Journal articles: 'Supervised quantum learning'

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Fanizza, Marco, Andrea Mari, and Vittorio Giovannetti. "Supervised Quantum State Discrimination." Proceedings 12, no. 1 (July 19, 2019): 21. http://dx.doi.org/10.3390/proceedings2019012021.

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Zheng, Yu-Lin, Wen Zhang, Cheng Zhou, and Wei Geng. "Quantum annealing for semi-supervised learning." Chinese Physics B 30, no. 4 (April 1, 2021): 040306. http://dx.doi.org/10.1088/1674-1056/abe298.

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Havlíček, Vojtěch, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta. "Supervised learning with quantum-enhanced feature spaces." Nature 567, no. 7747 (March 2019): 209–12. http://dx.doi.org/10.1038/s41586-019-0980-2.

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Shrapnel, Sally, Fabio Costa, and Gerard Milburn. "Quantum Markovianity as a supervised learning task." International Journal of Quantum Information 16, no. 08 (December 2018): 1840010. http://dx.doi.org/10.1142/s0219749918400105.

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Supervised learning algorithms take as input a set of labeled examples and return as output a predictive model. Such models are used to estimate labels for future, previously unseen examples, drawn from the same generating distribution. In this paper, we investigate the possibility of using supervised learning to estimate the dimension of a non-Markovian quantum environment. Our approach uses an ensemble learning method, the Random Forest Regressor, applied to classically simulated datasets. Our results indicate this is a promising line of research.

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Nivelkar, Mukta, and S. G. Bhirud. "Modeling of Supervised Machine Learning using Mechanism of Quantum Computing." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2161/1/012023.

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Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.

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Sarma, Abhijat, Rupak Chatterjee, Kaitlin Gili, and Ting Yu. "Quantum unsupervised and supervised learning on superconducting processors." Quantum Information and Computation 20, no. 7&8 (June 2020): 541–52. http://dx.doi.org/10.26421/qic20.7-8-1.

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Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the data, the computation time for training and using these statistical models grows quickly. Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it becomes less computationally expensive to implement for large datasets as compared to its classical counterpart.

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Innocenti, Luca, Leonardo Banchi, Sougato Bose, Alessandro Ferraro, and Mauro Paternostro. "Approximate supervised learning of quantum gates via ancillary qubits." International Journal of Quantum Information 16, no. 08 (December 2018): 1840004. http://dx.doi.org/10.1142/s021974991840004x.

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We present strategies for the training of a qubit network aimed at the ancilla-assisted synthesis of multi-qubit gates based on a set of restricted resources. By assuming the availability of only time-independent single and two-qubit interactions, we introduce and describe a supervised learning strategy implemented through momentum-stochastic gradient descent with automatic differentiation methods. We demonstrate the effectiveness of the scheme by discussing the implementation of nontrivial three qubit operations, including a QFT and a half-adder gate.

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Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning." Quantum Information and Computation 15, no. 3&4 (March 2015): 316–56. http://dx.doi.org/10.26421/qic15.3-4-7.

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We present quantum algorithms for performing nearest-neighbor learning and $k$--means clustering. At the core of our algorithms are fast and coherent quantum methods for computing the Euclidean distance both directly and via the inner product which we couple with methods for performing amplitude estimation that do not require measurement. We prove upper bounds on the number of queries to the input data required to compute such distances and find the nearest vector to a given test example. In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to Monte Carlo algorithms. We also study the performance of our quantum nearest-neighbor algorithms on several real-world binary classification tasks and find that the classification accuracy is competitive with classical methods.

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Jackson, Nicholas E., Alec S. Bowen, Lucas W. Antony, Michael A. Webb, Venkatram Vishwanath, and Juan J. de Pablo. "Electronic structure at coarse-grained resolutions from supervised machine learning." Science Advances 5, no. 3 (March 2019): eaav1190. http://dx.doi.org/10.1126/sciadv.aav1190.

