Despite the limited availability and quantum volume of quantum computers, quantum image representation is a widely researched area. Currently developed methods use quantum entanglement to encode information about pixel positions. These methods range from using the angle parameter of the rotation gate (e.g., the Flexible Representation of Quantum Images, FRQI), sequences of qubits (e.g., Novel Enhanced Quantum Representation, NEQR), or the angle parameter of the phase shift gates (e.g., Local Phase Image Quantum Encoding, LPIQE) for storing color information. All these methods are significantly affected by decoherence and other forms of quantum noise, which is an inseparable part of quantum computing in the noisy intermediate-scale quantum era. These phenomena can highly influence the measurements and result in extracted images that are visually dissimilar to the originals. Because this process is at its foundation quantum, the computational reversal of this process is possible. There are many methods for error correction, mitigation, and reduction, but all of them use quantum computer time or additional qubits to achieve the desired result. We report the successful use of a generative adversarial network trained for image-to-image translation, in conjunction with Phase Distortion Unraveling error reduction method, for reducing overall error in images encoded using LPIQE.

翻译：尽管量子计算机的可用性和量子体积有限，量子图像表示仍是一个广泛研究的领域。当前开发的方法利用量子纠缠来编码像素位置信息，包括使用旋转门角度参数（如量子图像灵活表示，FRQI）、量子比特序列（如新型增强量子表示，NEQR）或相移门角度参数（如局部相位图像量子编码，LPIQE）来存储颜色信息。所有这些方法都显著受到退相干及其他形式量子噪声的影响，这是含噪中等规模量子时代量子计算不可分割的一部分。这些现象会极大影响测量结果，导致提取的图像与原始图像在视觉上存在差异。由于该过程在本质上是量子的，因此其计算逆过程是可行的。目前存在多种纠错、缓解和降低误差的方法，但均需消耗量子计算机时间或额外量子比特以达到预期效果。我们报告了成功使用针对图像到图像翻译训练的生成对抗网络，结合相位失真解析误差降低方法，来减少基于LPIQE编码图像的整体误差。