99248 - QUANTUM COMPUTING

Learning outcomes

At the end of the course students will acquire some fundamental knowledge on: - the theoretical framework for quantum information processing; - theory and applications of quantum programming; - models and methods of quantum machine learning. Students will be able to: - analyze quantum circuits and algorithms, hybrid quantum-classical protocols and quantum machine learning models; - use these tools to solve simple problems in fundamental and applied physics, also with the use of quantum emulators.

Course contents

The course is organized in three modules, which describe the theoretical fundamentals of quantum computing and quantum machine learning and introduce the students to applications via hands-on sessions.

• The first module (Prof. Calarco, 24 hours) starts with the fundamentals of quantum computing, focusing on universal digital computation. It then delves into more advanced topics, including quantum algorithms and error correction schemes.

  Topics

  - Basics of Quantum Mechanics for Computing.
  The qubits: states, evolution and measurements.
  Separability and entanglement.
  Applications to simple quantum information processing protocols.
  - Quantum circuits.
  Introductions to circuit based universal computers.
  Simple and universal quantum gates.
  Examples of simple algorithms.
  Non cloning theorem and classical computation.

  - Quantum algorithms.
  Examples of advanced algorithms (Quantum Fourier Transform; Quantum Search; Quantum Phase Estimation).
  Error Correction.
  
  

• The second Module (Prof. Ercolessi, 12 hours) presents the different quantum computing paradigms and contains hands-on sessions with the goal to learn the basics of two different Software Development Kits working on different physical platforms and emulators.

  Topics
  - Quantum computing paradigms.

  Simulations and computations.

  Digital and analog computers.

  Hybrid protocols

  - Hands-on with Qiskit.

  Simple circuits.

  Some examples of more advanced protocols.

  - Hands-on with Pulser

  Analog and Digital circuit with Rydberg atom platforms.

  Optimization problems and QUBO formulation.

• The third module (Prof. Bonacorsi, 28 hours) is an introduction to Quantum Machine Learning.

Topics
- Review of classical machine learning.
- Models and methods of quantum machine learning.
- Applications, via hands-on sessions.

Readings/Bibliography

• M.A. Nielsen and I.L. Chuang, Quantum Computation and Quantum information, Cambridge
• J. Preskill, Quantum information and Computation and Quantum, http://theory.caltech.edu/~preskill/

Slides of some lectures, notebooks of the hands-on sessions, and suggested additional readings can be found on the course page in Virtuale.

Teaching methods

The course is organized in three modules, which include both frontal lectures (all modules) and computing lab sessions (second and third modules). During the latter, students will be asked to bring their laptops and will be guided to develop, alone or in groups, methodologies and software for the implementation of quantum digital, analog, and machine learning protocols for different kinds of applications.

Assessment methods

The exam is divided into two parts: one for modules 1 and 2 and one for module 3.

Both parts can be taken in two separate modes, as described below.

Traditional oral exam, with questions on the course syllabus.

During the exam, students will be asked to introduce one or two topics chosen from the ones in the syllabus and:

1. Present and give arguments to prove the main results;
2. Discuss the implications, in terms of advantages/ disadvantages with respect to other methods, including classical ones.

   Oral presentation of a research paper: the topic can be chosen either from the suggested readings that will be posted in Virtuale or from other published research works. In any case, the choice needs to be discussed and agreed with Prof. Ercolessi (for modules 1 and 2) and Prof. Bonacorsi (for module 3).

   The presentation should address the following aspects:

3. research problem addressed,
4. used methodology,
5. results that can be achieved.

The final grade is the result of an average of the grades of the two parts, evaluated according to the following scheme:

Grade 18-19: basic knowledge and ability to analyze only a very limited number of topics covered in the course; overall correct language.

Grade 20-25: discrete knowledge and ability to analyze only a limited number of topics covered in the course; overall correct language.

Grade: 26-28: good knowledge and ability to analyze a large number of topics covered in the course; mastery of scientific language and correct use of specific terminology.

Grade: 29-30: comprehensive preparation on the topics covered in the course, showing a very good/excellent knowledge and ability analysis; mastery of scientific language and correct use of specific terminology.

The “cum laude” honor is granted to students who demonstrate the ability to organize comparative analyses and personal/critical rethinking of the subject.

Students with Specific Learning Disabilities (SLD) or temporary/permanent disabilities are advised to contact the University Office responsible in a timely manner (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ). The office will be responsible for proposing any necessary accommodations to the students concerned. These accommodations must be submitted to the instructor for approval at least 15 days in advance, and will be evaluated in light of the learning objectives of the course.

Office hours

See the website of Tommaso Calarco

See the website of Elisa Ercolessi

See the website of Daniele Bonacorsi