Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can
lead to vision impairment due to lesions formed on the retina. Detecting DR at
an advanced stage often results in irreversible blindness. The traditional
process of diagnosing DR through retina fundus images by ophthalmologists is
not only time-intensive but also expensive. While classical transfer learning
models have been widely adopted for computer-aided detection of DR, their high
maintenance costs can hinder their detection efficiency. In contrast, Quantum
Transfer Learning offers a more effective solution to this challenge. This
approach is notably advantageous because it operates on heuristic principles,
making it highly optimized for the task. Our proposed methodology leverages
this hybrid quantum transfer learning technique to detect DR. To construct our
model, we utilize the APTOS 2019 Blindness Detection dataset, available on
Kaggle. We employ the ResNet-18, ResNet34, ResNet50, ResNet101, ResNet152 and
Inception V3, pre-trained classical neural networks, for the initial feature
extraction. For the classification stage, we use a Variational Quantum
Classifier. Our hybrid quantum model has shown remarkable results, achieving an
accuracy of 97% for ResNet-18. This demonstrates that quantum computing, when
integrated with quantum machine learning, can perform tasks with a level of
power and efficiency unattainable by classical computers alone. By harnessing
these advanced technologies, we can significantly improve the detection and
diagnosis of Diabetic Retinopathy, potentially saving many from the risk of
blindness.
Keywords: Diabetic Retinopathy, Quantum Transfer Learning, Deep Learning

利用深度学习和量子转移学习的结合，我们的混合量子模型在检测和诊断糖尿病视网膜病变方面取得了显著的结果，潜在地能够挽救许多人免于失明的风险。

利用量子迁移学习进行糖尿病视网膜病变检测

Diabetic Retinopathy Detection Using Quantum Transfer Learning

Hybrid quantum-classical classifiers promise to positively impact critical
aspects of natural language processing tasks, particularly
classification-related ones. Among the possibilities currently investigated,
quantum transfer learning, i.e., using a quantum circuit for fine-tuning
pre-trained classical models for a specific task, is attracting significant
attention as a potential platform for proving quantum advantage.
This work shows potential advantages, both in terms of performance and
expressiveness, of quantum transfer learning algorithms trained on embedding
vectors extracted from a large language model to perform classification on a
classical Linguistics task: acceptability judgments. Acceptability judgment is
the ability to determine whether a sentence is considered natural and
well-formed by a native speaker. The approach has been tested on sentences
extracted from ItaCoLa, a corpus that collects Italian sentences labeled with
their acceptability judgment. The evaluation phase shows results for the
quantum transfer learning pipeline comparable to state-of-the-art classical
transfer learning algorithms, proving current quantum computers' capabilities
to tackle NLP tasks for ready-to-use applications. Furthermore, a qualitative
linguistic analysis, aided by explainable AI methods, reveals the capabilities
of quantum transfer learning algorithms to correctly classify complex and more
structured sentences, compared to their classical counterpart. This finding
sets the ground for a quantifiable quantum advantage in NLP in the near future.

该研究展示了使用从大型语言模型中提取的嵌入向量进行量子迁移学习算法在经典语言学任务（可接受性判断）上进行分类的潜在优势，包括性能和表达能力，评估结果显示出与最先进的经典迁移学习算法相媲美的量子迁移学习流程，为当前量子计算机在自然语言处理任务中的能力提供了证明，并通过可解释的人工智能方法进行了定性的语言分析，揭示了量子迁移学习算法相较于经典算法在正确分类复杂和结构化句子方面的能力，为未来在自然语言处理中取得量子优势奠定了基础。

量子迁移学习的可接受性判断

Quantum Transfer Learning for Acceptability Judgements

Quantum Transfer Learning (QTL) recently gained popularity as a hybrid
quantum-classical approach for image classification tasks by efficiently
combining the feature extraction capabilities of large Convolutional Neural
Networks with the potential benefits of Quantum Machine Learning (QML).
Existing approaches, however, only utilize gate-based Variational Quantum
Circuits for the quantum part of these procedures. In this work we present an
approach to employ Quantum Annealing (QA) in QTL-based image classification.
Specifically, we propose using annealing-based Quantum Boltzmann Machines as
part of a hybrid quantum-classical pipeline to learn the classification of
real-world, large-scale data such as medical images through supervised
training. We demonstrate our approach by applying it to the three-class
COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices.
Using Simulated Annealing as a stand-in for actual QA, we compare our method to
classical transfer learning, using a neural network of the same order of
magnitude, to display its improved classification performance. We find that our
approach consistently outperforms its classical baseline in terms of test
accuracy and AUC-ROC-Score and needs less training epochs to do this.

利用量子迁移学习 (QTL) 和量子退火 (QA) 的混合量子经典方法，通过将量子玻尔兹曼机引入混合量子经典管道，实现了对大规模医学图像的监督学习，取得了比传统迁移学习更好的分类性能。

基于退火量子玻尔兹曼机的大规模图像分类的迁移学习

Towards Transfer Learning for Large-Scale Image Classification Using  Annealing-based Quantum Boltzmann Machines

Quantum computers possess the potential to process data using a remarkably
reduced number of qubits compared to conventional bits, as per theoretical
foundations. However, recent experiments have indicated that the practical
feasibility of retrieving an image from its quantum encoded version is
currently limited to very small image sizes. Despite this constraint,
variational quantum machine learning algorithms can still be employed in the
current noisy intermediate scale quantum (NISQ) era. An example is a hybrid
quantum machine learning approach for edge detection. In our study, 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, offering insights into
their execution efficiency.

应用量子迁移学习检测灰度图像中的裂纹，比较了 PennyLane 的标准量子位和 IBM 的 qasm_simulator 以及真实后端的性能和训练时间，提供了他们的执行效率的见解。