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Journal articles on the topic 'Quantum machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 March 2023

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1

Chiara, Maria Luisa Dalla, Roberto Giuntini, Giuseppe Sergioli, and Roberto Leporini. "Abstract quantum computing machines and quantum computational logics." International Journal of Quantum Information 14, no. 04 (June 2016): 1640019. http://dx.doi.org/10.1142/s0219749916400190.

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Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.

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2

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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TARASOV, VASILY E. "QUANTUM NANOTECHNOLOGY." International Journal of Nanoscience 08, no. 04n05 (August 2009): 337–44. http://dx.doi.org/10.1142/s0219581x09005517.

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Nanotechnology is based on manipulations of individual atoms and molecules to build complex atomic structures. Quantum nanotechnology is a broad concept that deals with a manipulation of individual quantum states of atoms and molecules. Quantum nanotechnology differs from nanotechnology as a quantum computer differs from a classical molecular computer. The nanotechnology deals with a manipulation of quantum states in bulk rather than individually. In this paper, we define the main notions of quantum nanotechnology. Quantum analogs of assemblers, replicators and self-reproducing machines are discussed. We prove the possibility of realizing these analogs. A self-cloning (self-reproducing) quantum machine is a quantum machine which can make a copy of itself. The impossibility of ideally cloning an unknown quantum state is one of the basic rules of quantum theory. We prove that quantum machines cannot be self-cloning if they are Hamiltonian. There exist quantum non-Hamiltonian machines that are self-cloning machines. Quantum nanotechnology allows us to build quantum nanomachines. These nanomachines are not only small machines of nanosize. Quantum nanomachines should use new (quantum) principles of work.

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Peleshenko, Vitaly A. "QUANTUM MACHINE LEARNING." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 82–107. http://dx.doi.org/10.36871/2618-9976.2022.11.008.

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5

YU, YANG, and LIU YE. "PROPOSAL FOR A GENERAL QUANTUM CLONING MACHINE VIA DISTANT QUBITS IN A QUANTUM NETWORK." International Journal of Modern Physics B 27, no. 23 (August 21, 2013): 1350154. http://dx.doi.org/10.1142/s0217979213501543.

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We propose a scheme for implementing a general quantum cloning machine with distant qubits. By regulating some times and phases, we can easily realize almost all kinds of quantum cloning machines (optimal phase-covariant cloning, optimal universal quantum cloning, optimal economical phase-covariant cloning and optimal real state cloning). The quantum cloning machine may have important applications in quantum cryptography for quantum network.

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6

CASTAGNOLI, GIUSEPPE. "QUANTUM STEADY COMPUTATION." International Journal of Modern Physics B 05, no. 13 (August 10, 1991): 2253–69. http://dx.doi.org/10.1142/s0217979291000870.

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Current conceptions of "quantum mechanical computers" inherit from conventional digital machines two apparently interacting features, machine imperfection and temporal development of the computational process. On account of machine imperfection, the process would become ideally reversible only in the limiting case of zero speed. Therefore the process is irreversible in practice and cannot be considered to be a fundamental quantum one. By giving up classical features and using a linear, reversible and non-sequential representation of the computational process — not realizable in classical machines — the process can be identified with the mathematical form of a quantum steady state. This form of steady quantum computation would seem to have an important bearing on the notion of cognition.

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7

Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. "Quantum machine learning." Nature 549, no. 7671 (September 2017): 195–202. http://dx.doi.org/10.1038/nature23474.

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8

Acampora, Giovanni. "Quantum machine intelligence." Quantum Machine Intelligence 1, no. 1-2 (May 15, 2019): 1–3. http://dx.doi.org/10.1007/s42484-019-00006-5.

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9

Allcock, Jonathan, and Shengyu Zhang. "Quantum machine learning." National Science Review 6, no. 1 (November 30, 2018): 26–28. http://dx.doi.org/10.1093/nsr/nwy149.

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10

Molina, Abel, and John Watrous. "Revisiting the simulation of quantum Turing machines by quantum circuits." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2226 (June 2019): 20180767. http://dx.doi.org/10.1098/rspa.2018.0767.

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Yao's 1995 publication ‘Quantum circuit complexity’ in Proceedings of the 34th Annual IEEE Symposium on Foundations of Computer Science , pp. 352–361, proved that quantum Turing machines and quantum circuits are polynomially equivalent computational models: t ≥ n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t , and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time. We revisit the simulation of quantum Turing machines with uniformly generated quantum circuits, which is the more challenging of the two simulation tasks, and present a variation on the simulation method employed by Yao together with an analysis of it. This analysis reveals that the simulation of quantum Turing machines can be performed by quantum circuits having depth linear in t , rather than quadratic depth, and can be extended to variants of quantum Turing machines, such as ones having multi-dimensional tapes. Our analysis is based on an extension of method described by Arright, Nesme and Werner in 2011 in Journal of Computer and System Sciences 77 , 372–378. ( doi:10.1016/j.jcss.2010.05.004 ), that allows for the localization of causal unitary evolutions.

