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Journal articles on the topic 'Quantum Learning'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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1

Dunjko, Vedran. "Quantum learning unravels quantum system." Science 376, no. 6598 (June 10, 2022): 1154–55. http://dx.doi.org/10.1126/science.abp9885.

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2

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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3

Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (May 2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.

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In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.
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4

Behrman, E. C., J. E. Steck, and M. A. Moustafa. "Learning quantum annealing." Quantum Information and Computation 17, no. 5&6 (April 2017): 460–87. http://dx.doi.org/10.26421/qic17.5-6-6.

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We propose and develop a new procedure, whereby a quantum system can learn to anneal to a desired ground state. We demonstrate successful learning to produce an entangled state for a two-qubit system, then demonstrate generalizability to larger systems. The amount of additional learning necessary decreases as the size of the system increases. Because current technologies limit measurement of the states of quantum annealing machines to determination of the average spin at each site, we then construct a “broken pathway” between the initial and desired states, at each step of which the average spins are nonzero, and show successful learning of that pathway. Using this technique we show we can direct annealing to multiqubit GHZ and W states, and verify that we have done so. Because quantum neural networks are robust to noise and decoherence we expect our method to be readily implemented experimentally; we show some preliminary results which support this.
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5

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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6

Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn. "Quantum Reinforcement Learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (October 2008): 1207–20. http://dx.doi.org/10.1109/tsmcb.2008.925743.

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7

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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8

Bisio, Alessandro, Giacomo Mauro DʼAriano, Paolo Perinotti, and Michal Sedlák. "Quantum learning algorithms for quantum measurements." Physics Letters A 375, no. 39 (September 2011): 3425–34. http://dx.doi.org/10.1016/j.physleta.2011.08.002.

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9

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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10

Lukac, Martin, and Marek Perkowski. "Inductive learning of quantum behaviors." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 561–86. http://dx.doi.org/10.2298/fuee0703561l.

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In this paper studied are new concepts of robotic behaviors - deterministic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not given as examples) are then converted not only to deterministic cares as in the classical learning, but also to output values generated with various probabilities. The Occam Razor principle, fundamental to inductive learning, is satisfied in this approach by seeking circuits of reduced complexity. This is illustrated by the synthesis of single output quantum circuits, as we extended the logic synthesis approach to Inductive Machine Learning for the case of learning quantum circuits from behavioral examples.
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11

Afidin, Devi, Fauzi Achmad, Kasturi Kasturi, Shirly Rizki Kusumaningrum, and Radeni Sukma Indra Dewi. "QUANTUM LEARNING MODEL TO INCREASE SCIENCE LEARNING ACTIVITIES." SENTRI: Jurnal Riset Ilmiah 1, no. 4 (December 2, 2022): 919–29. http://dx.doi.org/10.55681/sentri.v1i4.307.

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Based on the observation of student activity in grade 6 of elementary school as a whole during the process of learning science activities, there was a problem that science learning activities were still low. This is evidenced by observational data which shows that science learning activities only reach 40%. The low science learning activity is caused by several factors, including; (1) learning is still centered on the teacher (teacher center), (2) the model used by the teacher is the lecture model. The appropriate solution to this problem is to apply the Quantum Learning model . The teacher has applied the Quantum Learning model in science learning for 3 years. As long as using the Quantum Learning model in science learning, science learning activities are consistently obtained.
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12

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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13

Li, Yangyang, Mengzhuo Tian, Guangyuan Liu, Cheng Peng, and Licheng Jiao. "Quantum Optimization and Quantum Learning: A Survey." IEEE Access 8 (2020): 23568–93. http://dx.doi.org/10.1109/access.2020.2970105.

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14

Wiebe, Nathan, Christopher Granade, and D. G. Cory. "Quantum bootstrapping via compressed quantum Hamiltonian learning." New Journal of Physics 17, no. 2 (February 13, 2015): 022005. http://dx.doi.org/10.1088/1367-2630/17/2/022005.