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Computational studies aimed at understanding conformationally dependent electronic structure in soft materials require a combination of classical and quantum-mechanical simulations, for which the sampling of conformational space can be particularly demanding. Coarse-grained (CG) models provide a means of accessing relevant time scales, but CG configurations must be back-mapped into atomistic representations to perform quantum-chemical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models. A machine learning approach, denoted as artificial neural network electronic coarse graining (ANN-ECG), is presented here in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to identify computationally optimal CG resolutions.

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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (November 16, 2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.

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Kimura, Tomoaki, Kodai Shiba, Chih-Chieh Chen, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. "Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation." Mathematical Problems in Engineering 2021 (December 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/3511029.

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Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.

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Li, Wei-Ming, and Shi-Ju Ran. "Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity." Mathematics 10, no. 6 (March 15, 2022): 940. http://dx.doi.org/10.3390/math10060940.

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In quantum and quantum-inspired machine learning, a key step is to embed the data in the quantum space known as Hilbert space. Studying quantum kernel function, which defines the distances among the samples in the Hilbert space, belongs to the fundamental topics in this direction. In this work, we propose a tunable quantum-inspired kernel function (QIKF) named rescaled logarithmic fidelity (RLF) and a non-parametric algorithm for the semi-supervised learning in the quantum space. The rescaling takes advantage of the non-linearity of the kernel to tune the mutual distances of samples in the Hilbert space, and meanwhile avoids the exponentially-small fidelities between quantum many-qubit states. Being non-parametric excludes the possible effects from the variational parameters, and evidently demonstrates the properties of the kernel itself. Our results on the hand-written digits (MNIST dataset) and movie reviews (IMDb dataset) support the validity of our method, by comparing with the standard fidelity as the QIKF as well as several well-known non-parametric algorithms (naive Bayes classifiers, k-nearest neighbors, and spectral clustering). High accuracy is demonstrated, particularly for the unsupervised case with no labeled samples and the few-shot cases with small numbers of labeled samples. With the visualizations by t-stochastic neighbor embedding, our results imply that the machine learning in the Hilbert space complies with the principles of maximal coding rate reduction, where the low-dimensional data exhibit within-class compressibility, between-class discrimination, and overall diversity. The proposed QIKF and semi-supervised algorithm can be further combined with the parametric models such as tensor networks, quantum circuits, and quantum neural networks.

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Li, Xiaoyu, Yifei Pei, Ying Zhao, Haoqiang Song, Jianhui Zhao, Lei Yan, Hui He, Siyu Lu, and Xiaobing Yan. "Memristors based on carbon dots for learning activities in artificial biosynapse applications." Materials Chemistry Frontiers 6, no. 8 (2022): 1098–106. http://dx.doi.org/10.1039/d2qm00151a.

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Carbon quantum dots (CDs) were doped into the memristor to prepare Ag/HfO2/CDs/Pt devices, which improved the uniformity of device parameters and accomplished simulations of supervised learning, interest-based learning activities and preview and review learning method.

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Chabaud, Ulysse, Damian Markham, and Adel Sohbi. "Quantum machine learning with adaptive linear optics." Quantum 5 (July 5, 2021): 496. http://dx.doi.org/10.22331/q-2021-07-05-496.

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We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine – either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.

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Li, Guangxi, Zhixin Song, and Xin Wang. "VSQL: Variational Shadow Quantum Learning for Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8357–65. http://dx.doi.org/10.1609/aaai.v35i9.17016.

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Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.

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Miano, Mariana Godoy Vazquez, and Aleccheevina Silva de Oliveira. "DESEMPENHO DE ALGORITMOS QUÂNTICOS E CLÁSSICOS EM TREINAMENTO DE MACHINE LEARNING SUPERVISIONADO." REVISTA TECNOLÓGICA DA FATEC AMERICANA 09, no. 02 (December 20, 2021): 81–99. http://dx.doi.org/10.47283/244670492021090281.

