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

We are a cutting-edge research team dedicated to advancing quantum computing and graph machine learning (ML) methods. Our focus is on leveraging neutral atom quantum computers to solve complex problems in graph ML tasks. One of the most prominent technologies is based on Rydberg neutral atoms, where an analog approach can be applied: as opposed to the case of digital quantum 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. Classical data such as graphs can be naturally encoded in such quantum processors by trapping atoms with lasers in the form of a graph. The force of their interactions defines the presence or absence of an edge on an effective graph. With this approach, we have shown in [1] that quantum graph kernels can distinguish non-isomorphic graphs where many other classical graph kernels fail. Recently, we also demonstrated in [2] that quantum features can serve as positional encodings for graphs and probably enhance the performance of classical graph transformers.

We are looking for an intern to work on deep learning models applied to chemistry / drug discovery, exploring the potential of using neutral atom computers for machine learning powered drug discovery tasks (for instance, molecular dynamics, synthesis prediction, generative models, molecular docking, etc.). This project involves training a machine learning model enhanced by quantum features provided by a quantum computer (see [3]). The accepted candidate will collaborate with experts in quantum computing, quantum physics and graph and Quantum ML. The project goal is to extend the applicability of quantum-enhanced graph ML models to chemistry with potential provable benefits of the quantum approach. The accepted candidate will collaborate with experts in quantum computing, graph machine learning teams. The intern will learn to efficiently train ML models and understand the intricacies of quantum computing with neutral atom devices. 

[1] Henry, Louis-Paul, et al. "Quantum evolution kernel: Machine learning on graphs with programmable arrays of qubits." Physical Review A 104.3 (2021): 032416.

[2] Thabet, Slimane, et al. "Quantum Positional Encodings for Graph Neural Networks." arXiv preprint arXiv:2406.06547 (2024).

[3] D'Arcangelo, M., Henry, L. P., Henriet, L., Loco, D., Gouraud, N., Angebault, S., ... & Piquemal, J. P. (2024). Leveraging analog quantum computing with neutral atoms for solvent configuration prediction in drug discovery. Physical Review Research, 6(4), 043020.

About you

You are actively enrolled in a PhD program in a related program (i.e quantum physics, quantum computing, machine learning), and have the following assets: 

Hard skills: 

• Experience with programming in Python
• Strong interest in quantum computing, graph theory, and cutting-edge technologies
• Machine Learning models in frameworks such as PyTorch, JAX and g
• Experience with chemical informatics packages, e.g. RDKit and good knowledge in chemistry / material design / drug discovery
• Experience with at least one of the following: (quantum) machine learning or chemical or physics simulation or analog/digital quantum computing.
• Strong taste for Quantum Computing and a keen interest in deep tech and new technologies
• Excellent algorithm development, coding practices and numerical simulations
• Strong documentation and report writing skills
• Fluency in English (oral and written)

Preferred:

• Experience with at least one of the following: (Quantum) Machine Learning, computational chemistry (DFT, molecular dynamics, etc.)

• Experience simulating simple Quantum Spin Models

Bonus:

• Previous research experience – Masters thesis, previous Internship or similar
• Experience using quantum hardware

• Clear and simple verbal and scientific communication 
• Autonomy 
• Proactive
• Team player

What we offer

• Offices in Amsterdam, The Netherlands
• Type of contract: 6-months internship
• Start date: January 2025
• A dynamic and close-knit international team
• A key role in a growing start-up

Recruitment process

• An interview with our Talent Acquisition Specialist (~30 mins)
• A coding test to prepare at home (optional)
• An technical interview with the Hiring Manager (~45 mins)
• 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.