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Journal of Quantum Information Science

Quantum Image Searching Based on Probability Distributions

Fei Yan, Abdullah M. Iliyasu, Chastine Fatichah, Martin Leonard Tangel, Janet Pomares Betancourt, Fangyan Dong, Kaoru Hirota

Published Jan 1, 2012• 27 citations
Publisher

Abstract

A quantum image searching method is proposed based on the probability distributions of the readouts from the quantum measurements. It is achieved by using low computational resources which are only a single Hadamard gate combined with m + 1 quantum measurement operations. To validate the proposed method, a simulation experiment is used where the image with the highest similarity value of 0.93 to the particular test image is retrieved as the search result from 4 × 4 binary image database. The proposal provides a basic step for designing a search engine on quantum computing devices where the image in the database is retrieved based on its similarity to the test image.

DOI
10.4236/jqis.2012.23010

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A quantum image searching method is proposed based on the probability distributions of the readouts from the quantum measurements. It is achieved by using low computational resources which are only a single Hadamard gate combined with m + 1 quantum measurement operations. To validate the proposed method, a simulation experiment is used where the image with the highest similarity value of 0.93 to the particular test image is retrieved as the search result from 4 × 4 binary image database. The proposal provides a basic step for designing a search engine on quantum computing devices where the image in the database is retrieved based on its similarity to the test image.
A method to compare multiple pairs of quantum images in parallel is proposed, where the similarities of the images are estimated according to the probability distributions of the readouts from quantum measurements. The proposed method by means of a single Hadamard gate with control-conditions to transform the entire information encoding the quantum images in a strip, offers a significant speed-up in comparison to performing the same task on traditional computing devices. Three simulation experiments comprising of the comparison of two images, multiple pairs of images, and the sub-blocks from two images are implemented using MATLAB to demonstrate the feasibility and efficiency of the parallel comparison. The proposal advances a fundamental step towards image searching on quantum computers in which the image with the highest similarity to a particular reference image is retrieved as a search result from a database.
A method to analyze the similarity between two quantum images of the same size is proposed based on a representation for the quantum images. The similarity value is estimated according to the probability distribution of the results from quantum measurements. The proposed method is fast because a single operation can transform the entire information encoded in two images simultaneously. Two simulation-based experiments, which provide a reasonable estimation to the quantum images' similarity, are implemented using Matlab on a classical computer by means of linear algebra with complex vectors as quantum states and unitary matrices as unitary transformations. It also opens the door towards image searching from a database on quantum computers.
Quantum computation is a promising and rapidly growing field that has witnessed a series of significant breakthroughs in the last several decades.While some practitioners envision the realization of complete quantum computing devices, the research presented in the thesis focusses on the works that we can do with quantum computing hardware when they are realized.One of such areas is that of quantum image processing.A Flexible Representation of Quantum Images (FRQI) allows us to dedicate n-qubits and 1-qubit to capturing the information about the position and color of every pixel in the image, respectively.A method to compare multiple pairs of quantum images in parallel is proposed, where the similarities of the images are estimated according to the probability distributions of the readouts from quantum measurements.It offers a significant speed-up in comparison to performing the same task on traditional computing devices by means of a single Hadamard gate with control-conditions to transform the entire information encoding the quantum images in a strip.The simulation experiments comprising of the comparison of two images, multiple pairs of images, and the sub-blocks from two images are implemented to demonstrate the feasibility and efficiency of the parallel comparison.On the basis of such a comparison strategy, a quantum image searching method is presented where the image in a 4 × 4 binary image database with the highest similarity value to the particular test image is retrieved as the search result using low computational resources.To process the RGB-like quantum image, the representation for Multi-Channel Quantum Images (MCQI) is given, and a double-key, double-domain watermarking strategy for i
This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need100−150 ms100-150 ~\text{ms}100−150 ms. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.
Quantum computing is promising for image processing applications with its parallel processing capability. Today, studies are carried out to perform various image processing operations via quantum computing. In this study, a framework for window-based image processing is proposed. After encoding input images, the proposed framework keeps all the values in the relevant window in separate registers, depending on the window size. Window-based operations can be performed in parallel by applying Hadamard gate to the inputs and performing the related operations on the values in the window. The proposed framework is applied for image matching applications, which is an important branch of image processing. By comparing the searched pattern with the values in the window, it is checked whether it matches the searched pattern. Binary values are used to make the application more understandable.
Quantum computing is a rapidly developing field with faster computational capabilities than classical computing.The popularity of quantum computing has reached the field of image processing, particularly with a breakthrough method known as Quantum Hadamard Edge Detection.This approach represents a significant advancement in edge detection techniques using quantum computing.Quantum Hadamard Edge Detection is a method that can detect image edges more quickly than classical methods with exponential acceleration.This paper explains the Quantum Hadamard Edge Detection method in detail, including how it is implemented, a time complexity explanation, some experiments, and future research directions.Our experiments utilize a quantum computer simulator and employ four measurement metrics: Structural Similarity Index, Figure of Merit, Entropy, and a Proposed Metric with radius-based features, to detect simple binary images, MNIST images, and the Berkeley Segmentation datasets.We recognize the potential of quantum computing and believe that image processing with quantum representation will make processing more efficient and significantly valuable in the future.