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In this paper, a neural networks model for quantum computer is proposed. The core of this model is quantum neuron. Firstly, the inner product of the input qubits and the weight qubits is mapped to the phase of the control qubits in the neuron by the swap test technology, and then these phases are obtained by the phase estimation method, which are further used as the phase of the output qubit in the neuron. In this way, the mapping of input qubits to output qubit in quantum neuron is completed. The quantum neurons mentioned above can be used to construct quantum neural networks. In this paper, the quantum circuit for each operation step are given. The simulation results on the classic computer verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• Lithofacies identification based on the shape characteristics of logging response. • Lithofacies identification method of using 2-dimensional convolution network. • Lithofacies identification method of using 1-dimensional convolution network and LSTM. To address lithofacies identification of single-well, we proposed two solutions based on logging response and convolutional neural network. Firstly, the lithofacies types are obtained according to the coring well data, and then the logging response characteristics of different lithofacies are obtained by associating the lithofacies with some logging responses. Finally, the mapping relationship between logging responses and lithofacies types is established by convolutional neural network. The trained network can be used for single-well lithofacies identification. In the first scheme, we employed a deep residual network based on transfer learning. This network receives three-dimensional image as input and use two-dimensional convolution to extract pattern characteristics. Hence, it needs to plot the logging responses of each lithofacies into an image in advance. In the second scheme, we employed a parallel model composed of residual network and long short-term memory network, which can directly receive the sequence data of logging response without transforming them into images. The lithofacies identification results of 2 test single-wells show that the identification accuracy of the two schemes is more than 92 % , which provides a new way for lithofacies identification in oil and gas exploration and development. [ABSTRACT FROM AUTHOR]

Abstract: To enhance the performance of classical neural networks, a quantum-inspired neural networks model based on the controlled-Hadamard gates is proposed. In this model, the inputs are discrete sequences described by a matrix where the number of rows is equal to the number of input nodes, and the number of columns is equal to the sequence length. This model includes three layers, in which the hidden layer consists of quantum neurons, and the output layer consists of classical neurons. The quantum neuron consists of the quantum rotation gates and the multi-qubits controlled-Hadamard gates. A learning algorithm is presented in detail according to the basic principles of quantum computation. The characteristics of input sequence can be effectively obtained from both breadth and depth. The experimental results show that, when the number of input nodes is closer to the sequence length, the proposed model is obviously superior to the BP neural networks. [Copyright &y& Elsevier]

Abstract: A novel quantum-inspired evolutionary algorithm is proposed based on the Bloch coordinates of quantum bits (qubits) in this paper. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The quantum chromosomes are updated by quantum rotation gates, and are mutated by quantum non-gates. For the rotation direction of quantum rotation gates, a simple determining method is proposed. For the rotation and mutation of qubits, two new operators are constructed based on Bloch coordinates of qubits. In this algorithm, the Bloch coordinates of each qubit are regarded as three paratactic genes, each chromosome contains three gene chains, and each gene chain represents an optimization solution, which can accelerate the convergence process for the same number of chromosomes. By two application examples of function extremum and neural network weights optimization, the simulation results show that the approach is superior to common quantum evolutionary algorithm and simple genetic algorithm in both search capability and optimization efficiency. [Copyright &y& Elsevier]

Abstract: When the Grover''s algorithm is applied to search an unordered database, the probability of getting correct results usually decreases with the increase of marked items. The reason for this phenomenon is analyzed in this Letter, the Grover iteration is studied, and a new phase matching is proposed. With application of the new phase matching, when the fraction of marked items is greater than 1/3, the probability of getting correct results is greater than 25/27 with only one Grover iteration. The validity of the new phase matching is verified by a search example. [Copyright &y& Elsevier]