Quantum Computing Analyst

 In Jobs

EDF Innovation Lab, Los Altos, CA

Quantum computing could contribute to climate change mitigation by helping to a better integration of Renewable Energy:

Do you want to be part of it?

Job Description

Job Title: Quantum computing analyst

Department: R&D Innovation Lab

Supervisor: Neha Nandakumar

Duration Co-op or internship (3 to 6 months).

Location: Los Altos, California

Application: Send a Resume/CV and Cover Letter to innovation-lab@edf-inc.com, indicate the Reference NN-2020-1

Job Summary:

The Renewable Energy integration challenge

EDF (Electricite de France) has been developing renewable energy projects for years in the US; the kind we need to tackle climate change challenges.

There are a lot of economic challenges linked to the location of these projects and the way they are integrated into the electric grid.

Much of the optimization involves high computation time, where the problem might not converge.

Quantum computing could provide better ways to solve these optimization problems accurately.

Exploring Quantum Computing

Quantum Computing (QC) is a new, very promising, computational paradigm.

Among the various fields of hopeful application of QC, optimization is a major area, alongside machine-learning, simulation of chemical systems, and many others.

Although many researches on quantum approaches for optimization have focused on discrete optimization problems [1][2][3][4][5][8][10][11][13], some works explore the potential advantages of quantum computing for continuous optimization problems likewise [6][9][7][12].

The problem above can be modelled as Mixed Integer Programming problems (MIP), involving both discrete and continuous decision variables.

The internship main goal will be to explore and test quantum algorithms and technologies that could be used to solve these class of MIP problems in much higher dimensions than the current classical methods used by EDF allow.

Resources

The internship will be co-advised by EDF’s experts of these problems and EDF’s experts in Computer Science, located in Palo Alto and Paris (France). The student will also take advantage of the current research program conducted by EDF on Quantum Computing, which includes PhDs on quantum algorithms for hard optimization problems.

The applicants should ideally master a background in quantum computing and mathematical optimization.

References

  • Tameem Albash, Daniel A. Lidar. Adiabatic Quantum Computing. arXiv:1611.04471v2 [quant-ph] 2 Feb 2018.
  • Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser. Quantum computation by adiabatic evolution. arXiv:quant-ph/0001106, January 2000.
  • Edward Farhi, Jeffrey Goldstone, Sam Gutmann, Joshua Lapan, Andrew Lundgren, and Daniel Preda. A quantum adiabatic evolution algorithm applied to instances of an NP-complete problem. Science, 292:5516, 2001
  • Edward Farhi, Jeffrey Goldstone. A Quantum Approximate Optimization Algorithm. arXiv:1411.4028v1. 2014
  • Grover, L. Fast quantum mechanical algorithm for database search. In Proceedings of the 28th Annual ACM Symposium on Theory of Computing, 1996, pp. 212 – 219
  • Aram W. Harrow, Avinatan Hassidim, Seth Lloyd. Quantum algorithm for solving linear systems of equations. arXiv:0811.3171v3. 2009
  • Pedro C. S. Lara, Renato Portugal, and Carlile Lavor. A New Hybrid Classical-Quantum Algorithm for Continuous Global Optimization Problems. arXiv:1301.4667v1 [math.OC]. (2013)
  • Andrew Lucas. Ising formulations of many NP problems. arXiv:1302.5843v3. 2014.
  • Protopopescu, J. Barhen. Solving a class of continuous global optimization problems using quantum algorithms. Physics Letters A 296 (2002) 9–14.
  • Eleanor G. Rieffel, Davide Venturelli, Bryan O’Gorman, Minh B. Do, Elicia Prystay, and Vadim N. Smelyanskiy. A case study in programming a quantum annealer for hard operational planning Problems. arXiv:1407.2887v1 [quant-ph] 10 Jul 2014.
  • Vadim N. Smelyanskiy, Eleanor G. Rieffel, and Sergey I. Knysh. A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration. arXiv:1204.2821v2 [quant-ph] 18 Apr 2012
  • Guillaume Verdon, Juan Miguel Arrazola, Kamil Brádler, and Nathan Killoran. A Quantum Approximate Optimization Algorithm for continuous problems. arXiv:1902.00409v1 [quant-ph] 1 Feb 2019
  • Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, and Mikhail D. Lukin. Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices. arXiv:1812.01041v1 [quant-ph] 3 Dec 2018

EDF Innovation Lab

EDF Innovation Lab (EDF IL)’s mission is twofold: first, to explore, analyze and research trends and disruptive technologies for EDF Group in North America, leveraging the innovation of Silicon Valley and building local partnerships; and second, to develop and test new markets, innovative businesses and services, to support EDF Group’s growth in decentralized, data-driven and low-carbon energy sectors.

A focus in the Lab is on new businesses around Energy Markets. This will be an exciting role for someone with strong data science skills and an interest in energy challenges. However no energy knowledge is a pre-requisite to apply. The analyst will be a part of the EDF IL team and will have easy access to all of the Lab resources including people to support her/his work.

Duties/Responsibilities:

  • The internship main goal will be to explore and test quantum algorithms and technologies related to the exposed challenge
  • Literature review
  • Algorithm development

Required Skills/Abilities:

  • The applicants should ideally master a background in quantum computing and mathematical optimization
  • Ability to structure and deliver analyses with minimal supervision
  • Ability to work in a multicultural environment

Education and Experience:

  • Having or pursuing a degree (Master or Undergraduate) in data science, or other relevant field
  • Experience and interest in quantum computing
  • Experience or training in energy appreciated but not required

Disclaimer:

  • May perform other duties assigned

 

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