… of training should be treated in detail. We propose a simple quantum neural network and a
training … It can be shown that this algorithm works in quantum systems. Results on several real-…

… a training algorithm for this quantum neural network that is efficient in the sense that it only
depends on the width of the individual layers and not on the depth of the network. … network has …

… intermediate scale quantum devices involve training a parameterized quantum circuit with a
… Such hybrid quantum-classical algorithms are popular for applications in quantum simulation…

… To support the promising training performance of the quantum neural network, we also train
… We find that the quantum neural network tangibly demonstrates faster training; however, the …

… quantum computing [24], Open Quantum Neural Network models would make use of dissipation
in order to obtain dynamical properties similar to neural … can be trained by the quantum …

… We train the quantum Hopfield net using simultaneous recurrent … of the quantum neural
network to do a logic gate. We specify as inputs the initial (t=0) state of a single quantum dot …

… When we have a quantum neural network model, we wish to train it and apply it to classification
tasks. In most cases, we need to first formalize the task to be an optimization problem. …

… After combining the concepts of fuzzy and ANN, author [65] gave the concept of quantum
neural network with the detailed training algorithm. QNN gives better diagnosis accuracy in this …

… Our aim will be to train a classical optimizer neural network to learn parameter update
heuristics for optimizee quantum neural networks. As mentioned previously, for our QNN’s of …

… quantum training by Grover adaptive search as a baseline prior to our quantum training …
potential of our quantum training to a variety of application including training VQE, learning …