Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by util...
详细信息
Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by utilizing the various geometric degrees of freedom of nanostructures, metasurfaces are expected to be useful for processingquantuminformation. Here, we propose and experimentally demonstrate a programmable metasurface capable of performing quantumalgorithms using both classical and quantum light with single photons. Our approach encodes multiple programmable quantumalgorithms and operations, such as Grover's search algorithm and the quantum Fourier transform, onto the same metalens array on a metasurface. A spatial light modulator selectively excites different sets of metalenses to carry out the quantumalgorithms, while the interference patterns captured by a single-photon camera are used to extract information about the output state at the selected output directions. Our programmable quantum metasurface approach holds promising potential as a cost-effective means of miniaturizing components for quantum computing and informationprocessing.
In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and ...
详细信息
In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the real world. In recent years, researchers have begun to focus on utilizing quantum computing to solve the multi-category classification problem. Some studies have shown that the process of processinginformation in the brain may be related to quantum phenomena, with different brain regions having neurons with different structures. Inspired by this, we design a quantum multi-category classifier model from this perspective for the first time. The model employs a heterogeneous population of quantum neural networks (QNNs) to simulate the cooperative work of multiple different brain regions. When processinginformation, these heterogeneous clusters of QNNs allow for simultaneous execution on different quantum computers, thus simulating the brain's ability to utilize multiple brain regions working in concert to maintain the robustness of the model. By setting the number of heterogeneous QNN clusters and parameterizing the number of stacks of unit layers in the quantum circuit, the model demonstrates excellent scalability in dealing with different types of data and different numbers of classes in the classification problem. Based on the attention mechanism of the brain, we integrate the processing results of heterogeneous QNN clusters to achieve high accuracy in classification. Finally, we conducted classification simulation experiments on different datasets. The results show that our method exhibits strong robustness and scalability. Among them, on different subsets of the MNIST dataset, its classification accuracy improves by up to about 5% compared to other quantum multiclassification algorithms. This result becomes the state-of-the-art simulation result for quantum classification models and exceeds the performance of class
Many problems intractable on classical devices could be solved by algorithms explicitly based on quantum mechanical laws, i.e. exploiting quantuminformationprocessing. As a result, increasing efforts from different ...
详细信息
As a branch of quantum image processing,quantum image scaling has been widely ***,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantu...
详细信息
As a branch of quantum image processing,quantum image scaling has been widely ***,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been *** this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral ***,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT ***,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic ***,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these *** simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.
To further enhance the study of quantum image edge extraction algorithms, this paper proposes a color quantum image edge extraction algorithm based on an order of color vectors. The algorithm utilizes the NCQI (novel ...
详细信息
To further enhance the study of quantum image edge extraction algorithms, this paper proposes a color quantum image edge extraction algorithm based on an order of color vectors. The algorithm utilizes the NCQI (novel quantum representation of color digital images) model, which allows for fine processing of the color information in the image simultaneously. Our scheme extracts the edges of the color quantum image through operations such as cyclic shifts, color vectors ordering, dilation and erosion, and presents a complete quantum circuit. Finally, the complexity of the complete quantum circuit is calculated based on the quantum modules used, and simulation experimental results on classical computers are provided. Compared to all classical digital image edge extraction algorithms, our scheme can achieve exponential acceleration.
quantuminformation science is an interdisciplinary subject spanning physics, mathematics, and computer science. It involves finding new ways to apply natural quantum-mechanical effects, particularly superposition and...
详细信息
quantuminformation science is an interdisciplinary subject spanning physics, mathematics, and computer science. It involves finding new ways to apply natural quantum-mechanical effects, particularly superposition and entanglement, to informationprocessing in an attempt to exceed the limits of traditional computing. In addition to promoting its mathematical and physical foundations, scientists and engineers have increasingly begun studying cross-disciplinary fields in quantuminformationprocessing, such as quantum machine learning, quantum neural networks, and quantum image processing (QIMP). Herein, we present an overview of QIMP consisting of a succinct review of state-of-the-art techniques along with a critical analysis of several key issues important for advancing the field. These issues include improving current models of quantum image representations, designing quantumalgorithms for solving sophisticated operations, and developing physical equipment and software architecture for capturing and manipulating quantum images. The future directions identified in this work will be of interest to researchers working toward the greater realization of QIMP-based technologies.
