Correlation Property of Multipartite Quantum Image

Sanchez, M. A. Mercado; Sun, Guo Hua; Dong, Shi Hai

doi:10.1007/s10773-019-04247-9

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…In general, we measure the advantages of quantum image processing algorithms by comparing their time complexity with traditional image processing algorithms, but this is often not enough. It is more appropriate to explore an accurate and comprehensive way to show the advantages of QIP and to make a comprehensive comparison between the process and result [66][67][68][69]. In the future, quantum image processing algorithms are used to develop quantum algorithms related to areas such as computer vision, automatic detection, and manufacturing, resulting in a range of basic or advanced image processing algorithms.…”

Section: B Research On the Methods Of Qip Advantage Displaymentioning

confidence: 99%

A New Trend of Quantum Image Representations

Guo

Liu

et al. 2020

IEEE Access

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Quantum image processing will be an important issue in the age of quantum computers. Quantum image processing generally includes quantum image representations, quantum image processing algorithms, and quantum image measurement. Among them, quantum image representation is a very important part of quantum image processing. Therefore, it is of great significance for quantum image processing to study the representation of quantum images. First, we elaborate on the main quantum image representations and analyze, compare and summarize them. This paper focuses on the definition of these quantum image representations the preparation of quantum images and the number of quantum bits required as well as the computational complexity. Then, this paper analyzes, compares and summarizes the similarities and differences between these quantum image representations. Finally, the challenges and future development in the field of quantum image processing are summarized and forecasted. This work will help researchers to understand the advances related to quantum image representations in the field of quantum image processing and is therefore of some reference value.

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Section: B Research On the Methods Of Qip Advantage Displaymentioning

confidence: 99%

A New Trend of Quantum Image Representations

Guo

Liu

et al. 2020

IEEE Access

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“…Encoding of complex image datasets has been studied extensively [22][23][24], as well as using QRAM as a future prospect for algorithm implementation (QRAM currently hasn't been physically realized) [38,39]. Generally speaking, for any arbitrarily large or complicated dataset, exp(n) operations would be necessary to encode the information into n qubits.…”

Section: Data For Simulationsmentioning

confidence: 99%

“…Our simulations demonstrate nearlossless compression of quantum data using an image dataset, where each image is encoded as a unique superposition state, using a scheme similar to [21], described in section 3. Complex datasets incorporating color pixels have been encoded to qubits using flexible representation of quantum images (FRQI) [22][23][24]. In general, when using simulators, complex data sets can be encoded using translation transforms [22], or using enhanced data loading, which encode feature vectors that characterize a model [13].…”

Section: Introductionmentioning

confidence: 99%

“…Complex datasets incorporating color pixels have been encoded to qubits using flexible representation of quantum images (FRQI) [22][23][24]. In general, when using simulators, complex data sets can be encoded using translation transforms [22], or using enhanced data loading, which encode feature vectors that characterize a model [13]. These data encoding procedures rely on the ideal conditions provided by simulators.…”

Section: Introductionmentioning

confidence: 99%

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Information loss and run time from practical application of quantum data compression

et al. 2023

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We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBM’s Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with densitymatrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for training and compressing our data on quantum devices, and relate trainability to specific characteristics and performance metrics of our parametric quantum circuits.

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