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11

Chen, Samuel Yen-Chi, and Shinjae Yoo. "Federated Quantum Machine Learning." Entropy 23, no. 4 (April 13, 2021): 460. http://dx.doi.org/10.3390/e23040460.

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Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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12

Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (January 28, 2021): 33. http://dx.doi.org/10.3390/photonics8020033.

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Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

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13

Roggero, Alessandro, Jakub Filipek, Shih-Chieh Hsu, and Nathan Wiebe. "Quantum Machine Learning with SQUID." Quantum 6 (May 30, 2022): 727. http://dx.doi.org/10.22331/q-2022-05-30-727.

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In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

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14

Guts, А. К. "THE QUANTUM TIME MACHINE." SPACE, TIME AND FUNDAMENTAL INTERACTIONS 3, no. 28 (2019): 20–44. http://dx.doi.org/10.17238/issn2226-8812.2019.3.20-44.

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15

Suter, Dieter, Matthias Ernst, and Richard R. Ernst. "Quantum time-translation machine." Molecular Physics 78, no. 1 (January 1993): 95–102. http://dx.doi.org/10.1080/00268979300100091.

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16

Cho, A. "The First Quantum Machine." Science 330, no. 6011 (December 16, 2010): 1604. http://dx.doi.org/10.1126/science.330.6011.1604.

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17

Mullins, Justin. "The quantum time machine." New Scientist 208, no. 2787 (November 2010): 34–37. http://dx.doi.org/10.1016/s0262-4079(10)62886-2.

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18

Sudbery, Tony. "A quantum time machine." Nature 346, no. 6286 (August 1990): 699–700. http://dx.doi.org/10.1038/346699a0.

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19

Adhikari, S., A. K. Pati, I. Chakrabarty, and B. S. Choudhury. "Hybrid Quantum Cloning Machine." Quantum Information Processing 6, no. 4 (June 20, 2007): 197–219. http://dx.doi.org/10.1007/s11128-007-0053-6.

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20

Pudenz, Kristen L., and Daniel A. Lidar. "Quantum adiabatic machine learning." Quantum Information Processing 12, no. 5 (November 21, 2012): 2027–70. http://dx.doi.org/10.1007/s11128-012-0506-4.

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21

Vaidman, Lev. "A quantum time machine." Foundations of Physics 21, no. 8 (August 1991): 947–58. http://dx.doi.org/10.1007/bf00733217.

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22

Saini, Shivani, PK Khosla, Manjit Kaur, and Gurmohan Singh. "Quantum Driven Machine Learning." International Journal of Theoretical Physics 59, no. 12 (December 2020): 4013–24. http://dx.doi.org/10.1007/s10773-020-04656-1.

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23

Fung, Fred. "QUANTUM SOFTWARE: Quantum Machine Learning in Telecommunication." Digitale Welt 6, no. 2 (March 12, 2022): 30–31. http://dx.doi.org/10.1007/s42354-022-0472-7.

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24

Hansson, Johan. ""Quantum machine" to solve quantum "measurement problem"?" Advanced Studies in Theoretical Physics 9 (2015): 233–36. http://dx.doi.org/10.12988/astp.2015.5113.

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25

Cárdenas‐López, Francisco A., Mikel Sanz, Juan Carlos Retamal, and Enrique Solano. "Enhanced Quantum Synchronization via Quantum Machine Learning." Advanced Quantum Technologies 2, no. 7-8 (January 7, 2019): 1800076. http://dx.doi.org/10.1002/qute.201800076.

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26

Lamata, Lucas, Mikel Sanz, and Enrique Solano. "Quantum Machine Learning and Bioinspired Quantum Technologies." Advanced Quantum Technologies 2, no. 7-8 (August 2019): 1900075. http://dx.doi.org/10.1002/qute.201900075.

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27

Ayoade, Olawale, Pablo Rivas, and Javier Orduz. "Artificial Intelligence Computing at the Quantum Level." Data 7, no. 3 (February 25, 2022): 28. http://dx.doi.org/10.3390/data7030028.

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The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.

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28

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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29

Ciliberto, Carlo, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, and Leonard Wossnig. "Quantum machine learning: a classical perspective." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2209 (January 2018): 20170551. http://dx.doi.org/10.1098/rspa.2017.0551.

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Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

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30

Matsui, Nobuyuki. "Quantum Neural Network: Prospects for Quantum Machine Learning." Journal of The Japan Institute of Electronics Packaging 23, no. 2 (March 1, 2020): 139–44. http://dx.doi.org/10.5104/jiep.23.139.

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31

Lamata, Lucas. "Quantum machine learning and quantum biomimetics: A perspective." Machine Learning: Science and Technology 1, no. 3 (July 22, 2020): 033002. http://dx.doi.org/10.1088/2632-2153/ab9803.

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32

Hardy, Lucien, and Adam G. M. Lewis. "Quantum computation with machine-learning-controlled quantum stuff." Machine Learning: Science and Technology 2, no. 1 (December 9, 2020): 015008. http://dx.doi.org/10.1088/2632-2153/abb215.

