Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability
This addresses noise issues in QIP for quantum computing applications, but it is incremental as it applies existing machine learning techniques to a specific domain problem.
The researchers tackled noise in Quantum Image Processing (QIP) by training a machine learning model to correct noise in quantum-processed images, achieving results comparable to classical computers with higher efficiency, as evaluated using PSNR, SSIM, and MOS metrics.
Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research, 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, we can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency. The model is trained by learning a dataset consisting of both existing processed images and quantum-processed images from open-access datasets. This model will be capable of providing us with the confidence level for each pixel and its potential original value. To assess the model's accuracy in compensating for loss and decoherence in QIP, we evaluate it using three metrics: Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS). Additionally, we discuss the applicability of our model across domains well as its cost effectiveness compared to alternative methods.