Search

Quantum walks have been shown to be fruitful tools in analyzing the dynamic properties of quantum systems. This article proposes using quantum walks as an approach to quantum neural networks (QNNs). QNNs replace binary McCulloch-Pitts neurons with a qubit in order to use the advantages of quantum computing in neural networks. A quantum walk on the firing states of such a QNN is supposed to simulate the central properties of the dynamics of classical neural networks, such as associative memory. It is shown that a biased discrete Hadamard walk derived from the updating process of a biological neuron does not lead to a unitary walk. However, a stochastic quantum walk between the global firing states of a QNN can be constructed, and it is shown that it contains the feature of associative memory. The quantum contribution to the walk accounts for a modest speedup in some regimes. [ABSTRACT FROM AUTHOR]

We give an algorithm for prediction on a quantum computer which is based on a linear regression model with least-squares optimization. In contrast to related previous contributions suffering from the problem of reading out the optimal parameters of the fit, our scheme focuses on the machine-learning task of guessing the output corresponding to a new input given examples of data points. Furthermore, we adapt the algorithm to process nonsparse data matrices that can be represented by low-rank approximations, and significantly improve the dependency on its condition number. The prediction result can be accessed through a single-qubit measurement or used for further quantum information processing routines. The algorithm's runtime is logarithmic in the dimension of the input space provided the data is given as quantum information as an input to the routine. [ABSTRACT FROM AUTHOR]