Faculty of Engineering, Istanbul Atlas University, 34295, Istanbul, Türkiye.
Faculty of Engineering, Istanbul Atlas University, Hamidiye, Anadolu Cd. No:40, 34408, 34403, Kağıthane, Istanbul, Turkey.
BMC Med Imaging. 2023 Sep 14;23(1):126. doi: 10.1186/s12880-023-01084-5.
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods. Further research and development in this area can revolutionize the way medical images are classified and help improve patient diagnosis and treatment. The use of Conventional Neural Networks (CNN) for the classification and diagnosis of medical images using big datasets requires, in most cases, the use of special high-performance computing machines, which are very expensive and hard to access by most researchers. A new form of Machine Learning (ML), Quantum machine learning (QML), is being introduced as an emerging strategy to overcome this problem. A hybrid quantum-classical CNN uses both quantum and classical convolution layers designed to use a parameterized quantum circuit. This means that the computing model utilizes a quantum circuits approach to construct QML algorithms, which are then used to transform the quantum state to extract image hidden features. This computational acceleration is expected to achieve better algorithm performance than classical CNNs. This study intends to evaluate the performance of a Hybrid Quantum CNN (HQCNN) against a conventional CNN. This is followed by some optimizer modifications for both proposed and classical CNN methods to investigate the possible further improvement of their performance. The optimizer modification is based on forcing the optimizer to be directly adaptive to model accuracy. The optimizer adaptiveness is based on the development of an optimizer with a loss base adaptive momentum. Several algorithms are developed to achieve the above-mentioned goals, including CNN, QCNN, CNN with the adaptive optimizer, and QCNN with the Adaptive optimizer. The four algorithms are tested against a Kaggle brain dataset containing over 7000 samples. The test results show the hybrid quantum circuit algorithm outperformed the conventional CNN algorithm. The performance of both algorithms was further improved by using a fully adaptive SGD optimizer.
混合量子系统通过结合经典算法和量子算法的优势，在图像分类方面显示出了很大的潜力。这些系统利用量子计算机的并行处理能力来执行复杂的计算，同时利用经典算法来处理成像中涉及的大量数据。与传统的经典方法相比，混合方法旨在提高准确性和速度。该领域的进一步研究和开发可以彻底改变医学图像分类的方式，并有助于改善患者的诊断和治疗。
在使用大型数据集对医学图像进行分类和诊断时，传统的神经网络 (CNN) 需要在大多数情况下使用特殊的高性能计算机构成，这非常昂贵，而且大多数研究人员都难以获得。一种新的机器学习 (ML) 形式，量子机器学习 (QML)，作为一种新兴策略被引入，以解决这个问题。混合量子-经典卷积神经网络使用量子和经典卷积层，旨在使用参数化量子电路。这意味着计算模型利用量子电路方法构建 QML 算法，然后使用这些算法转换量子态以提取图像隐藏特征。这种计算加速预计将比经典 CNN 实现更好的算法性能。
本研究旨在评估混合量子卷积神经网络 (HQCNN) 与传统 CNN 的性能。然后，对提出的和经典的 CNN 方法进行一些优化器修改，以研究其性能的进一步提高的可能性。优化器修改的基础是迫使优化器直接适应模型的准确性。优化器的适应性是基于开发一种基于损失的自适应动量的优化器。为了实现上述目标，开发了几种算法，包括 CNN、QCNN、带有自适应优化器的 CNN 和带有自适应优化器的 QCNN。在包含超过 7000 个样本的 Kaggle 脑数据集上对这四个算法进行了测试。测试结果表明，混合量子电路算法优于传统的 CNN 算法。通过使用完全自适应的 SGD 优化器，这两种算法的性能得到了进一步提高。
