Efficient quantum image representation and compression circuit using zero-discarded state preparation approach

• URL: http://arxiv.org/abs/2306.12634v1
• Date: Thu, 22 Jun 2023 02:18:56 GMT
• Title: Efficient quantum image representation and compression circuit using zero-discarded state preparation approach
• Authors: Md Ershadul Haque, Manoranjan Paul, Anwaar Ulhaq, Tanmoy Debnath
• Abstract summary: A novel zero-discarded state connection novel enhance quantum representation (ZSCNEQR) is introduced to reduce complexity further. The proposed method requires 11.76% less qubits compared to the recent existing method.
• Score: 9.653976364051564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum image computing draws a lot of attention due to storing and processing image data faster than classical. With increasing the image size, the number of connections also increases, leading to the circuit complex. Therefore, efficient quantum image representation and compression issues are still challenging. The encoding of images for representation and compression in quantum systems is different from classical ones. In quantum, encoding of position is more concerned which is the major difference from the classical. In this paper, a novel zero-discarded state connection novel enhance quantum representation (ZSCNEQR) approach is introduced to reduce complexity further by discarding '0' in the location representation information. In the control operational gate, only input '1' contribute to its output thus, discarding zero makes the proposed ZSCNEQR circuit more efficient. The proposed ZSCNEQR approach significantly reduced the required bit for both representation and compression. The proposed method requires 11.76\% less qubits compared to the recent existing method. The results show that the proposed approach is highly effective for representing and compressing images compared to the two relevant existing methods in terms of rate-distortion performance.

Related papers

• A Fast Quantum Image Compression Algorithm based on Taylor Expansion [0.0]
  In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86% reduction in the number of iterations.
  arXiv  Detail & Related papers  (2025-02-15T06:03:49Z)
• PALQA: A Novel Parameterized Position-Aware Lossy Quantum Autoencoder using LSB Control Qubit for Efficient Image Compression [7.340017786387768]
  This work introduces a novel parameterized position-aware lossy quantum autoencoder (PALQA) circuit that utilizes the least significant bit control qubit for image compression. The PALQA circuit demonstrates superior performance in terms of the number of gates required and PSNR metrics.
  arXiv  Detail & Related papers  (2025-02-04T10:08:02Z)
• Hybrid quantum transfer learning for crack image classification on NISQ hardware [62.997667081978825]
  We present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
  arXiv  Detail & Related papers  (2023-07-31T14:45:29Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Block-wise quantum grayscale image representation and compression scheme using state connection [9.653976364051564]
  A novel SCMNEQR approach has been proposed that uses fewer qubits to map the arbitrary size of the grayscale image. The experimental results show that the proposed approach outperforms the existing methods in terms of compression.
  arXiv  Detail & Related papers  (2022-12-19T03:17:53Z)
• A novel state connection strategy for quantum computing to represent and compress digital images [10.20554144865699]
  We propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits. Unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.
  arXiv  Detail & Related papers  (2022-12-14T08:10:40Z)
• Advance quantum image representation and compression using DCTEFRQI approach [0.5735035463793007]
  We have proposed a DCTEFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently. The objective of this work is to represent and compress various gray image size in quantum computer using DCT(Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approach together.
  arXiv  Detail & Related papers  (2022-08-30T13:54:09Z)
• A hybrid quantum image edge detector for the NISQ era [62.997667081978825]
  We propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era.
  arXiv  Detail & Related papers  (2022-03-22T22:02:09Z)
• Implicit Neural Representations for Image Compression [103.78615661013623]
  Implicit Neural Representations (INRs) have gained attention as a novel and effective representation for various data types. We propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. We find that our approach to source compression with INRs vastly outperforms similar prior work.
  arXiv  Detail & Related papers  (2021-12-08T13:02:53Z)
• Improved FRQI on superconducting processors and its restrictions in the NISQ era [62.997667081978825]
  We study the feasibility of the Flexible Representation of Quantum Images (FRQI) We also check experimentally what is the limit in the current noisy intermediate-scale quantum era. We propose a method for simplifying the circuits needed for the FRQI.
  arXiv  Detail & Related papers  (2021-10-29T10:42:43Z)
• Quantization Guided JPEG Artifact Correction [69.04777875711646]
  We develop a novel architecture for artifact correction using the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
  arXiv  Detail & Related papers  (2020-04-17T00:10:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.