QUBO Embedding Optimization for Quantum Algorithms Internship

Job title:

QUBO Embedding Optimization for Quantum Algorithms Internship

Company

Pasqal

Job description

About PasqalPASQAL 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 DescriptionAs part of our strong growth, we are recruiting an intern to work on the implementation and optimization of a pre-existing algorithm designed to handle Quadratic Unconstrained Binary Optimization (QUBO) problems that do not align with the constraints of a specific Quantum Processing Unit (QPU).The initial focus will be on translating the theoretical formulation into a functional implementation, followed by rigorous benchmarking across a variety of QUBO cases to understand where the algorithm performs or fails. Subsequently, the internship will emphasize the optimization of the algorithm to enhance overall efficiency, reduce computational overhead, and fully leverage the quantum-enhanced optimization potential of the QPU.
If time permits, the scope of the internship might potentially be extended to design and explore alternatives and potentially more efficient approaches. This could involve the design and evaluation of new methodologies aimed at improving overall performance and adapting to evolving QPU specifications.The tasks of this internship will be as follows:

• Analyze the initial version of the algorithm and evaluate its current performance
• Implement the initial algorithm and test its effectiveness on benchmark QUBOs
• Optimize the algorithm by improving workflow efficiency, reducing computational overhead, and enhancing solution quality
• Explore and test advanced optimization methods to further improve the current strategy or propose new approaches

About youYou are a Master’s student on Operations Research or Theoretical computer science and have the following skills:

• Experience with Python language
• Strong taste for Applied Mathematics and algorithmics, and a keen interest in deep tech and new technologies
• Good practices in algorithms development
• Report / documentation writing
• English fluency

Notions of quantum computing are not mandatory but highly appreciated.What we offer

• Brand new offices in Palaiseau, France
• Type of contract: internship
• A dynamic and close-knit international team
• A key role in a growing start-up

Recruitment process

• An interview with our Talent Acquisition Specialist of 30′.
• A technical test
• An interview with the Hiring Manager of 45′.
• An offer!

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.About us:

• Full stack, Neutral Atom, Quantum Technology, global leader
• Enabled by Nobel Prize-winning technology
• French Tech 2030 Laureate

• Growing global team made of Quantum experts and scientists, quantum software and hardware engineers and deeptech enthusiasts
• In direct competition with the world’s biggest players (Google – Microsoft – IBM – Amazon)
• Privately owned
• 140 million Euros raised (equity) to date

Our business:

• PASQAL designs quantum processors (QPUs: Quantum Processing Units), manufactures the hardware, and develops the associated software
• QPUs are highly energy-efficient which will help to significantly reduce the carbon footprint of the HPC industry
• We manage use cases for our customers currently beyond the reach of the most powerful supercomputers

Expected salary

Location

Palaiseau, Essonne

Job date

Mon, 25 Nov 2024 00:01:38 GMT

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