Fundamental Algorithmic Research for Quantum Computing (FAR-QC)

Developing novel resource-efficient quantum algorithms to help realize the potential of quantum computing

The FAR-QC project is made possible by research and collaboration of DOE National Laboratories, Universities, and industry affiliates working together to advance quantum and classical capabilities in quantum simulation, optimization, and machine learning. For more information contact Ojas Parekh.

Achieving a Quantum Advantage

Quantum computing is unique among Beyond Moore’s Law computing contenders in that it leverages quantum mechanics to offer potentially exponential resource advantages over technologies relying only on classical physics. A few beacons, such as Shor’s famous quantum algorithm for integer factorization, suggest applications where quantum computing may offer a tremendous advantage.

Through rigorous asymptotic scaling analysis of these algorithms, the FAR-QC team is exploring and identifying scientific domains and problems for which quantum resources may offer significant advantages over classical counterparts, which is vital in realizing the potential of quantum computing. FAR-QC seeks to deliver quantum algorithms that offer provable asymptotic advantages over the best-known or best-possible classical counterparts.

Advancing Future Capabilities

FAR-QC will afford insights into unique advantages of quantum resources for shaping emerging and future quantum systems and applications.


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Sandia National Laboratories:

  • Parekh. Project Directory, SNL PI, Optimization Lead
  • Baczewski. Simulation Lead
  • Debusschere
  • Phillips
  • Rudinger
  • Sarovar
  • Thompson
  • Young

Oak Ridge National Laboratory

  • Ryan Bennink. Deputy Project Directory, ORNL PI and Machine Learning Director 
  • Hamilton
  • Hauck
  • Humble
  • Irle
  • Jakowski
  • Law
  • Powers

Berkeley National Laboratory

  • de Jong, LBNL PI
  • Buluç
  • Metcalf
  • Shapoval
  • Yang

Los Alamos National Laboratory

  • Somma, Theory & Practice Interface Lead, LANL PI
  • Subasi

Argonne National Laboratory

  • Larson, ANL PI
  • Leyffer

University of Maryland

  • Childs, UMD PI
  • Alagic
  • Davoudi
  • Gorshkov
  • K. Liu
  • Swingle
  • Wu


  • Whitfield, Dartmouth PI
  • Viola


  • Preskill, Caltech PI
  • Brandão


  • Hen, USC PI

Paderborn University

  • Gharibian, UPB PI

Centrum Wiskunde & Informatica

  • Buhrman, CWI/UA PI
  • de Wolf
  • Jeffery

University of Amsterdam

  • Schaffner
  • Walter


  • Jordan, Microsoft PI