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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]

The vortex search is proposed as a new optimization algorithm recently. This algorithm has the advantages of simple operation and strong search capabilities. By introducing quantum computing into this algorithm, A quantum-inspired vortex search algorithm is presented in this paper. The initial population of the algorithm has only one individual called vortex center. First this individual is encoded by qubits described on the Bloch sphere, and then by repeatedly rotating all qubits on this individual about the same coordinate axis through random angles, some new individuals are generated. By choosing the best individual as a new vortex center, and rotating it again until meeting the termination conditions, the global optimal solution can be obtained. As the search in each dimension is carried out on the Bloch sphere, thus it is helpful to enhance the diversity of candidate solutions and inhibit premature convergence in the late stages of the algorithm. That the proposed algorithm is superior to the original one is demonstrated by the experimental results of some benchmark functions extreme optimization. [ABSTRACT FROM AUTHOR]

In this paper, we design a quantum associative classifier, which includes pattern storage and pattern classification. For pattern storage, we present a method to construct quantum superposition states with arbitrary number of basis states. The method employs three groups of qubits to encode the sequence number, category, and value of each sample respectively. Firstly, according to the total number of samples N to be stored, the number of sequence number qubits needed by associative classifier is calculated. Then, some quantum rotation gates and Hadamard gates are used to transform the sequence number qubits with initial state of | 0 ⟩ into the quantum equilibrium superposition state which contains exactly N basis states. Finally, according to the category and value of the sample to be stored, the remaining two groups of qubits with initial state of | 0 ⟩ are converted into corresponding quantum basis states by some controlled operations. For associative classification, we design a classification method based on quantum minimum search. Firstly, the Hamming distances between the input sample and all the stored samples are calculated, and then Grover’s algorithm with fixed phase is used to search the minimum value of these Hamming distances. At this time, the category of the stored sample corresponding to the minimum value is the category of the input sample. The specific classification results can be obtained through the measurement of quantum state in the register. The number of iterations of the search is exponentially lower than that of its classical counterpart, so that pattern classification can be realized quickly. [ABSTRACT FROM AUTHOR]

This paper presented a quantum representation and some basic operations for one-dimensional finite length digital signal. For the representation, similar to the existing flexible representation of quantum audio (FRQA), two sets of qubits are used to represent the amplitudes and positions of the signal, respectively, wherein the amplitude is represented by an (n + 1)-bit signed number in twos complement, including one sign bit, m integer bits and n − m fractional bits. The basic operations include the addition, subtraction, multiplication, division, and circular convolution of two digital signals. We designed the quantum circuits that implements these basic operations, analyzed the computational complexity, and verified the correctness of these circuits by simulations on classical computers. [ABSTRACT FROM AUTHOR]

In this paper, a novel quantum representation and watermarking scheme based on the controlled rotation of qubits are proposed. Firstly, a flexible representation for quantum color image (FRQCI) is proposed to facilitate the image processing tasks. Some basic image processing operations based on FRQCI representation are introduced. Then, a novel watermarking scheme for quantum images is presented. In our scheme, the carrier image is stored in the phase $$\theta $$ of a qubit; at the same time, the watermark image is embedded into the phase $$\phi $$ of a qubit, which will not affect the carrier image's visual effect. Before being embedded into the carrier image, the watermark image is scrambled to be seemingly meaningless using quantum circuits, which further ensures the security of the watermark image. All the operations mentioned above are implemented by the controlled rotation of qubits. The experimental results on the classical computer show that the proposed watermarking scheme has better visual quality under a higher embedding capacity and outperforms the existing schemes in the literature. [ABSTRACT FROM AUTHOR]

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]

Morphological image processing is a relatively mature image processing method in classical image processing. However, in quantum image processing, the related results are still quite scarce. In this paper, we first design the quantum circuits of the two basic operations of dilation and erosion for binary images and grayscale images. On this basis, for binary image, the quantum circuits of three morphological algorithms (noise removal, boundary extraction and skeleton extraction) are designed in detail. For grayscale image, the quantum circuits of three morphological algorithms (i.e., edge detection, image enhancement and texture segmentation) are also designed. In the design of these circuits, the parallelism of quantum computation is considered. The analysis of the circuits complexity shows that all the six morphological algorithms can speed up their classic counterparts. [ABSTRACT FROM AUTHOR]