Готові списки джерел за темами / Supervised quantum learning

Добірка наукової літератури з теми "Supervised quantum learning"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Supervised quantum learning"

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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Анотація:

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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Більше джерел

Дисертації з теми "Supervised quantum learning"

Cangini, Nicolò. "Quantum Supervised Learning: Algoritmi e implementazione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17694/.

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Il Quantum Computing non riguarda più soltanto la scienza della Fisica, negli ultimi anni infatti la ricerca in questo campo ha subito una notevole espansione dimostrando l'enorme potenziale di cui dispongono questi nuovi calcolatori che in un futuro prossimo potranno rivoluzionare il concetto di Computer Science così come lo conosciamo. Ad oggi, siamo già in grado di realizzare algoritmi su piccola scala eseguibili in un quantum device grazie ai quali è possibile sperimentare uno speed-up notevole (in alcuni casi esponenziale) su diversi task tipici della computazione classica. In questo elaborato vengono discusse le basi del Quantum Computing, con un focus particolare sulla possibilità di eseguire alcuni algoritmi supervisionati di Machine Learning in un quantum device per ottenere uno speed-up sostanziale nella fase di training. Oltre che una impostazione teorica del problema, vengono effettuati diversi esperimenti utilizzando le funzionalità dell'ambiente Qiskit, grazie al quale è possibile sia simulare il comportamento di un computer quantistico in un calcolatore classico, sia eseguirlo in cloud sui computer messi a disposizione da IBM.

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Книги з теми "Supervised quantum learning"

Schuld, Maria, and Francesco Petruccione. Supervised Learning with Quantum Computers. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9.

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Petruccione, Francesco, and Maria Schuld. Supervised Learning with Quantum Computers. Springer, 2019.

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Petruccione, Francesco, and Maria Schuld. Supervised Learning with Quantum Computers. Springer, 2018.

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Частини книг з теми "Supervised quantum learning"

Nivelkar, Mukta, and S. G. Bhirud. "Supervised Machine Learning Strategies for Investigation of Weird Pattern Formulation from Large Volume Data Using Quantum Computing." In Advanced Computing and Intelligent Technologies, 569–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2164-2_45.

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Wittek, Peter. "Supervised Learning and Support Vector Machines." In Quantum Machine Learning, 73–84. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-800953-6.00007-4.

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Oneto, L., S. Ridella, and D. Anguita. "Quantum computing and supervised machine learning." In Quantum Inspired Computational Intelligence, 33–83. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-804409-4.00002-4.

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Di Ventra, Massimiliano. "Application to Machine Learning and Quantum Mechanics." In MemComputing, 180–222. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845320.003.0010.

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Анотація:

This Chapter shows how to employ MemComputing in the field of Machine Learning and Quantum Mechanics. It demonstrates that both the supervised and unsupervised training of neural networks can be improved substantially. It also shows a possible way to use MemComputing to find the ground state of quantum Hamiltonians and the reconstruction of quantum states (quantum tomography).

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Тези доповідей конференцій з теми "Supervised quantum learning"

Banchi, Leonardo, and Stefano Pirandola. "Supervised Quantum Learning as Quantum Channel Simulation." In Quantum Information and Measurement. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/qim.2019.s4b.5.

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Zhuang, Quntao, and Zheshen Zhang. "Entanglement-Enhanced Physical-Layer Classifier Using Supervised Machine Learning." In Quantum 2.0. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/quantum.2020.qth6a.8.

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Innocenti, Luca, Leonardo Banchi, Alessandro Ferraro, Sougato Bose, and Mauro Paternostro. "Supervised learning of time-independent Hamiltonians for gate design." In Quantum Information and Measurement. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/qim.2019.f5a.28.

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Coulibaly, S., F. Bessin, M. G. Clerc, and A. Mussot. "Forecasting turbulence in a passive resonator with supervised machine learning." In 2021 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC). IEEE, 2021. http://dx.doi.org/10.1109/cleo/europe-eqec52157.2021.9542017.

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Hossain, Muhammad Minoar, Mohammed Sowket Ali, Reshma Ahmed Swarna, Md Mahmodul Hasan, Nahida Habib, Md Wahidur Rahman, Mir Mohammad Azad, and Mohammad Motiur Rahman. "Analyzing the effect of feature mapping techniques along with the circuit depth in quantum supervised learning by utilizing quantum support vector machine." In 2021 24th International Conference on Computer and Information Technology (ICCIT). IEEE, 2021. http://dx.doi.org/10.1109/iccit54785.2021.9689853.

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Звіти організацій з теми "Supervised quantum learning"

Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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Анотація:

We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.

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