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15

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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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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17

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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18

Bishwas, Arit Kumar, Ashish Mani, and Vasile Palade. "Gaussian kernel in quantum learning." International Journal of Quantum Information 18, no. 03 (April 2020): 2050006. http://dx.doi.org/10.1142/s0219749920500069.

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The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity using the quantum random access memory (QRAM) in the context of quantum SVM. Our analysis shows that the runtime computational complexity of the quantum Gaussian kernel is approximated to [Formula: see text] and even [Formula: see text] when [Formula: see text] and the error [Formula: see text] are small enough to be ignored, where [Formula: see text] is the dimension of the training instances, [Formula: see text] is the accuracy, [Formula: see text] is the dot product of the two quantum states, and [Formula: see text] is the Taylor remainder error term. Therefore, the run time complexity of the quantum version of the Gaussian kernel seems to be significantly faster when compared with its classical version.
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19

Wahyuni, Resty. "PENGARUH PENGGUNAAN STRATEGI QUANTUM LEARNING TERHADAP KEMAMPUAN SISWA DALAM SPEAKING." SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED 7, no. 4 (December 25, 2017): 419–22. http://dx.doi.org/10.24114/sejpgsd.v7i4.8093.

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20

Bonomi, Andrea, Thomas De Min, Enrico Zardini, Enrico Blanzieri, Valter Cavecchia, and Davide Pastorello. "Quantum annealing learning search implementations." Quantum Information and Computation 22, no. 3&4 (February 2022): 181–208. http://dx.doi.org/10.26421/qic22.3-4-1.

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This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019 as a novel technique to solve general QUBO problems that cannot be directly represented into the hardware architecture of a D-Wave machine. Repeated calls to the quantum machine within a classical iterative structure and a related convergence proof originate a learning mechanism to find an encoding of a given problem into the quantum architecture. The present work considers the Number Partitioning Problem (NPP) and the Travelling Salesman Problem (TSP) for the testing of QALS. The results turn out to be quite unexpected, with QALS not being able to perform as well as the other considered methods, especially in NPP, where classical methods outperform quantum annealing in general. Nevertheless, looking at the TSP tests, QALS has fulfilled its primary goal, i.e., processing QUBO problems not directly mappable to the QPU topology.
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21

Ganger, Michael, and Wei Hu. "Quantum Multiple Q-Learning." International Journal of Intelligence Science 09, no. 01 (2019): 1–22. http://dx.doi.org/10.4236/ijis.2019.91001.

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22

Wang, Jianwei, Stefano Paesani, Raffaele Santagati, Sebastian Knauer, Antonio A. Gentile, Nathan Wiebe, Maurangelo Petruzzella, et al. "Experimental quantum Hamiltonian learning." Nature Physics 13, no. 6 (March 13, 2017): 551–55. http://dx.doi.org/10.1038/nphys4074.

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23

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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24

McCoss, Angus. "Quantum Deep Learning Triuniverse." Journal of Quantum Information Science 06, no. 04 (2016): 223–48. http://dx.doi.org/10.4236/jqis.2016.64015.

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25

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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26

Rocchetto, Andrea, Scott Aaronson, Simone Severini, Gonzalo Carvacho, Davide Poderini, Iris Agresti, Marco Bentivegna, and Fabio Sciarrino. "Experimental learning of quantum states." Science Advances 5, no. 3 (March 2019): eaau1946. http://dx.doi.org/10.1126/sciadv.aau1946.

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The number of parameters describing a quantum state is well known to grow exponentially with the number of particles. This scaling limits our ability to characterize and simulate the evolution of arbitrary states to systems, with no more than a few qubits. However, from a computational learning theory perspective, it can be shown that quantum states can be approximately learned using a number of measurements growing linearly with the number of qubits. Here, we experimentally demonstrate this linear scaling in optical systems with up to 6 qubits. Our results highlight the power of the computational learning theory to investigate quantum information, provide the first experimental demonstration that quantum states can be “probably approximately learned” with access to a number of copies of the state that scales linearly with the number of qubits, and pave the way to probing quantum states at new, larger scales.
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27

Huang, Hsin-Yuan, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, et al. "Quantum advantage in learning from experiments." Science 376, no. 6598 (June 10, 2022): 1182–86. http://dx.doi.org/10.1126/science.abn7293.