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This article addresses the interdisciplinary theme of Quantum Computing with Machine Learning, two technologies potentially capable of making changes in how computing is performed, solving initially unsolvable problems. The focus of this research was Quantum Computing applications that result in computational performance gain in specific Machine Learning tasks. The objective is to analyze the feasibility of using quantum algorithms for Machine Learning. More specifically, to analyze which quantum algorithms can be applied to Machine Learning tasks, compared to classical algorithms, in the search for better performance. For the development of the research, a bibliographic review of quantum algorithms was carried out and, subsequently, the implementation and performance verification of the quantum algorithm QSVM and its corresponding classic version SVM, in supervised learning with the AD HOC and IRIS datasets.

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Liu, Yunchao, Srinivasan Arunachalam, and Kristan Temme. "A rigorous and robust quantum speed-up in supervised machine learning." Nature Physics 17, no. 9 (July 12, 2021): 1013–17. http://dx.doi.org/10.1038/s41567-021-01287-z.

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Wang, Rui, Carlos Hernani-Morales, José D. Martín-Guerrero, Enrique Solano, and Francisco Albarrán-Arriagada. "Quantum pattern recognition in photonic circuits." Quantum Science and Technology 7, no. 1 (November 16, 2021): 015010. http://dx.doi.org/10.1088/2058-9565/ac3460.

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Abstract This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained supervised learning algorithms that can predict the degree of entanglement in the two-mode state as well as perform the full tomography of one photonic mode, obtaining satisfactory values in the considered regression metrics.

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Kawai, Hiroki, and Yuya O. Nakagawa. "Predicting excited states from ground state wavefunction by supervised quantum machine learning." Machine Learning: Science and Technology 1, no. 4 (October 31, 2020): 045027. http://dx.doi.org/10.1088/2632-2153/aba183.

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Zhu, Jinlin, Zhiqiang Ge, and Zhihuan Song. "Quantum statistic based semi-supervised learning approach for industrial soft sensor development." Control Engineering Practice 74 (May 2018): 144–52. http://dx.doi.org/10.1016/j.conengprac.2018.03.001.

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Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (December 2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.

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Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the interaction distance reduces to a global optimization problem whose goal is to search for the free-fermion entanglement spectrum closest to the given entanglement spectrum. In this work, we employ techniques from machine learning in order to perform this same task. In a supervised learning setting, we use labeled data obtained by computing the interaction distance and predict its value via linear regression. Moving to a semi-supervised setting, we train an autoencoder to estimate an alternative measure to the interaction distance, and we show that it behaves in a similar manner.

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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, Hsun-Chung Wu, Hua Li Chen, Chien-Ming Wu, and Ray-Kuang Lee. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (April 25, 2022): 874. http://dx.doi.org/10.3390/sym14050874.

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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation.

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Hsieh, Hsien-Yi, Jingyu Ning, Yi-Ru Chen, Hsun-Chung Wu, Hua Li Chen, Chien-Ming Wu, and Ray-Kuang Lee. "Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography." Symmetry 14, no. 5 (April 25, 2022): 874. http://dx.doi.org/10.3390/sym14050874.

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With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation.

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Belis, Vasilis, Samuel González-Castillo, Christina Reissel, Sofia Vallecorsa, Elías F. Combarro, Günther Dissertori, and Florentin Reiter. "Higgs analysis with quantum classifiers." EPJ Web of Conferences 251 (2021): 03070. http://dx.doi.org/10.1051/epjconf/202125103070.

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We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

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Yang, Jing Hai. "Application of Quantum Self-Organization Mapping Networks to Classification." Applied Mechanics and Materials 411-414 (September 2013): 707–11. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.707.

