Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction

• URL: http://arxiv.org/abs/2306.02214v3
• Date: Thu, 8 Feb 2024 21:48:30 GMT
• Title: Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction
• Authors: Akihiro Haga
• Abstract summary: The study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels. Approach: The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization (QUBO) problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4x4 to 24x24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP). Main result: By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT outperformed that of MLEM and FBP. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM. Significance: This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.

Related papers

• 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution [83.09117439860607]
  Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment. It is notorious that low-bit quantization degrades the accuracy of SR models compared to their full-precision (FP) counterparts. We present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization.
  arXiv  Detail & Related papers  (2024-06-10T06:06:11Z)
• AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution [53.23803932357899]
  We introduce the first on-the-fly adaptive quantization framework that accelerates the processing time from hours to seconds. We achieve competitive performance with the previous adaptive quantization methods, while the processing time is accelerated by x2000.
  arXiv  Detail & Related papers  (2024-04-04T08:37:27Z)
• QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction [0.0]
  We introduce QN-Mixer, an algorithm based on the quasi-Newton approach. Incept-Mixer is an efficient neural architecture that serves as a non-local regularization term. Our approach intelligently downsamples information, significantly reducing computational requirements.
  arXiv  Detail & Related papers  (2024-02-28T00:20:25Z)
• Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability [3.8704324110545767]
  Quantum Image Processing (QIP) aims to utilize the benefits of quantum computing for manipulating and analyzing images. QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. We propose a novel approach to address the issue of noise in QIP by training and employing a machine learning model that identifies and corrects the noise in quantum-processed images.
  arXiv  Detail & Related papers  (2024-02-18T16:55:54Z)
• A Novel Approach to Threshold Quantum Images by using Unsharp Measurements [0.8287206589886881]
  We propose a hybrid quantum approach to threshold and binarize a grayscale image through unsharp measurements. The proposed methodology uses peaks of the overlapping Gaussians and the distance between neighboring local minima as the variance. The obtained thresholds are used to binarize a grayscale image by using novel enhanced quantum image representation integrated with a threshold encoder.
  arXiv  Detail & Related papers  (2023-10-16T18:34:40Z)
• A quantum segmentation algorithm based on local adaptive threshold for NEQR image [7.798738743268923]
  The complexity of our algorithm can be reduced to $O(n2+q)$, which is an exponential speedup compared to the classic counterparts. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
  arXiv  Detail & Related papers  (2023-10-02T04:01:42Z)
• Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations [8.442020709975015]
  In clinical imaging, this helps to improve patient comfort and reduce radiation exposure. We propose to use an adiabatic quantum computer and associated hybrid methods to solve the reconstruction problem.
  arXiv  Detail & Related papers  (2022-12-02T17:11:48Z)
• Post-Training Quantization for Vision Transformer [85.57953732941101]
  We present an effective post-training quantization algorithm for reducing the memory storage and computational costs of vision transformers. We can obtain an 81.29% top-1 accuracy using DeiT-B model on ImageNet dataset with about 8-bit quantization.
  arXiv  Detail & Related papers  (2021-06-27T06:27:22Z)
• Fully Quantized Image Super-Resolution Networks [81.75002888152159]
  We propose a Fully Quantized image Super-Resolution framework (FQSR) to jointly optimize efficiency and accuracy. We apply our quantization scheme on multiple mainstream super-resolution architectures, including SRResNet, SRGAN and EDSR. Our FQSR using low bits quantization can achieve on par performance compared with the full-precision counterparts on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-29T03:53:49Z)
• Fast and robust quantum state tomography from few basis measurements [65.36803384844723]
  We present an online tomography algorithm designed to optimize all the aforementioned resources at the cost of a worse dependence on accuracy. The protocol is the first to give provably optimal performance in terms of rank and dimension for state copies, measurement settings and memory. Further improvements are possible by executing the algorithm on a quantum computer, giving a quantum speedup for quantum state tomography.
  arXiv  Detail & Related papers  (2020-09-17T11:28:41Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)

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.