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Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors.
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28

Lockwood, Owen, and Mei Si. "Reinforcement Learning with Quantum Variational Circuit." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 245–51. http://dx.doi.org/10.1609/aiide.v16i1.7437.

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The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.
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Cao, Chenfeng, Chao Zhang, Zipeng Wu, Markus Grassl, and Bei Zeng. "Quantum variational learning for quantum error-correcting codes." Quantum 6 (October 6, 2022): 828. http://dx.doi.org/10.22331/q-2022-10-06-828.

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Quantum error correction is believed to be a necessity for large-scale fault-tolerant quantum computation. In the past two decades, various constructions of quantum error-correcting codes (QECCs) have been developed, leading to many good code families. However, the majority of these codes are not suitable for near-term quantum devices. Here we present VarQEC, a noise-resilient variational quantum algorithm to search for quantum codes with a hardware-efficient encoding circuit. The cost functions are inspired by the most general and fundamental requirements of a QECC, the Knill-Laflamme conditions. Given the target noise channel (or the target code parameters) and the hardware connectivity graph, we optimize a shallow variational quantum circuit to prepare the basis states of an eligible code. In principle, VarQEC can find quantum codes for any error model, whether additive or non-additive, degenerate or non-degenerate, pure or impure. We have verified its effectiveness by (re)discovering some symmetric and asymmetric codes, e.g., ((n,2n−6,3))2 for n from 7 to 14. We also found new ((6,2,3))2 and ((7,2,3))2 codes that are not equivalent to any stabilizer code, and extensive numerical evidence with VarQEC suggests that a ((7,3,3))2 code does not exist. Furthermore, we found many new channel-adaptive codes for error models involving nearest-neighbor correlated errors. Our work sheds new light on the understanding of QECC in general, which may also help to enhance near-term device performance with channel-adaptive error-correcting codes.
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30

Fatimah, Andriani, Ulfiani Rahman, and Andi Ika Prasasti. "Memahami Konsep Matematika dengan Quantum Learning dan Quantum Teaching." PUSAKA 6, no. 2 (November 1, 2018): 211–18. http://dx.doi.org/10.31969/pusaka.v6i2.58.

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Artikel ini membahas tentang perbandingan penerapan model pembelajaran Quantum Learning dan Quantum Teaching terhadap kemampuan pemahaman konsep matematika siswa kelas VIII SMP IT Wahdah Islamiyah Makassar. Penelitian ini bertujuan untuk (1) mengetahui kemampuan pemahaman konsep matematika siswa yang diajar dengan menerapkan model pembelajaran Quantum Learning; (2) mengetahui kemampuan pemahaman konsep matematika siswa yang diajar dengan menerapkan model pembelajaran Quantum Teaching; (3) mengetahui perbedaan yang signifikan antara kemampuan pemahaman konsep matematika melalui penerapan model pembelajaran Quantum Learning dengan model pembelajaran Quantum Teaching. Pendekatan penelitian ini adalah kuantitatif dengan jenis Quasi Experiment dengan design Nonequivalent Control Group Design. Sampel pada penelitian ini adalah siswa Kelas VIII B2 dan Kelas VIII B3 yaitu 60 orang dari jumlah keseluruhan siswa kelas VIII 120 orang. Instrumen penelitian yang digunakan adalah soal tes kemampuan pemahaman konsep matematika, lembar observasi dan dokumentasi. Data dianalisis dengan analisis statistik deskriptif dan statistik inferensial. Hasil penelitian menunjukkan bahwa (1) kemampuan pemahaman konsep matematika siswa yang diajar dengan menerapkan model pembelajaran Quantum Learning mengalami peningkatan; (2) kemampuan pemahaman konsep matematika siswa yang diajar dengan menerapkan model pembelajaran Quantum Teaching mengalami peningkatan; (3) terdapat perbedaan yang signifikan antara kemampuan pemahaman konsep matematika melalui penerapan model pembelajaran Quantum Learning dengan model pembelajaran Quantum Teaching. Kata Kunci: Kemampuan Pemahaman Konsep Matematika, Quantum Learning, Quantum Teaching
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31