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A quantum self-organization mapping networks model based on quantum neurons is presented in this paper. Both the input and the weight of the model are represented by the quantum bits, and the output of the model is represented by the real number. The model is composed of input layer and competitive layer. First, the samples are transformed into quantum states and are submitted to the input layer, and then the similar coefficients of quantum states are computed between the inputs and the weights. Secondly, the implicit pattern characters of the clustering samples are extracted in the competitive layer, and then the clustering results are showed. The quantum states of weights are updated by quantum rotation gates. The networks are trained by the algorithm combining the unsupervised learning and supervised learning together. Finally two experiments demonstrate that the model and algorithm are evidently superior to the general self-organization mapping networks.

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Regonia, Paul Rossener, Christian Mark Pelicano, Ryosuke Tani, Atsushi Ishizumi, Hisao Yanagi, and Kazushi Ikeda. "Predicting the band gap of ZnO quantum dots via supervised machine learning models." Optik 207 (April 2020): 164469. http://dx.doi.org/10.1016/j.ijleo.2020.164469.

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Srikumar, Maiyuren, Charles D. Hill, and Lloyd C. L. Hollenberg. "Clustering and enhanced classification using a hybrid quantum autoencoder." Quantum Science and Technology 7, no. 1 (December 21, 2021): 015020. http://dx.doi.org/10.1088/2058-9565/ac3c53.

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Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

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Et.al, Balaji Vicharapu. "A New Way To Prevent Colorectal Cancer Using Supervised Learning Technique." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 10, 2021): 3931–44. http://dx.doi.org/10.17762/turcomat.v12i3.1682.

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The Colorectal cancer prompts to more number of death as of late. The diagnosis of colorectal cancer as early is protected to treat the patient. To distinguish and treat this type of cancer, Colonoscopy is applied ordinarily. Several risk prediction models for colorectal cancer have been created and approved in various populations but colon cancer effecting the young adults. In this research, we projected a Supervised Learning Technique for detecting colorectal cancer in high dimensional information.One of the most important and very popular tool for performing the machine learning tasks that includesnovelty detection,classificationorregression is Support vector machine (SVM). Training the SVM requires large quantity of quadratic programming. Due to memory constraints conventional methods are not directly applied. To overcomethese inadequacies,we introduced, Least Square (LS), Particle Swarm Optimization (PSO), Quadratic Programming and Quantum-behave PSO methods for training SVM.To corroborate the competence and proficiency of our predictable system, it is developed in open source called NCSS Software.The acquiredoutcomesof these approaches are verified on a CCG1.11 Colorectal dataset and related with the particularresolution model.

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Hamann, Arne, and Sabine Wölk. "Performance analysis of a hybrid agent for quantum-accessible reinforcement learning." New Journal of Physics 24, no. 3 (March 1, 2022): 033044. http://dx.doi.org/10.1088/1367-2630/ac5b56.

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Abstract In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning (RL). In RL, a so-called agent is challenged to solve a task given by some environment. The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment. For some classical task environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms. In this paper, we analytically analyze the behavior of a hybrid agent which combines this quadratic speedup in exploration with the policy update of a classical agent. This leads to a faster learning of the hybrid agent compared to the classical agent. We demonstrate that if the classical agent needs on average ⟨J⟩ rewards and ⟨T⟩cl epochs to learn how to solve the task, the hybrid agent will take ⟨ T ⟩ q ⩽ α s α o ⟨ T ⟩ c l ⟨ J ⟩ epochs on average. Here, α s and α o denote constants depending on details of the quantum search and are independent of the problem size. Additionally, we prove that if the environment allows for maximally α o k max sequential coherent interactions, e.g. due to noise effects, an improvement given by ⟨T⟩q ≈ α o ⟨T⟩cl/(4k max) is still possible.

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Parker, Amanda J., and Amanda S. Barnard. "Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks." Nanoscale Horizons 6, no. 3 (2021): 277–82. http://dx.doi.org/10.1039/d0nh00637h.

