Speaker: Elisa Bäumer
Title: Introduction to Quantum Computing and Qiskit
The first part of this tutorial serves as an introduction to quantum computing. Starting with the basic principles of quantum computing, we learn how to mathematically describe quantum gates and build some simple algorithms. We will then continue with programming our first quantum circuits with Qiskit, a Python-based open-source toolkit to execute them on real quantum computers and learn how to handle noise on current hardware. This second part is supposed to be more hands-on, so bring your own laptop (and ideally already install Qiskit & jupyter notebooks).
Speaker: Hsin-Yuan (Robert) Huang
Title: Learning theory for quantum machines
In this tutorial, I will cover recent advances in developing learning theory for quantum machines. The tutorial will focus on the basic techniques for establishing prediction guarantees in quantum machine learning models and the fundamental ideas for proving the advantages of quantum machines over classical machines in learning from experiments.
Speaker: Sevag Gharibian
Title: Quantum algorithms – what’s quantum complexity theory got to do with it?
This tutorial gives a gentle introduction to the crucial interplay between quantum algorithms and quantum complexity theory, with an eye on developments in the Quantum Machine Learning sphere. We begin with basic complexity classes such as BQP, followed by the HHL algorithm for its complete problem, Matrix Inversion. We then discuss how the Quantum Singular Value Transform (QSVT) significantly generalizes HHL to general quantum algorithms for Linear Algebra. Finally, as time permits, we discuss the other side of the coin – what does it mean to “dequantize” algorithms like the QSVT, and when is it possible?