As medium-scale quantum computers progress, the application of quantum
algorithms across diverse fields like simulating physical systems, chemistry,
optimization, and cryptography becomes more prevalent. However, these quantum
computers, known as Noisy Intermediate Scale Quantum (NISQ), are susceptible to
noise, prompting the search for applications that can capitalize on quantum
advantage without extensive error correction procedures. Since, Machine
Learning (ML), particularly Deep Learning (DL), faces challenges due to
resource-intensive training and algorithmic opacity. Therefore, this study
explores the intersection of quantum computing and ML, focusing on computer
vision tasks. Specifically, it evaluates the effectiveness of hybrid
quantum-classical algorithms, such as the data re-uploading scheme and the
patch Generative Adversarial Networks (GAN) model, on small-scale quantum
devices. Through practical implementation and testing, the study reveals
comparable or superior performance of these algorithms compared to classical
counterparts, highlighting the potential of leveraging quantum algorithms in ML
tasks.

该研究探索了量子计算和机器学习的交叉领域，着重评估数据重新上传方案和补丁生成对抗网络模型等混合量子 - 经典算法在小规模量子设备上的有效性，并通过实际实现和测试揭示了这些算法与经典对应算法相当或更好的性能，突显了在机器学习任务中利用量子算法的潜力。

探索用于计算机视觉的量子增强机器学习：对噪声中尺度量子设备的应用和见解

Exploring Quantum-Enhanced Machine Learning for Computer Vision:  Applications and Insights on Noisy Intermediate-Scale Quantum Devices

Quantum machine learning is a promising programming paradigm for the
optimization of quantum algorithms in the current era of noisy intermediate
scale quantum (NISQ) computers. A fundamental challenge in quantum machine
learning is generalization, as the designer targets performance under testing
conditions, while having access only to limited training data. Existing
generalization analyses, while identifying important general trends and scaling
laws, cannot be used to assign reliable and informative "error bars" to the
decisions made by quantum models. In this article, we propose a general
methodology that can reliably quantify the uncertainty of quantum models,
irrespective of the amount of training data, of the number of shots, of the
ansatz, of the training algorithm, and of the presence of quantum hardware
noise. The approach, which builds on probabilistic conformal prediction, turns
an arbitrary, possibly small, number of shots from a pre-trained quantum model
into a set prediction, e.g., an interval, that provably contains the true
target with any desired coverage level. Experimental results confirm the
theoretical calibration guarantees of the proposed framework, referred to as
quantum conformal prediction.

该论文提出了一种处理量子机器学习中普遍存在的不确定性的通用方法学，该方法可以在训练数据量有限、量子计算硬件噪声存在的情况下可靠地度量量子模型的不确定，并基于概率共同预测来达到对真实目标的可信度量。