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Scientific intuition can help anticipate the outcome of experiments, but machine learning based on data does not always support these assumptions. A direct comparison of human intelligence (HI) and AI suggests domain knowledge is not always enough.

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Zhao, Wenlin, Yinuo Wang, Yingjie Qu, Hongyang Ma, and Shumei Wang. "Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm." Entropy 24, no. 12 (December 6, 2022): 1783. http://dx.doi.org/10.3390/e24121783.

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We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.

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Xue, Shichuan, Yizhi Wang, Yong Liu, Weixu Shi, and Junjie Wu. "Variational Quantum Process Tomography of Non-Unitaries." Entropy 25, no. 1 (January 1, 2023): 90. http://dx.doi.org/10.3390/e25010090.

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Quantum process tomography is a fundamental and critical benchmarking and certification tool that is capable of fully characterizing an unknown quantum process. Standard quantum process tomography suffers from an exponentially scaling number of measurements and complicated data post-processing due to the curse of dimensionality. On the other hand, non-unitary operators are more realistic cases. In this work, we put forward a variational quantum process tomography method based on the supervised quantum machine learning framework. It approximates the unknown non-unitary quantum process utilizing a relatively shallow depth parametric quantum circuit and fewer input states. Numerically, we verified our method by reconstructing the non-unitary quantum mappings up to eight qubits in two cases: the weighted sum of the randomly generated quantum circuits and the imaginary time evolution of the Heisenberg XXZ spin chain Hamiltonian. Results show that those quantum processes could be reconstructed with high fidelities (>99%) and shallow depth parametric quantum circuits (d≤8), while the number of input states required is at least two orders of magnitude less than the demands of the standard quantum process tomography. Our work shows the potential of the variational quantum process tomography method in characterizing non-unitary operators.

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Kong, Jian-Gang, Qing-Xu Li, Jian Li, Yu Liu, and Jia-Ji Zhu. "Self-Supervised Graph Neural Networks for Accurate Prediction of Néel Temperature." Chinese Physics Letters 39, no. 6 (June 1, 2022): 067503. http://dx.doi.org/10.1088/0256-307x/39/6/067503.

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Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. On the other hand, an accurate and efficient theoretical method is highly demanded for determining critical transition temperatures, Néel temperatures, of antiferromagnetic materials. The powerful graph neural networks (GNNs) that succeed in predicting material properties lose their advantage in predicting magnetic properties due to the small dataset of magnetic materials, while conventional machine learning models heavily depend on the quality of material descriptors. We propose a new strategy to extract high-level material representations by utilizing self-supervised training of GNNs on large-scale unlabeled datasets. According to the dimensional reduction analysis, we find that the learned knowledge about elements and magnetism transfers to the generated atomic vector representations. Compared with popular manually constructed descriptors and crystal graph convolutional neural networks, self-supervised material representations can help us to obtain a more accurate and efficient model for Néel temperatures, and the trained model can successfully predict high Néel temperature antiferromagnetic materials. Our self-supervised GNN may serve as a universal pre-training framework for various material properties.

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Md Ali, Mohd Adli, Nu’man Badrud’din, Hafidzul Abdullah, and Faiz Kemi. "Alternate methods for anomaly detection in high-energy physics via semi-supervised learning." International Journal of Modern Physics A 35, no. 23 (August 12, 2020): 2050131. http://dx.doi.org/10.1142/s0217751x20501316.

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Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.

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Nam, Chunghee. "Machine Learning Guided Prediction of Superhard Materials Based on Compositional Features." Korean Journal of Metals and Materials 60, no. 8 (August 5, 2022): 619–27. http://dx.doi.org/10.3365/kjmm.2022.60.8.619.

