Numerical Scientist (Machine Learning/Quantum Physics)

Numerical Scientist (Machine Learning/Quantum Physics) The Skolkovo Institute of Science and Technology’s Laboratory for Quantum Information Processing is seeking a candidate familiar with computational physics.  We are particularly interested in candidates experienced in quantum physics and/or deep learning.    Experience in high-performance computing and parallel algorithms is a plus.  The start date is flexible. Requirements: The candidate will work as part of a team lead by Jacob Biamonte devoted to realizing national priority initiatives supporting the development of quantum computing.  The laboratory is focused on the theory and applications of quantum algorithms.  The candidate is expected to optimize and develop numerical solutions related to the emulation of quantum systems.  Additionally, the candidate should be able to[…]

Postdoctoral research​ position in the theory of​ modern quantum algorithms

Applications are invited for ​a postdoctoral research​ position in the theory of​ modern quantum algorithms. The position will be held at ​Skolkovo Institute of Science and Technology (​Skoltech), Moscow, Russia. ​The start date is flexible but we would like to fill these position(s) as soon as possible. The successful applicant will work in the research laboratory structure led by Prof Jacob Biamonte in the general area of quantum computation, with a focus on quantum enhanced algorithms and error mitigation on experimental devices. The overarching project goals include: Develop, optimize, and benchmark variational quantum algorithms (e.g. QAOA and VQE) Implement quantum information processing tasks using trapped ions and optical networks available[…]

Deep Physics MSc Projects — Skoltech MSc class of 2020 

We still have two openings to advise MSc students.   Deep learning and quantum computing. We have several ongoing and new projects to select from that apply deep learning to understand physics.  We also use statistical mechanics and quantum theory as a mathematical tool to understand e.g. the informatic properties of deep neural networks.  For example, quantum computers provide new classes of neural networks that appear to be exponentially difficult to simulate using classical computers; on going work is geared towards refining quantum deep learning algorithms.   Applications of deep learning to predict properties of quantum systems Enhancement of deep learning algorithms using real-world quantum computers and programable quantum simulators[…]