Quantum Supremacy in Tomographic Imaging: Advances in Quantum Tomography Algorithms
- URL: http://arxiv.org/abs/2502.04830v1
- Date: Fri, 07 Feb 2025 11:05:41 GMT
- Title: Quantum Supremacy in Tomographic Imaging: Advances in Quantum Tomography Algorithms
- Authors: Hyunju Lee, Kyungtaek Jun,
- Abstract summary: This study substantiates quantum supremacy by reducing the required projection angles for tomographic reconstruction.
It achieves precise reconstructions using only 50% of the projection angles from the original sinogram spanning 0deg to 180deg.
These findings highlight the potential of quantum algorithms to revolutionize tomographic imaging.
- Score: 3.2995359570845912
- License:
- Abstract: Quantum computing has emerged as a transformative paradigm, capable of tackling complex computational problems that are infeasible for classical methods within a practical timeframe. At the core of this advancement lies the concept of quantum supremacy, which signifies the ability of quantum processors to surpass classical systems in specific tasks. In the context of tomographic image reconstruction, quantum optimization algorithms enable faster processing and clearer imaging than conventional methods. This study further substantiates quantum supremacy by reducing the required projection angles for tomographic reconstruction while enhancing robustness against image artifacts. Notably, our experiments demonstrated that the proposed algorithm accurately reconstructed tomographic images without artifacts, even when up to 50% error was introduced into the sinogram to induce ring artifacts. Furthermore, it achieved precise reconstructions using only 50% of the projection angles from the original sinogram spanning 0{\deg} to 180{\deg}. These findings highlight the potential of quantum algorithms to revolutionize tomographic imaging by enabling efficient and accurate reconstructions under challenging conditions, paving the way for broader applications in medical imaging, material science, and advanced tomography systems as quantum computing technologies continue to advance.
Related papers
- Computational metaoptics for imaging [3.105460926371459]
"Computational metaoptics" combines the physical wavefront shaping ability of metasurfaces with advanced computational algorithms to enhance imaging performance beyond conventional limits.
By treating metasurfaces as physical preconditioners and co-designing them with reconstruction algorithms through end-to-end (inverse) design, it is possible to jointly optimize the optical hardware and computational software.
Advanced applications enabled by computational metaoptics are highlighted, including phase imaging and quantum state measurement.
arXiv Detail & Related papers (2024-11-14T02:13:25Z) - 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) - 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) - Retrieving space-dependent polarization transformations via near-optimal
quantum process tomography [55.41644538483948]
We investigate the application of genetic and machine learning approaches to tomographic problems.
We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time.
We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
arXiv Detail & Related papers (2022-10-27T11:37:14Z) - Quantum Face Recognition Protocol with Ghost Imaging [1.4856165761750735]
We propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis (QPCA)
A novel quantum algorithm for finding dissimilarity in the faces based on the determinant of trace and computation of a matrix (image) is also proposed.
Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system.
arXiv Detail & Related papers (2021-10-19T16:31:46Z) - Facial Expression Recognition on a Quantum Computer [68.8204255655161]
We show a possible solution to facial expression recognition using a quantum machine learning approach.
We define a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.
arXiv Detail & Related papers (2021-02-09T13:48:00Z) - Sparse Reconstruction for Radar Imaging based on Quantum Algorithms [17.240702633984583]
This paper is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging.
The corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity.
The simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.
arXiv Detail & Related papers (2021-01-21T07:03:14Z) - Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry.
New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications.
We present a novel technique to reconstruct the transfer matrix of linear optical networks.
arXiv Detail & Related papers (2020-10-01T16:04:22Z) - 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) - Quantum Medical Imaging Algorithms [9.775834440292487]
A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices.
We provide quantum algorithms for image reconstruction with exponential speedup over classical counterparts when data is input as a quantum state.
arXiv Detail & Related papers (2020-04-04T22:19:22Z)
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.