Training and Meta-Training Binary Neural Networks with Quantum Computing
Abdulah Fawaz (Siemens);Sebastien Piat (Siemens);Paul Klein (Siemens);Simone Severini (University College London);Peter Mountney (Siemens);
Quantum computers promise significant advantages over classical computers for a number of different applications. We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. We demonstrate this explicitly for a binary neural network and, further, show how a quantum computer can train the network by manipulating this state using a well-known algorithm known as quantum amplitude amplification. We further show that with minor adaptation, this method can also represent the meta-loss landscape of a number of neural network architectures simultaneously. We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.
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