Muga, Wilfridus. "VIDEO ASSISTED QUANTUM LEARNING DESIGN TO IMPROVE PSYCHOMOTORIC LEARNING ACHIEVEMENT." Journal of Education Technology 1, no. 1 (May 3, 2017): 30. http://dx.doi.org/10.23887/jet.v1i1.10081.

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This paper aims at investigating how video-assisted quantum learning design improves the learning achievement on psychomotor aspect. This paper is constructed of review of related literature, deep investigation on journal articles and related empirical studies. Quantum teaching is an instructional design which integrates arts and feasible goals in all subjects. Quantum teaching is a shift in learning condition where interaction and interrelationship are used to maximize learning condition. In its relation to improve learning desire, a motivated and interesting media is highly needed. Video as media is integrated in the quantum strategy to maximize learning achievement particularly on the psychomotor aspect. Video contains audio, visual, and messages in form of concepts, principle, procedure, theories, application to help learners understanding a particular topic. These forms of messages are all delivered through the audio and visualisation simultaneously.
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32

Hu, Ling, Shu-Hao Wu, Weizhou Cai, Yuwei Ma, Xianghao Mu, Yuan Xu, Haiyan Wang, et al. "Quantum generative adversarial learning in a superconducting quantum circuit." Science Advances 5, no. 1 (January 2019): eaav2761. http://dx.doi.org/10.1126/sciadv.aav2761.

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Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum-state generator can be trained to replicate the statistics of the quantum data output from a quantum channel simulator, with a high fidelity (98.8% on average) so that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate–scale quantum devices.
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33

Yang, Feidiao, Jiaqing Jiang, Jialin Zhang, and Xiaoming Sun. "Revisiting Online Quantum State Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6607–14. http://dx.doi.org/10.1609/aaai.v34i04.6136.

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In this paper, we study the online quantum state learning problem which is recently proposed by Aaronson et al. (2018). In this problem, the learning algorithm sequentially predicts quantum states based on observed measurements and losses and the goal is to minimize the regret. In the previous work, the existing algorithms may output mixed quantum states. However, in many scenarios, the prediction of a pure quantum state is required. In this paper, we first propose a Follow-the-Perturbed-Leader (FTPL) algorithm that can guarantee to predict pure quantum states. Theoretical analysis shows that our algorithm can achieve an O(√T) expected regret under some reasonable settings. In the case that the pure state prediction is not mandatory, we propose another deterministic learning algorithm which is simpler and more efficient. The algorithm is based on the online gradient descent (OGD) method and can also achieve an O(√T) regret bound. The main technical contribution of this result is an algorithm of projecting an arbitrary Hermitian matrix onto the set of density matrices with respect to the Frobenius norm. We think this subroutine is of independent interest and can be widely used in many other problems in the quantum computing area. In addition to the theoretical analysis, we evaluate the algorithms with a series of simulation experiments. The experimental results show that our FTPL method and OGD method outperform the existing RFTL approach proposed by Aaronson et al. (2018) in almost all settings. In the implementation of the RFTL approach, we give a closed-form solution to the algorithm. This provides an efficient, accurate, and completely executable solution to the RFTL method.
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34

Fakhrurrazi, Muhammad, Mohammad Masykuri, and Sarwanto Sarwanto. "Improve Student Learning Outcomes through the Development of Quantum Learning-Based Learning Instruments on Hydrocarbon and Petroleum." Jurnal Penelitian Pendidikan IPA 8, no. 3 (July 31, 2022): 1204–14. http://dx.doi.org/10.29303/jppipa.v8i3.1495.

