公开(公告)号：US20250021855A1

公开(公告)日：2025-01-16

申请号：US18589534

申请日：2024-02-28

Applicant: Lockheed Martin Corporation

Inventor： Nicholas Sven Shorter ,  Joshua Adam Job ,  Steven Hiroshi Adachi ,  Julia Kwok ,  Danielle Marie Couger ,  Derek Keith Wise

IPC: G06N10/20

Abstract: According to an embodiment, a method includes removing a last feed forward layer from a pre-trained neural network. The method further includes appending the pre-trained neural network with a secondary last feed forward layer configured to output a plurality of parameters. The method further includes updating each one of the plurality of parameters by: 1) multiplying each one of the plurality of parameters by a first factor to produce a plurality of resultant parameters; and 2) adding a second term to each one of the plurality of resultant parameters, wherein both the first factor and the second term vary due to backpropagation. The method further includes using a loss function to determine the distance between a known class embedding and an input from an input dataset. Lastly, the method includes updating one or more weights associated with the number of variables in the pre-trained neural network.

公开(公告)号：US20250021856A1

公开(公告)日：2025-01-16

申请号：US18589568

申请日：2024-02-28

Applicant: Lockheed Martin Corporation

Inventor： Nicholas Sven Shorter ,  Joshua Adam Job ,  Steven Hiroshi Adachi ,  Julia Kwok ,  Danielle Marie Couger ,  Derek Keith Wise

IPC: G06N10/20

Abstract: According to an embodiment, a method includes receiving a pre-trained neural network and removing a last feed forward layer from the pre-trained neural network. The method further includes appending the pre-trained neural network with a secondary last feed forward layer, a quantum circuit, and another feed forward layer, and determining a number of measurements from the quantum circuit based on a plurality of qubits and a plurality of parameters output from the secondary last feed forward layer. The method further includes using a cross-entropy loss function to determine the difference between a probability distribution, output from the last feed forward layer, for a number of variables and a true value for each of the number of variables. Lastly, the method includes updating one or more weights associated with the number of variables in the pre-trained neural network through an optimizer.