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Portail > Offres > Offre UMR9012-CHRROB-005 - H/F / Post-doc sur l'Utilisation d'une machine-learning quantique: application à la physique nucléaire et la physique des particules expérimentale et théorique

H/F / Quantum machine learning for experimental and theoretical nuclear and particle physics

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : vendredi 1 octobre 2021

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General information

Reference : UMR9012-CHRROB-005
Workplace : ORSAY
Date of publication : Friday, September 10, 2021
Type of Contract : FTC Scientist
Contract Period : 24 months
Expected date of employment : 1 November 2021
Proportion of work : Full time
Remuneration : The amount of a Postdoc remuneration is fixed and depends on the date of hire
Desired level of education : Higher than 5-year university degree
Experience required : Indifferent


In recent years, we experienced the arrival of the first quantum computer (QC) demonstrators such as those developed by IBM or Google. Overall, this field is experiencing a new boost on a global scale. The programming on these prototypes of quantum computers remains today a challenge in particular concerning adapted algorithms and control of the quantum noise inherent to these computers. Nevertheless, in the next decade, these computers as well as the new associated algorithms may hypothetically provide computing power hitherto unequaled in terms of speed and storage.
In particular QC technology is expected to be competitive in the resolution of the type of high combinatorial complexity problems that are common in nuclear and particles physics or astrophysics. One field of QC which is evolving very fast, with an increasing number of applications, is Quantum Machine Learning (QML). The main goal of this project is to explore the future applications of quantum devices, and more specifically of QML in different areas of the IJCLab and LLR activities. Both laboratories are in the IN2P3 division of CNRS and also belongs to the P2IO Labex. More specifically, proof-of-principle applications will be provided, ranging from nuclear many-body problems to event recognition in multi-particle detectors.


We propose the following roadmap:

(i) As a first task, the postdoc will work to get an exhaustive view of the different techniques used in QML, e.g. HHL (Harrow, Hassidim, Lloyd), QAOA and QVE algorithms, with particular focus on the hybrid algorithms currently used on NISQ devices. In parallel to this review, she/he will work on the quantum computing formulation of specific nuclear and particle physics problems where QML can be applied that are nowadays treated on classical computers. For example: the optimization of many-body states in ab-initio calculations, the exploration of complex energy landscape for systems with large numbers of degrees of freedom, the classification of particle physics events in high-resolution multi-detectors.
(ii) In a second step, after different problems are correctly formulated in a quantum framework, the next challenge will be to make applications using either QC emulators or real quantum devices. The use of emulators will help to validate the whole strategy. Application on real quantum noisy device will add new aspects related to the use of NISQ computers. We forecast that the specific problems we are facing in nuclear and/or particle physics might lead to brand new approach in the QC context. A large part of the work will be to apply existing QML algorithms and/or develop new techniques that are suitable for the targeted applications.

(iii) In parallel to the tasks (i) and (ii), applications of the different algorithms will be made by the candidate on different subjects of interest for the group. The first applications that are anticipated are the event recognition, similar to image recognition problem, where ML is already well established as a useful tool. In this context, the applications and the algorithms development will be made in close collaboration with the LLR laboratory and will be tested on the Compact Muon Solenoid (CMS) detector data. The novel tools developed in the project, will then be applied to a wider class of subjects including the many-body problem.
The postdoc will work full time on the project.


The candidate should have skills in one or several of the following fields.
-Good knowledge in quantum mechanics
-quantum computing and quantum algorithms
-machine learning and/or quantum machine learning
-numerical methods and programming
-theoretical physics and many-body problem

Work Context

The retained person will work in the Phynet team of the Nuclear Physics Department of the IJCLab. The postdoc will also be co-supervised by the LLR laboratory (Andrea Sartirana), she/he will be integrated in the LLR IT group and work in interaction with the local CMS experiment group. Both laboratories belong to the French national nuclear and particle physics institute (IN2P3). IJClab also belongs to the Paris-Saclay University and LLR belongs to the “Institut Polytechnique de Paris”. The Phynet team has 13 members including PhD students and postdocs, the LLR IT group has 10 members.

Constraints and risks

There is no special risks or constraints.

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