Patent ID: 11924334
Assignee: GOOGLE LLC
Field: Computer technology (Electrical engineering)
Classification: CPC H  B  G | IPC B  G  H

Claim 14:
15. An apparatus comprising:
a quantum neural network implemented by one or more quantum processors; and
a classical processor in data communication with the quantum neural network;
wherein the apparatus is configured to perform operations for training the quantum neural network to perform a machine learning task, the operations comprising:
training the quantum neural network on multiple training examples, each training example comprising a machine learning task input paired with a known classification for the machine learning task input, the training comprising, for each training example:
encoding the machine learning task input into an initial quantum state of multiple qubits of an input quantum neural network layer;
processing the machine learning task input using one or more intermediate quantum neural network layers, wherein each intermediate quantum neural network layer comprises multiple quantum logic gates that operate on the multiple qubits and a target qubit that is also in the input quantum neural network layer, the processing comprising, for each intermediate quantum neural network layer and in a sequence, applying quantum logic gates for the intermediate quantum neural network layer to a current quantum state representing the multiple qubits and the target qubit;
measuring the target qubit in an output quantum neural network layer to obtain an output that represents a solution to the machine learning task, wherein the output comprises a measurement result that depends on an evolved quantum state of the multiple qubits and target qubit, wherein the evolved quantum state depends on the multiple quantum logic gates that operate on the multiple qubits and target qubit in each intermediate quantum neural network layer;
comparing the output to the known classification to determine one or more quantum logic gate parameter adjustments; and
adjusting values of quantum logic gate parameters from initial values to trained values according to the one or more quantum logic gate parameter adjustments.