Quantum Computing

Workshop / June 18, 2026 - July 02, 2026, 5:00 PM

Lectures on topics in applied quantum computing

The Center of Applied Quantum Computing (ZAQC) offers a lecture series on selected topics in applied quantum computing in June. The three lectures will cover the topics

"Tensor trains and their applications" (Thursday, June 18, 2026),
"Quantum-inspired optimization" (Thursday, June 25, 2026) and
"A primer on quantum error mitigation" (Thursday, July 2, 2026).

The lectures will take place online in Teams, each at 5:00 PM. Please register for the event using the registration link.

Event details

Registration

Location

Online

Date

June 18, 2026 - July 02, 2026, 5:00 PM

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Language

English, German

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Quantum-inspired optimization (June 18, 2026)

Quantum-inspired optimization

Quantum-inspired optimization leverages principles from quantum mechanics to enhance classical algorithms, offering novel approaches to solve complex binary optimization problems potentially more efficiently. Binary optimization has a wide range of applications including logistics, finance, machine learning or pharmacy.

In this talk we will give an overview over classical as well as quantum and quantum-inspired algorithms to heuristically solve binary optimization problems including simulated annealing, parallel tempering, quantum annealing and simulated quantum annealing. We will compare these algorithms and discuss their respective advantages and disadvantages.

Topics of the talk:

Introduction to quadratic binary optimization
Classical algorithms
Quantum and quantum-inspired algorithms

Tensor trains and their applications (June 25, 2026)

Tensor trains and their applications

Good approximations are an essential part of today’s numerical methods and are therefore highly desirable, especially when dealing with high-dimensional datasets. One tool for approximating such data are tensor networks. Already introduced in the early 90s for simulating quantum spin chains, tensor networks have proven to be applicable to classical data as well and offer a well-established framework for doing arithmetic with compressed data.

In this talk, we will introduce linear tensor networks, also called tensor trains in literature, and show their applications when dealing with classical data or simulations. Starting with the basics on how the data is approximated, we will continue with the arithmetic of compressed data described by tensor networks. We especially emphasize how tensor network libraries compare to other libraries like NumPy or Torch and describe their benefits and drawbacks.

Topics of the talk:

Introduction to linear tensor networks
Quantics representation
Arithmetic of compressed data
Tensor networks as extension of normal tensors
Functions as tensor networks
Examples: Data compression, neural networks, simulations

A Primer on Quantum Error Mitigation (July 2, 2026)

A Primer on Quantum Error Mitigation

Current generation quantum computers are severely limited by hardware noise, which leads to biased and unreliable measurement outcomes. Quantum error correction (QEC) in principle allows one to suppress this noise and realize fault tolerant quantum computation, but its resource overhead is far beyond what today’s noisy intermediate scale quantum (NISQ) devices can support. This raises a central question: how can we reduce the impact of noise before full fault tolerance is available?

This talk provides a primer on quantum error mitigation: a set of algorithmic techniques that reduce noise induced bias in expectation values at the cost of additional circuit executions and classical post processing, without requiring logical qubits.

Topics of the talk:

Quantum noise processes
Noise tailoring and randomized compiling
Zero-noise extrapolation
Probabilistic error cancellation

Workshop / June 18, 2026 - July 02, 2026, 5:00 PM

Lectures on topics in applied quantum computing

Event details

Registration

Location

Language

Tabbed contents

Quantum-inspired optimization (June 18, 2026)

Quantum-inspired optimization

Tensor trains and their applications (June 25, 2026)

Tensor trains and their applications

A Primer on Quantum Error Mitigation (July 2, 2026)

A Primer on Quantum Error Mitigation

Contact Press / Media

Dr. Matthias Heller