US 12,468,976 B1
Probabilistic inference in machine learning using a quantum oracle
Nan Ding, Los Angeles, CA (US); Masoud Mohseni, Redondo Beach, CA (US); and Hartmut Neven, Malibu, CA (US)
Assigned to Google LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on May 27, 2021, as Appl. No. 17/332,606.
Application 17/332,606 is a continuation of application No. 16/413,273, filed on May 15, 2019, granted, now 11,030,548.
Application 16/413,273 is a continuation of application No. 14/484,039, filed on Sep. 11, 2014, granted, now 10,339,466, issued on Jul. 2, 2019.
Claims priority of provisional application 61/876,744, filed on Sep. 11, 2013.
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06N 7/01 (2023.01); G06N 10/60 (2022.01)
CPC G06N 20/00 (2019.01) [G06N 7/01 (2023.01); G06N 10/60 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method performed by a system of one or more computers for probabilistic inference in a model for use in machine learning, the model comprising interconnected units, the method comprising:
receiving data for training the model, the data comprising observed data for training and validating the model;
deriving input to a quantum information processor using the received data and a state of the model, wherein the input maps at least some interactions of different interconnected units of the model to interactions of qubits in the quantum information processor;
providing the input to the quantum information processor for learning part of the inference in the model; and
receiving, from the quantum information processor, data representing the learned inference.