Research position in Quantum Hamiltonian Learning

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .

The centre has 40 project teams , 27 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

The successful candidate will join an active research environment in quantum information theory and will have considerable freedom to develop the project according to their interests. The position offers opportunities to interact with researchers working on quantum information, many-body physics, quantum algorithms, and related areas, and to develop collaborations with both theoretical and experimental groups.

Partners include:

• public with French National Research Agency (ANR), as part of the French HPC-quantum initiative [https://www.hqi.fr/]

Participation to conferences and workshops is anticipated and will be funded.

We are looking for a highly motivated researcher to work on Hamiltonian learning for interacting quantum systems, at the interface of quantum information theory, quantum metrology, and mathematical physics.

Research project

Hamiltonian learning aims to efficiently infer the parameters of an unknown Hamiltonian from observations of the corresponding quantum dynamics. Determining the optimal resources required for this task—such as the total evolution time or number of measurements needed to reach a prescribed accuracy—is a fundamental problem in quantum system identification.

Recent years have seen substantial progress in understanding optimal Hamiltonian learning for relatively simple quantum systems, including finite-dimensional systems and bosonic modes. Much less is known, however, for realistic interacting systems involving both matter and quantum fields.

The goal of this project is to develop a rigorous theory of Hamiltonian learning for interacting atom–field and spin–boson systems. A central objective will be to determine the attainable estimation precision and time complexity, and to design learning protocols that approach or saturate the Heisenberg limit.

Possible research directions include:

• deriving fundamental lower bounds on the time and sample complexity of Hamiltonian learning;

• designing protocols achieving optimal or near-optimal estimation rates;

• extending existing techniques for qudit and bosonic Hamiltonian learning to interacting spin–boson models;

• studying physically relevant atom–field Hamiltonians, including models from quantum optics and many-body physics;

• understanding the robustness of learning protocols to decoherence, imperfect control, and finite measurement precision.

The project is primarily theoretical, but is motivated by the characterization and calibration of increasingly complex quantum devices. Beyond Hamiltonian learning itself, it is expected to connect naturally with questions in quantum metrology, quantum information, open quantum systems, and complexity theory.

Activities:

Supervise PhD students

Produce literature review

Propose research questions

Contribute to the writing of research papers

Numerically test theoretical results on small instances

Present work progress on international conferences

Contribute to the discussions at HQI annual meetings

Candidates should have a strong background in theoretical physics, mathematics, computer science, or a related field. Depending on the precise direction of the project, experience in one or more of the following areas would be particularly useful:

• quantum information theory;

• mathematical physics;

• quantum optics or bosonic systems;

• many-body quantum systems;

• probability, statistics, or learning theory.

A strong interest in rigorous theoretical research is essential. Previous experience specifically with Hamiltonian learning is not required.

A good level of English will be required, the ability to speak in French is not necessary

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

In regards to professional experiences