Image Compression and Classification Using Qubits and Quantum Deep Learning

Mohsen, Ali; Tiwari, Mo

doi:10.48550/arxiv.2110.05476

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…Unfortunately, an understanding of how quantum-enhanced ML might be affected by practical considerations of data science workflows has been lacking in the research literature. In particular, existing work has often applied quantum-enhanced ML to canonical data sets such as Iris and MNIST [16,[27][28][29][30]. To our knowledge, no work has been done studying the implications of quantum-enhanced ML that would process streaming data.…”

Section: Extending Kernels To Process Streaming Datamentioning

confidence: 99%

Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning

Naveh¹,

Fitzgerald²,

Phan³

et al. 2021

Preprint

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Enhancing classical machine learning (ML) algorithms through quantum kernels is a rapidly growing research topic in quantum machine learning (QML). A key challenge in using kernels -both classical and quantum -is that ML workflows involve acquiring new observations, for which new kernel values need to be calculated. Transferring data back-and-forth between where the new observations are generated & a quantum computer incurs a time delay; this delay may exceed the timescales relevant for using the QML algorithm in the first place. In this work, we show quantum kernel matrices can be extended to incorporate new data using a classical (chordal-graph-based) matrix completion algorithm. The minimal sample complexity needed for perfect completion is dependent on matrix rank. We empirically show that (a) quantum kernel matrices can be completed using this algorithm when the minimal sample complexity is met, (b) the error of the completion degrades gracefully in the presence of finite-sampling noise, and (c) the rank of quantum kernel matrices depends weakly on the expressibility of the quantum feature map generating the kernel. Further, on a real-world, industriallyrelevant data set, the completion error behaves gracefully even when the minimal sample complexity is not reached.

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Section: Extending Kernels To Process Streaming Datamentioning

confidence: 99%

Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning

Naveh¹,

Fitzgerald²,

Phan³

et al. 2021

Preprint

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“…After that, following Shor algorithm, Grover provided an algorithm for database search in quantum domain 10 . The advantages of the quantum computer are given below 5,6,11,12 .…”

Section: Introductionmentioning

confidence: 99%

“…• Dramatically increase the speedup in case of cryptographic codes and internet base monetary transaction Limitations of quantum computer 5,6,11,12 are listed below.…”

Section: Introductionmentioning

confidence: 99%

Advanced quantum image representation and compression using a DCTEFRQI approach

Haque

Paul

Ulhoq

et al. 2022

Preprint

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In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is used to design the corresponding quantum image circuit. Due to the limitation of qubits, a total of 16 numbers of qubits is used to represent the coefficient and its position of the grayscale image. Among those, 8 number of qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio), and bit rate.

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Image Compression and Classification Using Qubits and Quantum Deep Learning

Cited by 2 publications

References 30 publications

Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning

Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning

Advanced quantum image representation and compression using a DCTEFRQI approach

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