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.
We show that NN parameter and hyperparameter cost landscapes can be generated as quantum states using a single quantum circuit and that these can be used for training and meta-training. Describes a method where a deep learning framework can be quantised by considering the two state form of a Bloch sphere/qubit and creating a quantum binary neural network. This paper proposes quantum amplitude amplification, a new algorithm for training and model selection in binary neural networks. Proposes a novel idea of outputting a quantum state that represents a complete cost landscape of all parameters for a given binary neural network, by constructing a quantum binary neural network (QBNN).