werden ausgewählte Themen aus dem Bereich der Mobilen und Verteilten Systeme behandelt, die insbesondere aus den Forschungsschwerpunkten des Lehrstuhls stammen. In den letzten Semestern führte das zu einem Fokus auf Themen aus dem Bereich des Maschinellen Lernens und Quantum Computing.