Quantum-assisted training of neural networks
AssigneeLockheed Martin Corporation
InventorsSteven H. Adachi, Daniel M. Davenport, Maxwell P. Henderson
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AssigneeLockheed Martin Corporation
InventorsSteven H. Adachi, Daniel M. Davenport, Maxwell P. Henderson
Estimating the term…
Aspects of the disclosure provide a method for configuring a Quantum Annealing (QA) device. Then QA device has a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits. The method includes mapping a node of a neural network that have a plurality of nodes and connections between the nodes to a qubit in the QA device, and mapping a connection of the neural network to a coupler at an intersection in the QA device where two qubits corresponding to two nodes connected by the connection intersect. The method further includes mapping a node of the neural network to a chain of qubits. In an embodiment, a coupling between qubits in the chain is configured to be a ferromagnetic coupling in order to map the node of the neural network to the chain of qubits.
A method for configuring a Quantum Annealing (QA) device, the QA device having a plurality of qubits and a plurality of couplers at overlapping intersections of the qubits, the method comprising:
A method for training a neural network using a quantum annealing (QA) device including qubits configured with biases and couplers configured with weights, where an original restricted Boltzmann machine (RBM) of one layer of the neural network is mapped onto the QA device that is configured to act as a quantum RBM, the method comprising:
Dependencies are inferred from the claim text.