Based on decoupling representation of Pauli operators, we propose partially decoupled belief propagation (PDBP) and fully decoupled belief propagation (FDBP) decoding algorithm for quantum LDPC codes. These two algori...
详细信息
Based on decoupling representation of Pauli operators, we propose partially decoupled belief propagation (PDBP) and fully decoupled belief propagation (FDBP) decoding algorithm for quantum LDPC codes. These two algorithms can handle the correlations between the X and Z components of the vectors in symplectic representation, which are introduced by Pauli Y errors. Hence, they can apply not only to CSS codes, but also to non-CSS codes. For planar surface code and XZZX planar surface code, compared with traditional BP based on symplectic representation, the decoding accuracy of PDBP and FDBP is significantly improved in pure Y noise and depolarizing noise, especially that of FDBP. The impressive performance of FDBP might promote the practical implementation of quantum error correcting codes in engineering.
quantum phase estimation (QPE) is a fundamental tool in quantum computing, facilitating efficient simulations of complex problems in quantum chemistry and materials science. While most phase estimation algorithms are ...
详细信息
quantum phase estimation (QPE) is a fundamental tool in quantum computing, facilitating efficient simulations of complex problems in quantum chemistry and materials science. While most phase estimation algorithms are deterministic, recent advancements indicate that incorporating randomness can enhance performance. This study introduces a framework for randomized QPE that merges the benefits of randomized compilation with phase estimation algorithms based on quantum signal processing. Our proposed algorithms effectively reduce circuit depths by eliminating the need for precise Hamiltonian time evolution, making them advantageous for digital quantum computers estimating the eigenvalue and eigenvector properties of Hamiltonians. Notably, our findings show that the quantum stochastic drift protocol (qDRIFT)-based randomized algorithm surpasses the original phase estimation with qDRIFT, especially in scaling inverse failure probabilities. We also establish that a circuit depth of O(log(M)) suffices for estimating M distinct observables. The protocol is executed through multiple iterations of the randomized algorithms combined with classical shadow techniques. Overall, our framework retains many advantages of the randomized compilation technique, making it a compelling solution for challenges in quantum chemistry.
Given x, y on an unweighted undirected graph G, the goal of the pathfinding problem is to find an x-y path. In this work, we first construct a graph G based on welded trees and define a pathfinding problem in the adja...
详细信息
Given x, y on an unweighted undirected graph G, the goal of the pathfinding problem is to find an x-y path. In this work, we first construct a graph G based on welded trees and define a pathfinding problem in the adjacency list oracle O. Then we provide an efficient quantum algorithm to find an x-y path in the graph G. Finally, we prove that no classical algorithm can find an x-y path in subexponential time with high probability. The pathfinding problem is one of the fundamental graph-related problems. Our findings suggest that quantumalgorithms could potentially offer advantages in more types of graphs to solve the pathfinding problem.
Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. quantum image processing offers a promising alternative, but current implementations face significant ch...
详细信息
Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. quantum image processing offers a promising alternative, but current implementations face significant challenges, such as time-consuming data acquisition, complex device requirements, and limited real-time processing capabilities. This work presents a novel paired transform-based quantum representation for efficient image processing. This representation enables the parallelization of convolution operations, simplifies gradient calculations, and facilitates the processing of one-dimensional and two-dimensional signals. We demonstrate that our approach achieves improved processing speed compared to classical methods while maintaining comparable accuracy. The successful implementation of real-world images highlights the potential of this research for large-scale quantum image processing, architecture-specific optimizations, and applications beyond edge detection.
暂无评论