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Graph Editing for Combinatorial Optimization Internship

Forum 'Stages' - Sujet créé le 04/12/2023 par Wesley Coelho (274 vues)


Le 04/12/2023 par Wesley Coelho :

About Pasqal

PASQAL designs and develops Quantum Processing Units (QPUs) and associated software tools. 
Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers; these cases can concern industrial application challenges as well as fundamental science needs. 
In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry.


Job Description

Graphs are used in a vast spectrum of fields. In particular, several combinatorial problems either are or can be defined on graphs and are of particular relevance for Quantum Computing. Among the quantum computing platforms, neutral atoms are particularly well suited to solving these combinatorial graph problems. In fact, the Ising Hamiltonian which governs the behavior of the qubits is closely related to the cost function to be minimized. 

Solving the problems is then equivalent to finding the ground state of the system, which can be achieved by adiabatic annealing. In recent years, the field of quantum computation has been focused on designing algorithms and hardware that can provide an advantage over classical computers. This advantage can take the form of more accurate results, a faster convergence, or even a lower energy consumption. These solutions are developed on very different platforms, using a wide range of technologies. One of the most prominent one is based on Rydberg neutral atoms, where an analog approach can be applied: as opposed to the case of digitalquantum computing, the quantum operations are not divided into discrete consecutive steps (gates) but are rather the result of a time-dependent control of the Hamiltonian acting upon the qubits.

In neutral-atom based quantum processors, atoms are trapped by lasers in a three-dimensional space, and the force of their two-by-two interaction can be used to define the presence or absence of an edge on the effective graph. 

In order to encode and solve problems on complex instances, we want to study graph editing processes. The aim is to study exact, approximate, and heuristic methods for transforming instances of complex graph classes into more QPU-friendly ones, while optimizing a given metric. We will then investigate the theoretical complexity of these methods, as well as their experimental effectiveness.

About you

You are in your last year of Master's in Operations Research or theoretical computer science and have the following assets: 

Hard skills: 

  • Experience with at least one of the following programing languages: C, C++, Python, Julia
  • Experience with linear and non-linear solvers such as Cplex, Baron, Gurobi, GLPK, IPOP
  • Strong taste for Applied Mathematics and graph theory, and a keen interest in deep tech and new technologies
  • Good practices in algorithms development
  • Report / documentation writing
  • English fluency

Soft skills: 

  • English fluency 
  • Autonomy 
  • Proactivity 
  • Curious 
  • Team spirit

What we offer

  • Beautiful brand new offices in Massy
  • Type of contract : 6-months internship
  • A dynamic and close-knit international team
  • A key role in a growing start-up
  • Free time to train and go to conferences/meetups

Recruitment process

  • A home assignment
  • A home assignment debrief and technical interview with our Hiring Managers
  • A 30-minute interview with one of our Talent Acquisition Specialist

If you think this position is for you, please send us your CV : https://careers.pasqal.com/jobs/3416369-graph-editing-for-combinatorial-optimization-internship 


PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, as inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, ethnicity, age, religion or sexual orientation.







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