Quantum Neural Machine Learning - Backpropagation and Dynamics
This work addresses quantum computing and machine learning integration, but appears incremental as it builds on existing quantum neural network concepts with extensions to recurrent architectures.
The paper tackles quantum machine learning by developing Quantum Artificial Neural Networks with separate learning and backpropagation stages, where the network converges to specific quantum circuits and self-programs to solve computing problems, extending results to recurrent networks that interact with environments and exhibit noise-resilient dynamical records.
The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to solve computing problems. The results are extended to general architectures including recurrent networks that interact with an environment, coupling with it in the neural links' activation order, and self-organizing in a dynamical regime that intermixes patterns of dynamical stochasticity and persistent quasiperiodic dynamics, making emerge a form of noise resilient dynamical record.