The summer school is offered in a hybrid format.`The speakers will be present on-site`, so on-site participation is

highly encouraged to make the most out of the experience!

On-site places are limited and assigned on first come first served basis.

The courses can be attended remotely on the Microsoft Teams platform.

The Summer School will include laboratory activities, so remember to bring you own PC if you want follow the tutorials in real-time.

`Paolo Cremonesi`

Polytechnic University of Milan – Milan, Italy ↗️

##### A Gentle Introduction to Quantum Computing

The lecture introduces the basic concepts of quantum computing to a non-specialist audience. No prior knowledge of quantum mechanics will be assumed. We will start by introducing the two main architectures: quantum annealing and universal quantum gate model. Then, we will introduce the qubit and study its properties. Finally, we will look into simple algorithms.

`Zoe Holmes`

EPFL – Lausanne, Switzerland ↗️

##### Hybrid variational quantum algorithms: how to make them work

After recapping the basic idea behind variational quantum algorithms we will spell out some ingredients to make them work. These include the choice in cost function, expressibility and trainability. The latter we will focus on, introducing barren plateau phenomenon and exploring its causes.

`Aroosa Ijaz`

University of Waterloo – Canada ↗️

##### Machine learning with quantum software

In this introductory tutorial, we will explore how various quantum softwares are set up and how to code quantum machine learning models in them. More specifically, we will look at Pennylane and Tensorflow quantum in detail. For both softwares, we will explore their underlying structure, how data is embedded into quantum states, how variational quantum models are setup, how optimization and gradient routines are carried out and how machine learning problems can be expressed using quantum learning models.

`Luca Innocenti`

University of Palermo – Palermo, Italy ↗️

##### Introduction to quantum reservoir computing

Quantum reservoir computing is a quantum machine learning protocol that shows great promise for implementation in the near future using state-of-the-art technologies, and its simplicity allows it to be implemented across a broad range of experimental scenarios. The basic principle involves utilizing a complex, untrained evolution to scramble input data in such a way that target functions become more accessible in the readout stage. We will delve into the intricacies that arise when translating classical reservoir computing schemes to the quantum domain, together with potential solutions and future prospects.

`Guglielmo Mazzola`

University of Zurich, Switzerland ↗️

##### Thresholds for quantum advantage in sampling and optimization

Sampling and optimization are anticipated applications of quantum computers. In this lecture, I will introduce new algorithms for sampling and re-visit established ones under a practical perspective, i.e. considering the effect of the unavoidable quantum measurements noise or taking into account the gate frequency constraint in the future fault-tolerant regime.

As a pedagogical example, I will discuss cases of end-to-end quantum resource estimates in the realm of quantum finance, where one is especially interested in assessing the runtime to solve a typical, concrete problem, beyond traditional asymptotic scaling assessments.

`Minh Ha Quang`

RIKEN Center for Advanced Intelligence Project – Tokyo, Japan ↗️

##### An introduction to Kernel Methods and Deep Neural Networks

This lecture will give an introduction to the fundamental concepts and algorithms at the core of two major paradigms in Machine Learning, namely Kernel Methods and Deep Neural Networks (DNNs). In the first part, we will discuss the basic concepts and algorithms in Kernel Methods, including positive definite kernels, feature maps, the associated reproducing kernel Hilbert spaces (RKHS), Support Vector Machine, and approximate methods such as the random Fourier features.Basic concepts of Statistical Learning Theory will be covered, including generalization error and VC dimension, which lead to fully rigorous theoretical justifications for algorithms in this setting. In the second part, we will discuss basic concepts from DNNs, including the most common architectures and training algorithms, as well as some of the recent theoretical results on their mathematical foundations. We will also discuss the connection between the two paradigms of DNNs and Kernel Methods via the neural tangent kernel.

`Thanasilp Supanut`

Centre for Quantum Technologies, National University of Singapore ↗️

##### Introduction to kernel methods in quantum machine learning

This lecture delves into the concept of quantum kernels and their application in machine learning. By leveraging the principles of quantum mechanics, quantum kernels allow for the efficient and accurate handling of high-dimensional data sets. We will explore the mathematical foundations of quantum kernels and their potential applications in improving the performance of various machine learning algorithms, as well as their limitations. Additionally, we will discuss the current state of research in this field and the potential for quantum kernels to play a significant role in the future of machine learning.

`Michael Lubasch`

Cambridge Quantum Computing Ltd ↗️

##### Introduction to quantum algorithms for differential equations

For the solution of differential equations, quantum computers have an exponential advantage over classical computers. This lecture addresses the questions where does this quantum advantage come from and how can we make use of it using current and future quantum computers. With respect to current quantum devices, useful machine learning-related techniques are presented for solving differential equations on noisy quantum hardware.

`Massimiliano Incudini`

University of Verona, Italy ↗️

##### Introduction to the Qiskit Framework and its Application in Machine Learning

We will introduce Qiskit as the software of choice for programming quantum computers. Qiskit is a powerful platform that empowers users to define and create quantum circuits that can be utilized for machine learning and optimization purposes.

With Qiskit, users can define and create quantum circuits that are tailored to their needs and can be utilized for a variety of applications, including machine learning and optimization. As we move forward in the subsequent lectures, Qiskit will be an essential tool that we will rely on to explore the power of quantum computing. Its extensive documentation and sample codes make it an ideal companion that will enable you to grasp the concepts and techniques of quantum computing with ease.

We will also explore how Qiskit can be utilized to implement machine learning pipelines based on quantum kernel technique.

We take great care in gathering world renowned lecturers and experts in the field.

This year’s speakers will come from both `academia` and `industry`,

to explore both `theoretical` and `practical` aspects of real-world problems.