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In this study, the mechanical properties of materials were predicted using machine learning to search for superhard materials. Based on an AFOW database consisting of DFT quantum calculation values, the mechanical properties of materials were predicted using various machine learning models. For supervised learning, the entire data was divided into training data and test data at a ratio of 8:2. Since the discovery of superhard materials can be predicted based on the bulk modulus and shear modulus, the bulk modulus was primarily predicted using only the chemical compositional ratio (chemical formula), and then the shear modulus was obtained using the predicted bulk moduli. To obtain good prediction performance, cross-validation and hyper-parameter tuning were carried out. Each characteristic was predicted using XGBoost, one of the ensemble algorithms, and its performance was compared to the treebased machine learning of RandomForest and Support Vector Machine regression using the coefficient of determination (R2) and root-mean-square-error (RMSE) as metrics. For the recently introduced four superhard materials (Mo0.9W1.1BC, ReWC0.8, MoWC2, and ReWB), the results of this study were similar to those of previous studies including the experimental values or the DFT quantum calculations. The shear modulus was underpredicted, which can be understood since structural properties were not considering as a feature in our machine learning models.

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Darulová, J., M. Troyer, and M. C. Cassidy. "Evaluation of synthetic and experimental training data in supervised machine learning applied to charge-state detection of quantum dots." Machine Learning: Science and Technology 2, no. 4 (September 13, 2021): 045023. http://dx.doi.org/10.1088/2632-2153/ac104c.

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Parisi, Luca, Amir Zaernia, Renfei Ma, and Mansour Youseffi. "Ηyper-sinh-Convolutional Neural Network for Early Detection of Parkinson’s Disease from Spiral Drawings." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 9 (March 31, 2021): 1–7. http://dx.doi.org/10.37394/232018.2021.9.1.

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Modern developments in the state-of-the-art open-source activation functions for Convolutional Neural Networks (CNNs) have broadened the selection of benchmark activations for Deep Learning (DL)-aided classification. Nevertheless, achieving discrimination of non-linear input image data in CNN is still not straightforward and it is unclear how such novel activation functions can have translational applications with tangible impact. hyper-sinh, made freely available in TensorFlow and Keras, was demonstrated as a benchmark activation function on five (N=5) datasets in its ground-breaking paper. Measuring the value from deploying this activation in a specific application is pivotal to supply the required evidence of its performance on real-life supervised DL-based image classification tasks. In this study, a CNN was for the first time combined with hypersinh to aid early detection of Parkinson’s Disease (PD) from discriminating pathophysiological patterns extracted from spiral drawings. Thus, the hyper-sinh activation was deployed to maximise the separability of the input features from spiral drawings via automated pattern recognition. We demonstrate the accuracy and reliability of hyper-sinh-CNN to aid early diagnosis of PD, evaluated against other gold standard activation functions, including the recent Quantum ReLU (QReLU) and the modified Quantum ReLU (m-QReLU) that solved the ‘dying ReLU’ problem for the first time in the literature of DL. Two (N=2) benchmark datasets from the database of the Botucatu Medical School, São Paulo State University in Brazil, scaled to be in 28 by 28 pixels as the MNIST benchmark data, were used to discriminate between input image patterns of 158 subjects (53 healthy controls and 105 patients with PD) from spirals drawn on graphics tablets. Overtraining was avoided via early stopping and the models were developed and tested in TensorFlow and Keras (Python 3.6). The supervised model (hyper-sinh-CNN) could detect early Parkinson’s Disease with 81% and 91% classification accuracy from the two datasets respectively (F1-scores: 73% and 91% correspondingly). Furthermore, the model achieved high sensitivity (81% and 91%). Thus, this study validates the application of hyper-sinh to aid real-life supervised DL-based image classification, in particular early diagnosis of PD from spiral drawings.

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Hamdan, Baida Abdulredha. "Neural Network Principles and its Application." Webology 19, no. 1 (January 20, 2022): 3955–70. http://dx.doi.org/10.14704/web/v19i1/web19261.

