Postdoctoral Researcher Position in Quantum Neuromorphic Computing with Superconducting Circuits(M/F)

4 Apr 2024
Job Information

Organisation/Company

CNRS

Department

Laboratoire Albert Fert

Research Field

Physics » Condensed matter properties
Physics » Solid state physics
Physics » Surface physics

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

24 Apr 2024 - 23:59 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

35

Offer Starting Date

1 Jun 2024

Is the job funded through the EU Research Framework Programme?

H2020 / ERC

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

We are seeking a highly motivated candidate to join our team in pioneering experiments in quantum neuromorphic computing using superconducting circuits. Quantum neuromorphic computing represents an approach that harnesses analog quantum systems to implement neural networks, leveraging their quantum properties to achieve more efficient learning [1-3]. Additionally, it offers a new tool for studying the evolution of open quantum systems.
In our team, we study various learning schemes with parametrically coupled quantum oscillators. Such quantum oscillators offer distinct advantages over traditional qubits, including a significantly larger Hilbert space for encoding neurons and the ability to learn parametric pump amplitudes as parameters within a neural network framework. Our ongoing investigations focus on exploring sources of neural nonlinearity, such as the strong Kerr effect and measurement [4]. Through simulations, we have already demonstrated the capability of parametrically coupled quantum oscillators to perform complex classification tasks requiring nonlinearity and memory, as well as their training using Gaussian boson sampling probabilities to analytically calculate gradients for gradient descent optimization [5].

References:

1. Fujii, K. & Nakajima, K. Harnessing disordered-ensemble quantum dynamics for machine learning. Phys Rev Appl 8, 024030 (2017).
2. Rudolph, M. S. et al, Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer. Phys. Rev. X, 12, 31010 (2022).
3. Huang, H. Y., Broughton, M., Cotler, J., Chen, S., Li, J., Mohseni, M., Neven, H., Babbush, R., Kueng, R., Preskill, J., & McClean, J. R. (2022). Quantum advantage in learning from experiments. Science, 376, 1182–1186.
4. Dudas, J. et al. Quantum reservoir neural network implementation on coherently coupled quantum oscillators. Npj Quant. Inf., 9, 64 (2023).
5. Marković, D. Physics for neuromorphic computing, APS March Meeting (2024).

The postdoctoral project focus is on the experimental implementation of this novel learning paradigm. It will involve conception, fabrication and measurement of superconducting circuits, and implementation of machine learning tasks.

The postdoctoral project is a part of the ERC project QDYNNET – Quantum Dynamical Neural Networks. The successful candidate will join the neuromorphic computing team at Laboratoire Albert Fert, CNRS, Thales, University Paris/Saclay. They will collaborate closely with two PhD students hired on the project, and researchers from companies Alice & Bob and Thales.

Requirements

Research Field

Physics

Education Level

PhD or equivalent

Research Field

Physics

Education Level

PhD or equivalent

Research Field

Physics

Education Level

PhD or equivalent

Languages

FRENCH

Level

Basic

Research Field

Physics » Condensed matter properties

Years of Research Experience

None

Research Field

Physics » Solid state physics

Years of Research Experience

None

Research Field

Physics » Surface physics

Years of Research Experience

None

Additional Information
Eligibility criteria

• A Ph.D. in Condensed Matter Physics, or a related field.
• Strong background in quantum computing, quantum information, or related areas.
• Experience with experimental work in superconducting circuits, cryogenics, microwave measurements, micro- and nano-fabrication.
• Proficiency in programming languages such as Python for data analysis and simulation.
• Excellent written and verbal communication skills, with the ability to work effectively within a collaborative research environment.

Website for additional job details

https://emploi.cnrs.fr/Offres/CDD/UMR137-DANMAR-003/Default.aspx

Work Location(s)

Number of offers available

1

Company/Institute

Laboratoire Albert Fert

Country

France

City

PALAISEAU

Geofield

Where to apply

Website

https://emploi.cnrs.fr/Candidat/Offre/UMR137-DANMAR-003/Candidater.aspx

Contact

City

PALAISEAU

Website

http://www.cnrs-thales.fr/

STATUS: EXPIRED