US 12,229,632 B2
Systems and methods to generate samples for machine learning using quantum computing
William G. Macready, West Vancouver (CA); Firas Hamze, Vancouver (CA); Fabian A. Chudak, Vancouver (CA); Mani Ranjbar, Burnaby (CA); Jack R. Raymond, Vancouver (CA); and Jason T. Rolfe, Vancouver (CA)
Assigned to D-WAVE SYSTEMS INC., Burnaby (CA)
Filed by D-WAVE SYSTEMS INC., Burnaby (CA)
Filed on Sep. 24, 2020, as Appl. No. 17/030,576.
Application 17/030,576 is a division of application No. 15/452,438, filed on Mar. 7, 2017, granted, now 10,817,796.
Claims priority of provisional application 62/322,116, filed on Apr. 13, 2016.
Claims priority of provisional application 62/304,737, filed on Mar. 7, 2016.
Prior Publication US 2021/0019647 A1, Jan. 21, 2021
Int. Cl. G06N 10/00 (2022.01); G06F 18/2415 (2023.01); G06F 111/10 (2020.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)
CPC G06N 10/00 (2019.01) [G06F 18/2415 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06F 2111/10 (2020.01)] 16 Claims
OG exemplary drawing
 
1. A method of operation of a computational system comprising at least one processor, the method comprising:
receiving information defining a target distribution comprising a Boltzmann distribution and a sampling process by the at least one processor;
receiving a plurality of samples using the sampling process by the at least one processor;
generating a sampling distribution based on the plurality of samples by the at least one processor;
generating a post-processed distribution from the plurality of samples by the at least one processor, wherein generating the post-processed distribution includes:
determining a partition defining a first and a second subset of variables by a graphical model defined by the Boltzmann distribution, wherein the partition between the first and the second subset of variables is selected such that a conditional probability distribution of the second subset of variables can be marginalized and an induced sub-graph caused by the partition is defined with respect to the Boltzmann distribution; and
expressing the post-processed distribution in a mixture model comprising the sampling distribution with respect to the first subset of variables and an analytic form with respect to the second subset of variables, suitable for conditional resampling on the first and the second subset of variables;
evaluating a Kullback-Leibler (KL) divergence from the target distribution to the post-processed distribution by the at least one processor; and
comparing the sampling distribution to the target distribution based at least in part on the KL divergence by the at least one processor.