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#### Summer term 2019

Lecture: Characterization and verification of quantum simulations

Monday 8:30am, room 25.32.03.51 (lecture)

Tuesday 10:30am, room 25.32.02.51 (tutorial class)

*Quantum simulations* and *quantum computing* are among the most exciting applications of quantum mechanics. More generally, in the quantum technology research field one aims to develop new devices using *quantum superposition* and *entanglement*. In a popular wording, these anticipated developments will lead to the *second quantum revolution*.

A main milestone is the use of quantum capabilities to solve a (computational) problem that cannot practically be solved otherwise. Theoretical proposals include integer factoring (Shor's algorithm), speed-ups for optimization and machine learning algorithms, the simulation of complex quantum systems, and certain sampling experiments specifically tailored to that milestone.

But if one cannot obtain the output of a quantum simulation or computation by conventional means how can one make sure that the outcome is correct? The output of integer factorization can efficiently be checked but, for instance, for the estimation of energies in quantum many-body systems, or outcomes of dynamical simulations, the situation is much less clear. Hence, for the development of trusted quantum technologies special characterization and verification techniques are urgently required.

This course gives an introduction to the research field, to the problems of characterization, validation, and verification, and first ways to solve them. More specifically, quantum state tomography, quantum states certification, quantum process tomography, and randomized benchmarking will be covered. In particular, the course provides an overview of the latest developments in this still young and very active research field. The approaches of the course are mainly of conceptual and mathematical nature.

## Content

- Fidelity estimation and certification of quantum state preparations
- Randomized benchmarking for quantum dynamics
- Quantum state and process tomography (based on compressed sensing)

## Details

- See here for
**lecture notes**, - www.mkliesch.eu/cvqs.html for details and the
- Sciebo online repo for exercise sheets and other files.

- Quantum many-body physics,
- Quantum information theory,
- Simulations of complex quantum systems,
- Characterization and validation of quantum computing components, and
- applied math.

Group seminar: Theoretical quantum science and technology

This is our group seminar. Interested students are always very welcome.

Time and place: **Friday 10:30h-12:00h, **either in the seminar room** 25.32.03.51** or the Hilbertraum **25.32.03.48**.

Group members will present and discuss their latest progress and results and give tutorial talks. Some talks will also be given by external visitors.

## Content

## Details

Winter term 2018/2019

# Machine learning in quantum physics

Quantum machine learning is an emerging research field. This first course focuses on the subfield where classical machine learning is applied in quantum physics. The goal of this course is to provide the students with the necessary skills to understand the main ideas and some details of the ongoing research in that area.

## Content

- Introduction to neural networks and deep learning
- Applications in quantum physics

## Details

- See www.mkliesch.eu/mlqm.html and also the
- Sciebo online repo for exercise sheets and other files.