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Neural networks which also known as artificial neural networks is generally a computing dependent technique that formed and designed to create a simulation to the real brain of a human to be used as a problem solving method. Artificial neural networks gain their abilities by the method of training or learning, each method have a certain input and output which called results too, this method of learning works to create forming probability-weighted associations among both of input and the result which stored and saved across the net specifically among its data structure, any training process is depending on identifying the net difference between processed output which is usually a prediction and the real targeted output which occurs as an error, then a series of adjustments achieved to gain a proper learning result, this process called supervised learning. Artificial neural networks have found and proved itself in many applications in a variety of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control (process control, vehicle control, quantum chemistry, trajectory prediction, and natural resource management. Etc.) In addition to face recognition which proved to be very effective. Neural network was proved to be a very promising technique in many fields due to its accuracy and problem solving properties.

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Kuzmenko, O., H. Yarovenko, and L. Skrynka. "ANALYSIS OF MATHEMATICAL MODELS FOR COUNTERING CYBER FRAUD IN BANKS." Vìsnik Sumsʹkogo deržavnogo unìversitetu 2022, no. 2 (2022): 111–20. http://dx.doi.org/10.21272/1817-9215.2022.2-13.

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The article is devoted to the current topic of analysis of mathematical models for countering cyber fraud in banks. This problem is due to the security risks growth in the banking system, which are formed by fraudsters' cyberattacks and cybercrimes implementation. Therefore, the priority task for cyberbanking security is the application of modern mathematical methods to analyse the sources of cyber attacks, identify threats and losses in the banking services market, identify cyber-attacks and assess the scenario of potential cyber risk, etc. The article analyses the most widespread types of cyber fraud: social engineering, phishing, stalking, farming, DoS attacks, online fraud, potentially unwanted programs, etc. The study also considered a model of cognitive computing and detection of suspicious transactions in banking cyber-physical systems based on quantum computing in BCPS for the post-quantum era. The advantages, disadvantages and results of the model are defined. Predictive modelling is proposed to detect fraud in real-time by analysing incoming bank transactions with payment cards. Within the framework of this method, such models are used for the classification of fraud detection as logistic regression, a decision tree, and a narrower technique - a random forest decision tree. The study also considered using the harmonic search algorithm in neural networks to improve fraud detection in the banking system. It is found that although this model has the advantage of learning ability based on past behaviour, there are difficulties in the long-term processing of many neural networks. The stages of model implementation are also given. In addition, the modelling of credit card fraud detection is based on using two types of models: supervised and unsupervised. Supervised models include logistic regression, K-nearest neighbours, and extreme gradient boosting. The one-class support vector model, restricted Boltzmann model, and generative-competitive network are considered among uncontrolled generative models.

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Kuhn, Stephen, Matthew J. Cracknell, Anya M. Reading, and Stephanie Sykora. "Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier." GEOPHYSICS 85, no. 6 (November 1, 2020): B249—B258. http://dx.doi.org/10.1190/geo2019-0461.1.

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Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and sampling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identified during conventional mapping and interpretation. We used random forests (RF), a supervised machine-learning algorithm, to classify rock types throughout the Kliyul porphyry prospect in British Columbia, Canada. Rock types determined at geochemical sampling sites were used as training data. Airborne magnetic and radiometric data, geochemistry, and topographic data were used in classification. Results were validated using First Quantum Minerals’ geologic map, which includes additional detail from targeted location and transect mapping. The petrophysical and compositional similarity of rock types resulted in a noisy classification. Intrusions, particularly the more discrete, were inconsistently predicted, likely due to their limited extent relative to data sampling intervals. Closer examination of class membership probabilities (CMPs) identified locations where the probability of an intrusion being present was elevated significantly above the background. Indeed, a large proportion of mapped intrusions correspond to areas of elevated probability and, importantly, areas were highlighted as potential intrusions that were not identified in geologic mapping. The RF classification produced a reasonable lithological map, if lacking in resolution, but more significantly, great benefit comes from the insights drawn from the RF CMPs. Mapping the spatial distribution of elevated intrusion CMP, a soft classifier approach, produced a map product that can target intrusions and prioritize detailed mapping for mineral exploration.

