Introduction to Quantum Computing with Qiskit
This workshop is meant to introduce you to Qiskit as software for programming quantum computers on the cloud. Whether it is used as an educational tool or as a research one, Qiskit offers a broad range of modules, an extended documentation and several features for support- ing any person working in quantum computing.

Quantum Mechanics for Quantum Computing
Through examples and exercises to be discussed during the lectures, we will present selected topics in Quantum Mechanics in order to practice with the mathematical tools which characterize Quantum Computing, and to understand the physics behind it: the qubit, super- positions, multi-qubit entanglement, the unitary gates, the distribution of outcomes of measurements.

Introduction to Qiskit Machine Learning for Supervised Learning
The focus of this workshop is Qiskit Machine Learning, a new applica- tion module built on top of Qiskit’s existing functionality to create and run (parametrized) quantum circuits, evaluate complex observables, and automatically evaluate the corresponding gradients with respect to circuit parameters.

Quantum Kernels and Deep Learning
Kernel machines and deep networks are two successful approaches to machine learning that seemingly differ in the way of learning from data: while kernel methods aim to linearise the learning function, neural networks learn from data samples via multiple layers of nonlinear functions. This difference explains the difficulty in using quantum computing for neural networks, as quantum dynamics is purely linear. We discuss some of the recent approaches to quantum deep learning and the related problem of bounding the generalization error in the overparameterization regime.

The Application of Machine Learning on Quantum Computation
This talk will give an overview of current challenges and goals of machine learning-based activities related to the different layers of full- stack quantum computation. Covered topics include: quantum circuit characterization and clustering; quantum circuit transformation and optimization; quantum error correction; quantum circuit mapping and scheduling; hybrid and distributed quantum computation.

Seeking the Benefit of Quantum Kernels
Can quantum kernels be used effectively to improve prediction accuracy in real-world supervised and unsupervised learning tasks? A definitive answer has yet to be found. However, we can try several techniques known in the literature to empirically assess the performance of the machine learning models. We will implement them using the popular quantum computing framework Qiskit. Covered topics include: implementation of quantum kernels, projected quantum kernels, and training of parameterized quantum kernels.

Hybrid Quantum–Classical Computing
Hybrid quantum–classical computing is a paradigm in which a quan- tum computer works in tandem with a classical computer. The most common setting for this is the one in which the quantum computer executes a parameterized quantum circuit (sometimes referred to as a “quantum neural network”) and the classical computer is used to iteratively update the parameters in the circuit, with the goal being to prepare on the quantum computer a quantum state that minimizes a given cost function. The talk presents an overview of hybrid quantum– classical algorithms in this particular setting. Dott. Khatri will provide an overview of parameterized quantum circuits, show some examples of problems that can be solved using them, and discuss the issues of trainability (e.g., barren plateaus) and noise resilience when working with near-term quantum computers.

Cineca's Vision on HPC and QC
The emergence of Quantum Computing (QC) is revolutionizing the way we compute. In particular, hybrid algorithms, that combine parametric quantum circuits together with classical resources, could exploit the principles of quantum mechanics, like entanglement, to extend the capabilities of classical Machine Learning (ML). Cineca has always provided cutting-edge High Performance Computing (HPC) solutions and now it is paving the way for a radical paradigm shift in ML techniques that will lead to a profitable relationship between QC and HPC resources.

Playing with Qubits: 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. In quantum computing, data is processed using quantum bits, qubits, instead of classical bits. We will start by introducing the qubit and studying its properties. Then, we will look into simple two-qubit algorithms, before moving to general n qubit quantum computing.