公开(公告)号：US20240248947A1

公开(公告)日：2024-07-25

申请号：US18286624

申请日：2022-03-30

Applicant: D-WAVE SYSTEMS INC.

Inventor： Hossein Sadeghi Esfahani ,  Mohsen Rahmani ,  Alex Zucca ,  William W. Bernoudy

IPC: G06F17/11

CPC classification number: G06F17/11

Abstract: Systems and methods for optimization algorithms, updating samples, and penalizing constraint violations are discussed. A method for updating samples includes receiving a problem definition with an objective function and constraint functions, an initial sample, and a value for a progress parameter. For each variable a total energy change is determined based on an objective energy change based on the sample value for the variable and one or more terms of the objective function that include the variable and a constraint energy change based on the sample value for the variable and each of the constraint functions defined by the variable. A sampling distribution is selected based on the variable type and an updated value is sampled based on the total energy change and the progress parameter. An updated sample is returned with an updated value for each variable of the set of variables. Such may improve operation of processor-based systems.

公开(公告)号：US20230042979A1

公开(公告)日：2023-02-09

申请号：US17785188

申请日：2020-12-14

Applicant: D-WAVE SYSTEMS INC.

Inventor： Hossein Sadeghi Esfahani ,  William W. Bernoudy ,  Mohsen Rahmani

IPC: G06N10/60

Abstract: Methods for solving discrete quadratic models are described. The methods compute an energy of each state of each variable based on its interaction with other variables, exponential weights, and normalized probabilities proportional to the exponential weights. The energy of each variable is computed as a function of the magnitude of each variable and a current state of all other variables, exponential weights, the feasible region for each variable, and normalized probabilities, proportional to the exponential weights and respecting constraints. Methods executed via a hybrid computing system obtain two candidate values for each variable; constructs a Hamiltonian that uses a binary value to determine which candidate values each variable should take, then constructs a binary quadratic model based on the Hamiltonian. Samples from the binary quadratic model are obtained via a quantum processor. The methods can be applied to solve resource scheduling optimization problems and/or for side-chain optimization for proteins.