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
Machine learning(ML)has accelerated the process of materials classification,particularly with crystal graph neural network(CGNN)architectures.However,advanced deep networks have hitherto proved challenging to build an...Machine learning(ML)has accelerated the process of materials classification,particularly with crystal graph neural network(CGNN)architectures.However,advanced deep networks have hitherto proved challenging to build and train for quantum materials classification and property prediction.We show that faithful representations,which directly represent crystal structure and symmetry,both refine current ML and effectively implement advanced deep networks to accurately predict these materials and optimize their properties.Our new models reveal the previously hidden power of novel convolutional and pure attentional approaches to represent atomic connectivity and achieve strong performance in predicting topological properties,magnetic properties,and formation energies.With faithful representations,the state-of-the-art CGNN accurately predicts quantum chemistry materials and properties,accelerating the design and discovery and improving the implicit understanding of complex crystal structures and symmetries.On two separate benchmarks,our non-graphical neural networks achieve near parity with the CGNN architecture,making them viable alternatives.展开更多
It is a critical challenge for quantum machine learning to classify the datasets accurately.This article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensi...It is a critical challenge for quantum machine learning to classify the datasets accurately.This article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensional datasets.First,a model of QC-IQS is presented by creating parameterized quantum circuits(PQCs)based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system.Then,a parameterized quantum classification algorithm(QCA)is designed to calculate the classification results by updating the loss function until it converges.Finally,the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets.The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data.Moreover,QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets.It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.展开更多
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.
基金supported as part of the Center for Energy Efficient Magnonics, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award number DE-AC02-76SF00515.
文摘Machine learning(ML)has accelerated the process of materials classification,particularly with crystal graph neural network(CGNN)architectures.However,advanced deep networks have hitherto proved challenging to build and train for quantum materials classification and property prediction.We show that faithful representations,which directly represent crystal structure and symmetry,both refine current ML and effectively implement advanced deep networks to accurately predict these materials and optimize their properties.Our new models reveal the previously hidden power of novel convolutional and pure attentional approaches to represent atomic connectivity and achieve strong performance in predicting topological properties,magnetic properties,and formation energies.With faithful representations,the state-of-the-art CGNN accurately predicts quantum chemistry materials and properties,accelerating the design and discovery and improving the implicit understanding of complex crystal structures and symmetries.On two separate benchmarks,our non-graphical neural networks achieve near parity with the CGNN architecture,making them viable alternatives.
基金supported by the National Natural Science Foundation of China(61972418,61872390)the Natural Science Foundation of Hunan Province(2020JJ4750)+2 种基金the Special Foundation for Distinguished Young Scientists of Changsha(kq1905058)China Computer Federation(CCF)-Baidu Open Fund(CCF-BAIDUOF2021031)the Fundamental Research Funds for the Central Universities of Central South University,China(2022XQLH014)
文摘It is a critical challenge for quantum machine learning to classify the datasets accurately.This article develops a quantum classifier based on the isolated quantum system(QC-IQS)to classify nonlinear and multidimensional datasets.First,a model of QC-IQS is presented by creating parameterized quantum circuits(PQCs)based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system.Then,a parameterized quantum classification algorithm(QCA)is designed to calculate the classification results by updating the loss function until it converges.Finally,the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets.The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data.Moreover,QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets.It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.
