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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]

Edge detection is a fundamental task in digital image processing. Marr–Hildreth edge detection is the basic tool for implementing edge detection in classic image processing. This paper studies the quantum version of the classical Marr–Hildreth edge detection, which includes two core processes: Gaussian–Laplacian filtering and zero-crossing extraction. Based on the sampling results of the Gaussian–Laplace function, Gaussian–Laplacian filtering is implemented directly using quantum multipliers and quantum adders. Zero-crossing extraction is achieved using several quantum comparators. The quantum circuits of these two core processes with several auxiliary operators are designed in detail. Complexity analysis shows that the quantum Marr–Hildreth edge detection has exponential speedup compared to its classical counterpart. The simulation on the classical computer verifies the correctness of the quantum Marr–Hildreth edge detection results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a quantum circuit for calculating the squared sum of the inner product of quantum states. The circuit is designed by the multi-qubits controlled-swapping gates, in which the initial state of each control qubit is | 0 〉 and they are in the equilibrium superposition state after passing through some Hadamard gates. Then, according to the control rules, each basis state in the superposition state controls the corresponding quantum states pair to swap. Finally, the Hadamard gates are applied to the control qubits again, and the squared sum of the inner product of many pairs of quantum states can be obtained simultaneously by measuring only one control qubit. We investigate the application of this method in quantum images matching on a classical computer, and the experimental results verify the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

To enhance the approximation and generalization ability of artificial neural networks (ANNs) by employing the principle of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state $$|1\rangle $$ in the target qubit. Then a quantum-inspired neural networks (QINN) is designed by employing the quantum-inspired neurons to the hidden layer and the common neurons to the output layer. The algorithm of QINN is derived by employing the Levenberg-Marquardt algorithm. Simulation results of some benchmark problems show that, under a certain condition, the QINN is obviously superior to the classical ANN. [ABSTRACT FROM AUTHOR]