PhD Studentships in Theoretical Physics: theory of quantum information processing at Skolkovo Institute of Science and Technology, Laboratory for Quantum Information Processing
Recent developments in quantum information processing platforms have fostered a utilitarian means of quantum computation enabled by an iterative classical-to-quantum feedback process. This so-called “variational” approach to quantum computation, as introduced partly in [1, 2], was formally proven to represent a universal model of quantum computation in . Little is known about the ultimate capacity of the variational model as present tools are difficult to apply to this new framework. Interesting recent findings include the discovery of barren plateaus  and of reachability deficits  a recent connection between variational algorithms and contextually  and recent findings relating barren plateaus to circuit depth .
An exciting global research effort to understand the variational model is redefining the field of quantum computation. We welcome motivated PhD candidate(s) to take part in this endeavour. Those admitted will join a leading team working to address the following questions.
a. To understand the power of low-depth circuits, particularly the approximate trainability of the hardware efficient ansatz 
b. To develop a new means of active error mitigation for the variational model of quantum computation
c. To study existing variational quantum algorithms and to determine, in physical and mathematical terms, their inherent limitations and potential advantages for tasks such as machine learning  and quantum simulation
Students should be motivated to work on topics which will form the modern foundation of quantum computation.
Information about the laboratory and about Skoltech can be found at http://deepquantum.ai
General information about PhD admissions – programs, deadlines (2020 deadlines might change due to cover-19 so please apply ASAP), requirements, etc:
Online application link. Choose the centre for “Photonics and Quantum Materials” as the educational unit.
*Applicants are encouraged to apply before contacting their potential advisor
 A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik & Jeremy L. O’Brien
Nature Communications 5, 4213 (2014)
 A Quantum Approximate Optimization Algorithm
Edward Farhi, Jeffrey Goldstone, Sam Gutmann
MIT-CTP/4610 (2014) https://arxiv.org/abs/1411.4028
 Universal Variational Quantum Computation
 Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, Hartmut Neven
Nature Communications, Volume 9, Article Number: 4812 (2018)
 Reachability Deficits in Quantum Approximate Optimization
V. Akshay, H. Philathong, M.E.S. Morales, J. Biamonte
Physical Review Letters 104, 090504 (2020) arXiv:1906.11259
 Contextuality Test of the Nonclassicality of Variational Quantum Eigensolvers
William M. Kirby, Peter Love
Phys. Rev. Lett. 123, 200501 (2019)
 Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks
M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, Patrick J. Coles
 Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
A. Kandala et al.
Nature 549, 242 (2017)
 Quantum Machine Learning
Jacob Biamonte et al.
Nature 549, 195-202 (2017) 10.1038/nature23474