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The study aims to develop a quantum learning-based learning instrument on hydrocarbon and petroleum; determine the feasibility of quantum learning-based learning instrument on hydrocarbon and petroleum, and determine the effectiveness of quantum learning-based learning instrument on hydrocarbon and petroleum to improve student's learning outcomes. This type of research is research and development (R&D) using the ADDIE model (analysis, design, development, implementation, and evaluation). The product developed consists of a syllabus, lesson plans, teaching materials, and assessment instruments. The validation results were analyzed using Aiken's validation obtained a validity coefficient of more than 0.80 or categorized as valid and suitable for use. The effectiveness test results were analyzed using SPSS22 obtained t-count > t-table, so it could conclude that quantum learning-based learning tools on hydrocarbon and petroleum materials were effective for improving student learning outcomes.
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35

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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36

Kappen, H. J. "Learning quantum models from quantum or classical data." Journal of Physics A: Mathematical and Theoretical 53, no. 21 (May 12, 2020): 214001. http://dx.doi.org/10.1088/1751-8121/ab7df6.

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37

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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38

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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39

Gosdzinsky, P., and R. Tarrach. "Learning quantum field theory from elementary quantum mechanics." American Journal of Physics 59, no. 1 (January 1991): 70–74. http://dx.doi.org/10.1119/1.16691.

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40

Bromley, Thomas R., and Patrick Rebentrost. "Batched quantum state exponentiation and quantum Hebbian learning." Quantum Machine Intelligence 1, no. 1-2 (May 15, 2019): 31–40. http://dx.doi.org/10.1007/s42484-019-00002-9.

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41

Panella, Massimo, and Giuseppe Martinelli. "Neural networks with quantum architecture and quantum learning." International Journal of Circuit Theory and Applications 39, no. 1 (January 2011): 61–77. http://dx.doi.org/10.1002/cta.619.

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42

Chen, Bu-Qing, and Xu-Feng Niu. "Quantum Neural Network with Improved Quantum Learning Algorithm." International Journal of Theoretical Physics 59, no. 7 (May 15, 2020): 1978–91. http://dx.doi.org/10.1007/s10773-020-04470-9.

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43

Purwanto, Setyoadi. "UNSUR PEMBELAJARAN EDUTAINMENT DALAM QUANTUM LEARNING." Al-Fikri: Jurnal Studi dan Penelitian Pendidikan Islam 2, no. 2 (September 2, 2019): 21. http://dx.doi.org/10.30659/jspi.v2i2.5149.

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Early childhood learning uses the principle of playing while learning so that it must be arranged so that it is fun, uplifting, and democratic. Quantum Learning is a learning method that is loaded with edutainment concepts. Based on the studies conducted, the following conclusions are obtained: (1) Quantum learning and edutainment both emphasize the urgency of a pleasant and easy atmosphere; (2) Equally starting from the world of non-education; (3) Both give primary attention to an exciting learning process, which involves physical-sensory, emotional, and suggestive beliefs; (4) Quantum learning and edutaintment are able to foster student confidence; (5) Quantum learning builds student learning skills through a variety of cutting-edge discoveries, and edutainment continually seeks to facilitate optimal learning processes; (6) Quantum learning and edutaintment simultaneously build effective communication to students.�Keywords: learning, quantum learning, edutaintmen
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44

Kusuma, Adevia Indah, and Diana Pramesti. "Teacherpreneur Learning Model: Model Pembelajaran Kewirausahaan Berbasis Quantum Learning." EDUKATIF : JURNAL ILMU PENDIDIKAN 3, no. 6 (October 27, 2021): 4913–28. http://dx.doi.org/10.31004/edukatif.v3i6.1572.

