Corso: Quantum Computing and its Application to Machine Learning Ing. Carlo Mastroianni, Prof. Francesco Plastina

26 maggio (15:00-17:00)
27 maggio (10:00-13:00)
30 maggio (08:30-11:30)
31 maggio (09:00-11.00)
1° giugno (09:00-11:00)


Il corso sarà erogato in presenza presso l’aula seminari del Dimes (V piano, cubo 42C) e online al link:


https://teams.microsoft.com/l/team/19%3ap32dI6Qi_zSBeNBLYogqwylyfXIPptD8iu30s6ajOJ41%40thread.tacv2/conversations?groupId=95402c84-db1b-425f-9709-de3db0e94ea8&tenantId=7519d0cd-2106-47d9-adcb-320023abff57

“Quantum Computing and its Application to Machine Learning”; Carlo Mastroianni e Francesco Plastina

Abstract
The course will introduce the basic elements of quantum information and quantum computation. Starting from the physical fundaments (principle of superposition, unitary evolution, principle of measurement), the course will introduce the students first to basic quantum gates, and then to quantum algorithms and quantum circuits. The course will discuss why, how, and in which contexts, a significant “quantum speedup” can be obtained by using quantum instead of classical computation. The course will expose some of the most renowned quantum algorithms (Deutsch–Jozsa, Grover, etc.) and then will focus on the most recent applications of quantum computing to optimization and machine learning, with an eye to the applications fields for which both public and private companies are investing a huge amount of money: e-health, finance, etc.

The lectures will be complemented by lab sessions that will exploit the IBM Quantum Experience

To take full benefit from the course, the students will be invited to brush up their basic knowledge on complex numbers and linear algebra.

Content

The following topics will be covered by the course:

  • Physical fundaments of quantum computation
  • Definition, representation and measurement of qubits and quantum registers
  • Quantum gates and their representation as unitary operators
  • Basic quantum gates: bit flip (X or NOT), phase flip (Z), Hadamard (H), controlled-NOT
  • Quantum entanglement and Bell states
  • Quantum circuits
  • Renowned quantum algorithms: Deutsch–Jozsa, Grover
  • Sample use of Grover: solution of the Satisfiability problem
  • Hybrid (classical/quantum) algorithms and their use for optimization, machine learning and deep learning
  • QAOA algorithm for optimization
  • Sample uses of QAOA: MaxCut problem, Integer Linear Programming
  • Basics of Quantum Machine Learning and Quantum Neural Networks