Now showing items 1-5 of 5

    • Adiabatic Quantum Simulation of Quantum Chemistry 

      Babbush, Ryan Joseph; Love, Peter; Aspuru-Guzik, Alan (Nature Publishing Group, 2014)
      We show how to apply the quantum adiabatic algorithm directly to the quantum computation of molecular properties. We describe a procedure to map electronic structure Hamiltonians to 2-body qubit Hamiltonians with a small ...
    • Bayesian network structure learning using quantum annealing 

      O’Gorman, B.; Babbush, Ryan Joseph; Perdomo-Ortiz, A.; Aspuru-Guzik, Alan; Smelyanskiy, V. (Springer Science + Business Media, 2015)
      We introduce a method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm. We do so by introducing an efficient reformulation of a standard posterior-probability scoring ...
    • Construction of Energy Functions for Lattice Heteropolymer Models: Efficient Encodings for Constraint Satisfaction Programming and Quantum Annealing 

      Babbush, Ryan Joseph; Perdomo-Ortiz, Alejandro; O'Gorman, Bryan Andrew; Macready, William; Aspuru-Guzik, Alan (Wiley-Blackwell, 2013-03-10)
      Optimization problems associated with the interaction of linked particles are at the heart of polymer science, protein folding and other important problems in the physical sciences. In this review we explain how to recast ...
    • Exploiting Locality in Quantum Computation for Quantum Chemistry 

      McClean, Jarrod Ryan; Babbush, Ryan Joseph; Love, Peter J.; Aspuru-Guzik, Alan (American Chemical Society (ACS), 2014)
      Accurate prediction of chemical and material properties from first principles quantum chemistry is a challenging task on traditional computers. Recent developments in quantum computation offer a route towards highly accurate ...
    • The theory of variational hybrid quantum-classical algorithms 

      McClean, Jarrod Ryan; Romero, Jonathan; Babbush, Ryan Joseph; Aspuru-Guzik, Alan (IOP Publishing, 2016)
      Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as 'the quantum variational eigensolver' ...