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33

Dan-Dan, Lv, Lu Hong, Yu Ya-Fei, Feng Xun-Li, and Zhang Zhi-Ming. "Universal Quantum Cloning Machine in Circuit Quantum Electrodynamics." Chinese Physics Letters 27, no. 2 (February 2010): 020302. http://dx.doi.org/10.1088/0256-307x/27/2/020302.

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34

Fabrizio, Alberto, Benjamin Meyer, Raimon Fabregat, and Clemence Corminboeuf. "Quantum Chemistry Meets Machine Learning." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 983–89. http://dx.doi.org/10.2533/chimia.2019.983.

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In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

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35

Spagnolo, Nicolò, Alessandro Lumino, Emanuele Polino, Adil S. Rab, Nathan Wiebe, and Fabio Sciarrino. "Machine Learning for Quantum Metrology." Proceedings 12, no. 1 (August 23, 2019): 28. http://dx.doi.org/10.3390/proceedings2019012028.

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Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach such bound when only a small number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limited number of photons is employed.

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36

BANG, Jeongho. "Machine Learning and Quantum Algorithm." Physics and High Technology 26, no. 12 (December 30, 2017): 25–29. http://dx.doi.org/10.3938/phit.26.048.

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37

Wang, Bingjie. "Quantum algorithms for machine learning." XRDS: Crossroads, The ACM Magazine for Students 23, no. 1 (September 20, 2016): 20–24. http://dx.doi.org/10.1145/2983535.

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38

Carrasquilla, Juan. "Machine learning for quantum matter." Advances in Physics: X 5, no. 1 (January 1, 2020): 1797528. http://dx.doi.org/10.1080/23746149.2020.1797528.

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39

Fang, Bao-Long, Gui-Yu Hu, and Liu Ye. "All-purpose quantum cloning machine." Journal of Physics B: Atomic, Molecular and Optical Physics 42, no. 7 (March 20, 2009): 075501. http://dx.doi.org/10.1088/0953-4075/42/7/075501.

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40

Ponomarev, Alexey V., Sergey Denisov, and Peter Hänggi. "Quantum Machine Using Cold Atoms." Journal of Computational and Theoretical Nanoscience 7, no. 11 (November 1, 2010): 2441–47. http://dx.doi.org/10.1166/jctn.2010.1631.

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41

Das Sarma, Sankar, Dong-Ling Deng, and Lu-Ming Duan. "Machine learning meets quantum physics." Physics Today 72, no. 3 (March 2019): 48–54. http://dx.doi.org/10.1063/pt.3.4164.

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42

Khan, Tariq M., and Antonio Robles-Kelly. "Machine Learning: Quantum vs Classical." IEEE Access 8 (2020): 219275–94. http://dx.doi.org/10.1109/access.2020.3041719.

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43

Stajic, Jelena. "Machine learning and quantum physics." Science 355, no. 6325 (February 9, 2017): 591.15–593. http://dx.doi.org/10.1126/science.355.6325.591-o.

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44

Schuld, Maria. "Machine learning in quantum spaces." Nature 567, no. 7747 (March 2019): 179–81. http://dx.doi.org/10.1038/d41586-019-00771-0.

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45

Sheng, Yu-Bo, and Lan Zhou. "Distributed secure quantum machine learning." Science Bulletin 62, no. 14 (July 2017): 1025–29. http://dx.doi.org/10.1016/j.scib.2017.06.007.

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46

Sheng-Xing, Zhang, Long Gui-Lu, and Liu Xiao-Shu. "A Remote Quantum Adding Machine." Chinese Physics Letters 19, no. 11 (November 2002): 1579–80. http://dx.doi.org/10.1088/0256-307x/19/11/303.

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47

Hush, Michael R. "Machine learning for quantum physics." Science 355, no. 6325 (February 9, 2017): 580. http://dx.doi.org/10.1126/science.aam6564.

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48

Melnikov, Alexey A., Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, and Hans J. Briegel. "Active learning machine learns to create new quantum experiments." Proceedings of the National Academy of Sciences 115, no. 6 (January 18, 2018): 1221–26. http://dx.doi.org/10.1073/pnas.1714936115.

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How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments—a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

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49

Miszczak, J. "Models of quantum computation and quantum programming languages." Bulletin of the Polish Academy of Sciences: Technical Sciences 59, no. 3 (September 1, 2011): 305–24. http://dx.doi.org/10.2478/v10175-011-0039-5.

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Models of quantum computation and quantum programming languagesThe goal of the presented paper is to provide an introduction to the basic computational models used in quantum information theory. We review various models of quantum Turing machine, quantum circuits and quantum random access machine (QRAM) along with their classical counterparts. We also provide an introduction to quantum programming languages, which are developed using the QRAM model. We review the syntax of several existing quantum programming languages and discuss their features and limitations.

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50

Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (February 2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs are trained more effectively than restricted Boltzmann machines (RBM) with the same number of nodes. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This method also outperforms the reinforcement learning method that uses RBMs.

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