Adaptive quantum networks We introduce a robust, error-tolerant adaptive training algorithm for generalized learning in high-dimensional, superposed quantum networks, or adaptive quantum networks. The formalized procedure applies standard backpropagation training to a coherent ensemble of discrete topological configurations of individual neural networks, each of which is formally merged under linear superposition. Quantum parallelism facilitates simultaneous training and revision within this coherent state space, resulting in accelerated convergence to optima. The protocol provides quantitative, numerical indicators for optimization of both single-neuron activation functions and reconfiguration of global network topology.

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