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Berdasarkan analisis kebutuhan dilakukannya pengembangan maka tujuan penelitian ini adalah mengembangkan model pembelajaran kewirausahaan berbasis quantum learning untuk menghasilkan karakter guru berjiwa kewirausahaan yang disebut dengan Teacherpreneur. Metode penelitian yang digunakan adalah langkah pengembangan ADDIE. Subjek penelitian terdiri dari validator dan respon pengguna. Model pembelajaran kewirausahaan berbasis Quantum Learning ini diberi nama Teacherpreneur Learning Model (TLM) dengan tujuh sintak (Potential, Intuitive, Conceptual, Management, Sustainable, Collaborative, dan Sharing and Caring) dilengkapi indikatornya. Berdasarkan hasil ujicoba kelayakan dan respon pengguna diketahui bahwa model TLM layak digunakan dalam proses pembelajaran. Hasil observasi menggambarkan bahwa mahasiswa mampu mengikuti sintak yang diinstruksikan serta menghasilkan kepercayaan diri terhadap kemampuannya.
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45

Chen, Yiwei, Yu Pan, Guofeng Zhang, and Shuming Cheng. "Detecting quantum entanglement with unsupervised learning." Quantum Science and Technology 7, no. 1 (November 3, 2021): 015005. http://dx.doi.org/10.1088/2058-9565/ac310f.

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Abstract Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.
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46

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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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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Chen, Samuel Yen-Chi, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, and Ying-Jer Kao. "Variational quantum reinforcement learning via evolutionary optimization." Machine Learning: Science and Technology 3, no. 1 (February 15, 2022): 015025. http://dx.doi.org/10.1088/2632-2153/ac4559.

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Abstract Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL in the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.
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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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Zayadi, Ach. "Quantum Learning dalam Perspektif Pendidikan Islam." Hikmah: Journal of Islamic Studies 13, no. 1 (May 15, 2017): 115. http://dx.doi.org/10.47466/hikmah.v13i1.84.

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The learning approach with the concept of quantum learning is a concept holding a strong philosophy that learning is a lifelong activity, which is implemented in a fun way at a class and presented with a methodology that is based on a curriculum as a blend of academic skills, physical achievements, and life skills. At a glance, this quantum approach is quite relevant to the lifelong educational philosophy that became the foundation of Islamic Education. The nature of education of Qur’an (Islam) is “rabbany” based on the first verse in the first revelation. People who carry out are also called “rabbany” who are described by al- Quran with the characteristics include teaching the book of Allah, both written (Al Qur’an) and the unwritten (the universe), and study of it continuously. This literature review will reveal another perspective on the quantum approach in optical studies of Islam. Keywords: Quantum Learning, Islamic education Pendekatan pembelajaran dengan konsep quantum learning adalah sebuah konsep yang memegang kuat falsafah bahwa belajar adalah kegiatan seumur hidup, diimplementasikan dalam kelas dengan cara yang menyenangkan, serta disajikan pula dengan metodologi yang didasarkan pada kurikulum yang merupakan perpaduan antara keterampilan akademis, prestasi fisik, dan keterampilan hidup (life skills). Sekilas pendekatan quantum ini sangatlah relevan dengan falsafah pendidikan sepanjang hayat yang menjadi pijakan dalam pendidikan Islam. Sifat pendidikan al-Qur’an (Islam) adalah “rabbany” berdasarkan ayat yang pertama dalam wahyu pertama. Orang yang melaksanakan juga disebut “rabbany” yang oleh al-Qur’an dijelaskan cirinya antara lain mengajarkan kitab Allah, baik yang tertulis (al-Qur’an) maupun yang tidak tertulis (alam raya) serta mempelajarinya secara terus menerus. Kajian literatur ini akan menguak persfektif lain tentang pendekatan quantum dalam optik kajian Islam. Kata Kunci: Quantum Learning, Pendidikan Islam
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