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Jaderberg, Ben, Lewis W. Anderson, Weidi Xie, Samuel Albanie, Martin Kiffner, and Dieter Jaksch. "Quantum Self-Supervised Learning." Quantum Science and Technology, April 19, 2022. http://dx.doi.org/10.1088/2058-9565/ac6825.

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Abstract The resurgence of self-supervised learning, whereby a deep learning model generates its own supervisory signal from the data, promises a scalable way to tackle the dramatically increasing size of real-world data sets without human annotation. However, the staggering computational complexity of these methods is such that for state-of-the-art performance, classical hardware requirements represent a signiﬁcant bottleneck to further progress. Here we take the ﬁrst steps to understanding whether quantum neural networks could meet the demand for more powerful architectures and test its eﬀectiveness in proof-of-principle hybrid experiments. Interestingly, we observe a numerical advantage for the learning of visual representations using small-scale quantum neural networks over equivalently structured classical networks, even when the quantum circuits are sampled with only 100 shots. Furthermore, we apply our best quantum model to classify unseen images on the ibmq_paris quantum computer and find that current noisy devices can already achieve equal accuracy to the equivalent classical model on downstream tasks.

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Monràs, Alex, Gael Sentís, and Peter Wittek. "Inductive Supervised Quantum Learning." Physical Review Letters 118, no. 19 (May 12, 2017). http://dx.doi.org/10.1103/physrevlett.118.190503.

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Saeedi, Seyran, Aliakbar Panahi, and Tom Arodz. "Quantum semi-supervised kernel learning." Quantum Machine Intelligence 3, no. 2 (October 7, 2021). http://dx.doi.org/10.1007/s42484-021-00053-x.

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Alvarez-Rodriguez, Unai, Lucas Lamata, Pablo Escandell-Montero, José D. Martín-Guerrero, and Enrique Solano. "Supervised Quantum Learning without Measurements." Scientific Reports 7, no. 1 (October 20, 2017). http://dx.doi.org/10.1038/s41598-017-13378-0.

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Zhou, Xiangzhen, Yuan Feng, and Sanjiang Li. "Supervised Learning Enhanced Quantum Circuit Transformation." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 1. http://dx.doi.org/10.1109/tcad.2022.3179223.

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Kulkarni, Viraj, Milind Kulkarni, and Aniruddha Pant. "Quantum computing methods for supervised learning." Quantum Machine Intelligence 3, no. 2 (September 6, 2021). http://dx.doi.org/10.1007/s42484-021-00050-0.

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Ciliberto, Carlo, Andrea Rocchetto, Alessandro Rudi, and Leonard Wossnig. "Statistical limits of supervised quantum learning." Physical Review A 102, no. 4 (October 28, 2020). http://dx.doi.org/10.1103/physreva.102.042414.

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Shin, S., Y. S. Teo, and H. Jeong. "Exponential data encoding for quantum supervised learning." Physical Review A 107, no. 1 (January 23, 2023). http://dx.doi.org/10.1103/physreva.107.012422.

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Hatakeyama-Sato, Kan, Yasuhiko Igarashi, Takahiro Kashikawa, Koichi Kimura, and Kenichi Oyaizu. "Quantum circuit learning as a potential algorithm to predict experimental chemical properties." Digital Discovery, 2022. http://dx.doi.org/10.1039/d2dd00090c.

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We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo- and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth...

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Adhikary, Soumik, Siddharth Dangwal, and Debanjan Bhowmik. "Supervised learning with a quantum classifier using multi-level systems." Quantum Information Processing 19, no. 3 (January 21, 2020). http://dx.doi.org/10.1007/s11128-020-2587-9.

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Journal articles on the topic 'Supervised quantum learning'

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