A hybrid-qudit representation of digital RGB images
- URL: http://arxiv.org/abs/2207.12550v1
- Date: Mon, 25 Jul 2022 21:57:46 GMT
- Title: A hybrid-qudit representation of digital RGB images
- Authors: Sreetama Das and Filippo Caruso
- Abstract summary: We use two entangled quantum registers constituting of total 7 qutrits to encode the color channels and their intensities.
We generalize the existing encoding methods by using both qubits and qutrits to encode the pixel positions of a rectangular image.
This hybrid-qudit approach aligns well with the current progress of NISQ devices in incorporating higher dimensional quantum systems.
- Score: 7.766921168069532
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum image processing is an emerging topic in the field of quantum
information and technology. In this paper, we propose a new quantum image
representation of RGB images, which is an improvement to all the existing
representations in terms of using minimum resource. We use two entangled
quantum registers constituting of total 7 qutrits to encode the color channels
and their intensities. Additionally, we generalize the existing encoding
methods by using both qubits and qutrits to encode the pixel positions of a
rectangular image. This hybrid-qudit approach aligns well with the current
progress of NISQ devices in incorporating higher dimensional quantum systems
than qubits. We then describe the image encoding method using higher-order
qubit-qutrit gates, and demonstrate the decomposition of these gates in terms
of simpler elementary gates. We use the Google Cirq's quantum simulator to
verify the image retrieval. We show that the complexity of the image encoding
process is linear in the number of pixels. Lastly, we discuss the image
compression and some basic RGB image processing protocols using our
representation.
Related papers
- Transformer based Pluralistic Image Completion with Reduced Information Loss [72.92754600354199]
Transformer based methods have achieved great success in image inpainting recently.
They regard each pixel as a token, thus suffering from an information loss issue.
We propose a new transformer based framework called "PUT"
arXiv Detail & Related papers (2024-03-31T01:20:16Z) - Tensor Network Based Efficient Quantum Data Loading of Images [0.0]
We present a novel method for creating quantum states that approximately encode images as amplitudes.
We experimentally demonstrate our technique on 8 qubits of a trapped ion quantum computer for complex images of road scenes.
arXiv Detail & Related papers (2023-10-09T17:40:41Z) - 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) - Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels.
They are not widely adopted by general users due to their substantial storage requirements.
We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
arXiv Detail & Related papers (2023-06-21T06:59:07Z) - 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) - 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) - Quantum pixel representations and compression for $N$-dimensional images [0.0]
We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI.
The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations.
arXiv Detail & Related papers (2021-10-08T23:32:00Z) - COIN: COmpression with Implicit Neural representations [64.02694714768691]
We propose a new simple approach for image compression.
Instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image.
arXiv Detail & Related papers (2021-03-03T10:58:39Z)
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.