We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantu...We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
目的利用网络药理学和分子对接技术研究古代经典名方辛夷散用于过敏性鼻炎防治的潜在作用机制。方法通过TCMSP数据库检索经典名方辛夷散的活性成分并确定作用靶点,通过GeneCards Database、Online Mendelian Inheritance in Man数据库...目的利用网络药理学和分子对接技术研究古代经典名方辛夷散用于过敏性鼻炎防治的潜在作用机制。方法通过TCMSP数据库检索经典名方辛夷散的活性成分并确定作用靶点,通过GeneCards Database、Online Mendelian Inheritance in Man数据库查询与过敏性鼻炎有关的分子靶点,获取两个数据库交集靶点基因。通过Search tool for the retrival of interacting genes/proteins数据库构建上述交集靶点的蛋白质相互作用网络,并导入Cytoscape 3.7.1软件中将分析结果可视化,进一步根据拓扑学数据筛选关键靶点。利用Metascape数据库对交集靶点进行GO和KEGG富集分析。运用AutoDockTools1.5.6软件将活性化合物与核心靶点蛋白进行分子对接。结果筛选得到辛夷散活性化合物184个,相关靶点224个,辛夷散与过敏性鼻炎共同的靶点104个。利用拓扑学筛选得到17个关键靶点,主要包括IL-6、TNF、AKT1、IL1B、VEGFA等。GO功能富集分析表明辛夷散治疗过敏性鼻炎可能涉及脂多糖的反应、细胞对有机环状化合物反应、氧化应激反应等1780条生物过程。KEGG通路结果显示主要参与IL-17信号通路、HIF-1信号通路、糖尿病并发症中的AGE-RAGE信号通路、癌症通路等。分子对接结果显示方中主要活性成分山奈酚、维斯体素、柚皮素、β-谷甾醇、芒柄花黄素等与IL-6、TNF、AKT1、IL1B、VEGFA等均能实现自发结合。结论经典名方辛夷散可能通过多成分、多靶点及多种通路治疗过敏性鼻炎,为深入研究辛夷散的作用机制提供了理论参考。展开更多
In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The pro...In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to n<sup>m</sup> classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n<sup>m</sup>. They are then interpreted as one-hot encoded labels, padded with n<sup>m</sup> - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.展开更多
基金Project supported by the Natural Science Foundation of Shandong Province,China (Grant No. ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001)。
文摘We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
文摘目的利用网络药理学和分子对接技术研究古代经典名方辛夷散用于过敏性鼻炎防治的潜在作用机制。方法通过TCMSP数据库检索经典名方辛夷散的活性成分并确定作用靶点,通过GeneCards Database、Online Mendelian Inheritance in Man数据库查询与过敏性鼻炎有关的分子靶点,获取两个数据库交集靶点基因。通过Search tool for the retrival of interacting genes/proteins数据库构建上述交集靶点的蛋白质相互作用网络,并导入Cytoscape 3.7.1软件中将分析结果可视化,进一步根据拓扑学数据筛选关键靶点。利用Metascape数据库对交集靶点进行GO和KEGG富集分析。运用AutoDockTools1.5.6软件将活性化合物与核心靶点蛋白进行分子对接。结果筛选得到辛夷散活性化合物184个,相关靶点224个,辛夷散与过敏性鼻炎共同的靶点104个。利用拓扑学筛选得到17个关键靶点,主要包括IL-6、TNF、AKT1、IL1B、VEGFA等。GO功能富集分析表明辛夷散治疗过敏性鼻炎可能涉及脂多糖的反应、细胞对有机环状化合物反应、氧化应激反应等1780条生物过程。KEGG通路结果显示主要参与IL-17信号通路、HIF-1信号通路、糖尿病并发症中的AGE-RAGE信号通路、癌症通路等。分子对接结果显示方中主要活性成分山奈酚、维斯体素、柚皮素、β-谷甾醇、芒柄花黄素等与IL-6、TNF、AKT1、IL1B、VEGFA等均能实现自发结合。结论经典名方辛夷散可能通过多成分、多靶点及多种通路治疗过敏性鼻炎,为深入研究辛夷散的作用机制提供了理论参考。
文摘In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to n<sup>m</sup> classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n<sup>m</sup>. They are then interpreted as one-hot encoded labels, padded with n<sup>m</sup> - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.
