Abstract: A method is disclosed for determining a conformation of a molecule on at least one degree of freedom to optimize according to at least one molecular objective function, the method comprising generating using at least one corresponding degree of freedom to optimize a connected rigid bodies representation for the molecule by identifying a plurality of groups of atoms, generating a data structure representative of the connected rigid bodies representation generating at least one neighborhood for each generated neighborhood of the at least one generated neighborhoods, generating a corresponding binary optimization problem using the data structure, providing the generated corresponding binary optimization problem to a high-performance binary optimizer, obtaining a solution from the high-performance binary optimizer; and providing at least one corresponding solution.

Abstract: A method is disclosed for determining a conformation of a molecule on at least one degree of freedom to optimize according to at least one molecular objective function, the method comprising generating using at least one corresponding degree of freedom to optimize a connected rigid bodies representation for the molecule by identifying a plurality of groups of atoms, generating a data structure representative of the connected rigid bodies representation generating at least one neighborhood for each generated neighborhood of the at least one generated neighborhoods, generating a corresponding binary optimization problem using the data structure, providing the generated corresponding binary optimization problem to a high-performance binary optimizer, obtaining a solution from the high-performance binary optimizer; and providing at least one corresponding solution.

Abstract: A method for solving a problem involving a hypergraph partitioning is disclosed, the method comprising receiving an indication of a problem involving a hypergraph partitioning; obtaining at least one property associated with a quadratic unconstrained binary optimization solver operatively coupled with the digital computer; formulating a partitioning problem of the hypergraph as an unconstrained binary optimization problem; reducing the unconstrained binary optimization problem into a quadratic unconstrained binary optimization problem; mapping the quadratic unconstrained binary optimization problem into the quadratic unconstrained binary optimization solver; obtaining from the quadratic unconstrained binary optimization solver at least one solution to the quadratic unconstrained binary optimization problem; applying a refinement procedure and translating the refined at least one solution to provide an indication of the partitioning and providing a solution to the problem.

Abstract: A method for solving a problem involving a hypergraph partitioning is disclosed, the method comprising receiving an indication of a problem involving a hypergraph partitioning; obtaining at least one property associated with a quadratic unconstrained binary optimization solver operatively coupled with the digital computer; formulating a partitioning problem of the hypergraph as an unconstrained binary optimization problem; reducing the unconstrained binary optimization problem into a quadratic unconstrained binary optimization problem; mapping the quadratic unconstrained binary optimization problem into the quadratic unconstrained binary optimization solver; obtaining from the quadratic unconstrained binary optimization solver at least one solution to the quadratic unconstrained binary optimization problem; applying a refinement procedure and translating the refined at least one solution to provide an indication of the partitioning and providing a solution to the problem.

Abstract: A method and system are disclosed for generating an embedding pattern used for solving a quadratic binary optimization problem using a quadratic solver characterized by an architecture. The method comprises obtaining an indication of a quadratic binary optimization problem; decomposing the quadratic binary optimization problem into a product of graphs representative of the input problem; selecting a graph of the product of graphs representative of the quadratic binary optimization problem; determining a nexus for embedding the selected graph of the product of graphs representative of the input problem in the architecture of the quadratic solver; determining a corresponding pattern for the nexus and buses to generate an embedding pattern and providing an indication of the embedding pattern.