Stage M2 (Recherche) (ou 3A) à Télécom SudParis - Quantum Computing for Combinatorial Optimization

Title: Quantum Computing for Solving Combinatorial Optimization Problems

Context and Objectives:
In this project, we focus on using the Quantum Computing (QC) for solving some classes of the combinatorial optimization problems. Indeed, Quantum Computing has emerged as a promising computational paradigm capable of addressing classes of problems that are intractable for classical computers. Among its potential applications, the use of quantum algorithms for solving combinatorial optimization problems has attracted considerable attention in recent years. These problems are ubiquitous in fields such as logistics, scheduling, and network design, and are often NP-hard, making them difficult to solve efficiently using classical methods.
Despite significant progress, the applicability of quantum computing to the real-world optimization problems remains limited by several factors, including noise, modeling complexity, etc. 
This internship aims to contribute to this growing body of research by investigating how quantum computing techniques can be tailored and integrated to solve some representative combinatorial optimization problems that frequently arise as a sub-problem in various applications (e.g., facility layout, logistics, and communication network design).

Potential PhD Thesis: Successful accomplishment of this internship can lead to a PhD thesis. 
Opportunities to continue with a PhD thesis on the same or other topics could be discussed with the interested applicants.

Research steps: 

• State-of-the-art literature review.
• Understanding and identifying the combinatorial optimization problems.
• Reformulating the selected combinatorial optimization problems. 
• Algorithmic Development: Adaptation, development, and implementation of quantum algorithms. 
• Experimental Evaluation: Conducting computational experiments to evaluate and compare the performance of the proposed algorithms in terms of solution quality, scalability, and computational efficiency.
• Preparing and writing a final report (in preference in English) as well as a presentation file (in preference in English). 

Required skills:
We are looking for a final-year engineering or master's student having the following characteristics:

• Student in computer science, applied mathematics, or similar domains.
• Strong background in Operations Research. 
• Familiarity with quantum computing techniques and algorithms is considered as an advantage, but it is not mandatory. 
• Excellent programming skills in Python.
• Familiarity with a MIP solver, e.g., Gurobi, or Hexaly.
• Holding effective teamwork skills.
• Excellent communication and writing skills in English. 

Supervisor:

• Mahdi MOEINI (mahdi.moeini(@)ensiie.fr), Associate professor in Computer Science (Operations Research) at the ensIIE and affiliated to the research lab. SAMOVAR of the Télécom SudParis, Institut Polytechnique de Paris (IP Paris). 

Complementary information:

• Dates: The starting date of the internship is flexible, but it should not be later than March 1, 2026. 
• Salary: Standard internship salary in France, for a duration of 6 months.
• Location: The research lab. SAMOVAR of the Télécom SudParis, Institut Polytechnique de Paris (IP Paris). It is located in Évry, which is located at 20km distance from Paris, reachable by RER D, etc.

How to apply:
Please send the following documents as a single pdf file, as soon as possible to the indicated e-mail addresses:

• Motivation letter (at most one page).
• Detailed Curriculum Vitae (maximum 2 pages)
• Academic transcripts 
• Contact information of two academic references

Application Deadline: the selection will be made either in December 2025, or in January 2026. 

Contacts:

• Mahdi MOEINI, Associate professor (https://sites.google.com/view/mahdi-moeini) at the ensIIE et affiliated to the research lab. SAMOVAR of the Télécom SudParis, Institut Polytechnique de Paris (IP Paris). 
  - (mahdi.moeini(@)ensiie.fr)
  - (moeini.mahdi(@)gmail.com)

If you require any further information, please don’t hesitate to contact me.

Some references:

[1] Nicolas Borrajo, Juan Marcos Ramirez, Farzam Nosrati, Jose Aguilar, Vincenzo Mancuso and Antonio Fernandez. New QUBO Transformations to Improve Quantum and Simulated Annealing Performance for Quadratic Knapsack. GECCO’25 Companion, July 14–18, 2025, Malaga, Spain, 203–206, 2025.

[2] Taku Mikuriya, Shintaro Fujiwara, Kein Yukiyoshi, Giuseppe Thadeu Freitas de Abreu, and Naoki Ishikawa. Grover Adaptive Search for the Higher-Order Formulation of Quadratic Assignment Problems. https://arxiv.org/pdf/2410.12181, October 10, 2025.

[3] Mahdi Moeini, Do Thanh Dat Le. Multiple Traveling Salesman Problem with a Drone Station: Using Multi-Package Payload Compartments. Recent Challenges in Intelligent Information and Database Systems, Communications in Computer and Information Science (CCIS), Vol. 2144: 226-237, Springer, 2024.

[4] Daniel Schermer, Mahdi Moeini, and Oliver Wendt. The traveling salesman drone station location problem. Advances in Intelligent Systems and Computing, Vol. 991: 1129–1138, 2019.

[5] Thomas G. Wong. Introduction to Classical and Quantum Computing. Rooted Grove, Omaha, Nebraska, 2022.