Recent progress in machine learning has affected almost all areas of the modern economy. The use of quantum protocols to speed up classical machine learning approaches may have further profound effects on society in the future. Here, we developed a hybrid quantum-assisted self-organizing feature map, a type of artificial neural network, and apply it to the data clustering problem in an unsupervised manner. We show that it allows us to reduce the number of calculations in a number of clusters. It is believed that similar types of hybrid quantum classical algorithms can be the main test bed to achieve practical quantum supremacy on Noisy Intermediate Scale Quantum devices.
In the present work the generation of entanglement between the receiver and sender is investigated in the process of the quantum state transfer in the homogeneous one-dimensional chain of spins 1/2 with the XY -Hamiltonian in the approximation of the nearest neighbour interactions. Fidelity is obtained for a quantum state transfer for various numbers of spins and temperatures and values of initial polarisation of the sender’s spin. The transmitted pure state is encoded in the state of first spin (sender) in the initial time moment. The other spins are in the thermodynamic equilibrium state. The reduced density matrix for the receiver and the sender is obtained for an investigation of entanglement in the “sender-receiver” system. The effect of temperature and polarization of the transmitted state on the generation of entanglement in the system is also investigated.
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