Thesis Code: 25001
Thesis Type: Master Thesis for Computer Science, Computer Engineering, Electronic Engineering, Physic Engineering, Quantum Engineering, Applied Mathematics

Research Area: Advanced Computing, Photonics & Electromagnetics

Description

Quantum Computing is transforming the landscape of computational problem-solving by leveraging the principles of quantum mechanics to perform tasks that are infeasible for classical computers. This master thesis explores various subtopics within the domain of quantum computing, providing a comprehensive analysis of its theoretical foundations, algorithmic developments, practical applications and low-level programming and emulation. The work’s focus can be chosen from several key areas, including quantum algorithms (such as Quantum Approximate Optimization Algorithm and Shor’s algorithm), quantum error correction, quantum hardware technologies (e.g., superconducting qubits and neutral atom systems), and domain-specific applications in optimization, machine learning and financial modelling. By addressing challenges and opportunities in each subfield, the thesis aims to contribute to the growing body of knowledge and offer innovative insights into harnessing quantum technologies for solving real-world problems.

The topics include, but are not limited at, the following:

  • Testing small-scale logical qubits: characterization of error correction/detection methodologies on superconducting machines, including LINKS’s IQM Spark superconducting computer
  • Neutral atoms compilation framework: development of an emulation framework for atom shuttling in a quantum register and/or related heuristics for digital machine
  • Near-term quantum algorithms for finance: development of quantum methodologies (e.g., Circuit knitting, reservoir computing, etc.) for a specific financial use cases (such as default prediction, portfolio optimization, option pricing etc.)
  • Quantum optimization: development and implementation of hybrid NISQ algorithms for different combinatorial optimization problems (such as graph coloring) using both digital and analogue quantum computers
  • Quantum Machine Learning: development and implementation of QML methods and algorithms (Quantum kernels, QNN, etc.) for different tasks (such as protein classification) using both digital and analogue quantum computers
  • Quantum Image Processing:  exploration and implementation of quantum image processing techniques  (e.g. Quantum Fourier Transform) for applications in astrophysics and cosmology

Requirements

  • MS students in Computer Science, Computer Engineering, Electronic Engineering, Physic Engineering, Quantum Engineering, Applied Mathematics
  • Experience with main programming languages (Python, C/C++), algorithms

Whenever possible and relevant, the work will include the implementation and execution on real quantum hardware (such as the IQM Spark acquired by LINKS and neutral atom platforms available on cloud). Moreover, the topic can be complemented/extended through a curricular internship period.

Contact: Send CV to giacomo.vitali@linksfoundation.com and chiara.vercellino@linksfoundation.com specifying the thesis code, title and specific topic(s) of interest.