Precision modeling of applied quantum neural networks
Nikolay Raychev · 2020
We studied quantum correlations in continuous-variational quantum neural networks by introducing a common method for building neural networks of quantum computing and simulation development matrix density network of quantum variational scheme built into the architecture, which encodes a quantum continuous information of the amplitudes of the field. We use a linear model the variation quantum network, which classifies data in probability space. This network has a layered structure and quantum computing with continuous parameter values. Proposed model for the incorporation of classical quantum network, and specialized models kvantovai versions. We experimentally demonstrate new scheme of adiabatic quantum computation using the calculations inside Hamiltonian system spin and process control through external Hamiltonian directly caused by electromagnetic impulses. Compared with the conventional method, this approach is easier to implement and with higher precision