Relevant bibliographies by topics / Quantum circuit learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Quantum circuit learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Quantum circuit learning"

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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 wit
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Menegasso Pires, Otto, Eduardo Inacio Duzzioni, Jerusa Marchi, and Rafael De Santiago. "Quantum Circuit Synthesis Using Projective Simulation." Inteligencia Artificial 24, no. 67 (2021): 90–101. http://dx.doi.org/10.4114/intartif.vol24iss67pp90-101.

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Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use.These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored.We studied the viability of using Projective Simulation,
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Ferrari, Davide, and Michele Amoretti. "Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices." International Journal of Quantum Information 16, no. 08 (2018): 1840006. http://dx.doi.org/10.1142/s0219749918400063.

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Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger–Horne–Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform qua
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Chen, Chih-Chieh, Masaya Watabe, Kodai Shiba, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. "On the Expressibility and Overfitting of Quantum Circuit Learning." ACM Transactions on Quantum Computing 2, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3466797.

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Applying quantum processors to model a high-dimensional function approximator is a typical method in quantum machine learning with potential advantage. It is conjectured that the unitarity of quantum circuits provides possible regularization to avoid overfitting. However, it is not clear how the regularization interplays with the expressibility under the limitation of current Noisy-Intermediate Scale Quantum devices. In this article, we perform simulations and theoretical analysis of the quantum circuit learning problem with hardware-efficient ansatz. Thorough numerical simulations show that t
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Zhu, D., N. M. Linke, M. Benedetti, et al. "Training of quantum circuits on a hybrid quantum computer." Science Advances 5, no. 10 (2019): eaaw9918. http://dx.doi.org/10.1126/sciadv.aaw9918.

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Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swar
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Hu, Ling, Shu-Hao Wu, Weizhou Cai, et al. "Quantum generative adversarial learning in a superconducting quantum circuit." Science Advances 5, no. 1 (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 rou
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Griol-Barres, Israel, Sergio Milla, Antonio Cebrián, Yashar Mansoori, and José Millet. "Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals." Applied Sciences 11, no. 14 (2021): 6427. http://dx.doi.org/10.3390/app11146427.

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Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problem
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Stokes, James, and John Terilla. "Probabilistic Modeling with Matrix Product States." Entropy 21, no. 12 (2019): 1236. http://dx.doi.org/10.3390/e21121236.

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Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuit-based models offer a useful inductive bias for classical datasets is supported by experimental results on the par
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Ren, Wanghao, Zhiming Li, Yiming Huang, et al. "Quantum generative adversarial networks for learning and loading quantum image in noisy environment." Modern Physics Letters B 35, no. 21 (2021): 2150360. http://dx.doi.org/10.1142/s0217984921503607.

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Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend q
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Benedetti, Marcello, Edward Grant, Leonard Wossnig, and Simone Severini. "Adversarial quantum circuit learning for pure state approximation." New Journal of Physics 21, no. 4 (2019): 043023. http://dx.doi.org/10.1088/1367-2630/ab14b5.

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Dissertations / Theses on the topic "Quantum circuit learning"

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Lamarre, Aldo. "Apprentissage de circuits quantiques par descente de gradient classique." Thesis, 2020. http://hdl.handle.net/1866/24322.

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Nous présentons un nouvel algorithme d’apprentissage de circuits quantiques basé sur la descente de gradient classique. Comme ce sujet unifie deux disciplines, nous expliquons les deux domaines aux gens de l’autre discipline. Conséquemment, nous débutons par une présentation du calcul quantique et des circuits quantiques pour les gens en apprentissage automatique suivi d’une présentation des algorithmes d’apprentissage automatique pour les gens en informatique quantique. Puis, pour motiver et mettre en contexte nos résultats, nous passons à une légère revue de littérature en apprentissage a
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Book chapters on the topic "Quantum circuit learning"

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Maeda, Michiharu, Masaya Suenaga, and Hiromi Miyajima. "A Learning Model in Qubit Neuron According to Quantum Circuit." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_34.

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Conference papers on the topic "Quantum circuit learning"

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Guy, Khalil. "Optimizing Quantum Circuit Layout Using Reinforcement Learning." In SIST/GEM End of Term Activities 2020, Batavia, IL (United States), 3-5 Aug 2020. US DOE, 2020. http://dx.doi.org/10.2172/1668390.

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Saravanan, Vedika, and Samah Mohamed Saeed. "Test Data-Driven Machine Learning Models for Reliable Quantum Circuit Output." In 2021 IEEE European Test Symposium (ETS). IEEE, 2021. http://dx.doi.org/10.1109/ets50041.2021.9465405.

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Levine, Edlyn V., Matthew J. Turner, Nicholas Langellier, Thomas M. Babinec, Marko Lončar, and Ronald L. Walsworth. "Backside Integrated Circuit Magnetic Field Imaging with a Quantum Diamond Microscope." In ISTFA 2020. ASM International, 2020. http://dx.doi.org/10.31399/asm.cp.istfa2020p0084.

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Abstract We present a new method for backside integrated circuit (IC) magnetic field imaging using Quantum Diamond Microscope (QDM) nitrogen vacancy magnetometry. We demonstrate the ability to simultaneously image the functional activity of an IC thinned to 12 µm remaining silicon thickness over a wide fieldof- view (3.7 x 3.7 mm2). This 2D magnetic field mapping enables the localization of functional hot-spots on the die and affords the potential to correlate spatially delocalized transient activity during IC operation that is not possible with scanning magnetic point probes. We use Finite El
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Bao, Maggie, Cole Powers, and Marek Perkowski. "Quantum Algorithm for Machine Learning and Circuit Design Based on Optimization of Ternary - Input, Binary-Output Kronecker-Reed-Muller Forms." In 2021 IEEE 51st International Symposium on Multiple-Valued Logic (ISMVL). IEEE, 2021. http://dx.doi.org/10.1109/ismvl51352.2021.00029.

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LaBorde, Margarite L., Allee C. Rogers, and Jonathan P. Dowling. "Finding Broken Gates in Quantum Circuits–Exploiting Hybrid Machine Learning." In Frontiers in Optics. OSA, 2020. http://dx.doi.org/10.1364/fio.2020.ftu8d.4.

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Guy, Khalil, and Gabriel Purdue. "Using Reinforcement Learning to Optimize Quantum Circuits in the Presence of Noise." In Using Reinforcement Learning to Optimize Quantum Circuits in the Presence of Noise. US DOE, 2020. http://dx.doi.org/10.2172/1648527.

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Radu, I. P., R. Li, A. Potocnik, et al. "Solid state qubits: how learning from CMOS fabrication can speed-up progress in Quantum Computing." In 2021 Symposium on VLSI Circuits. IEEE, 2021. http://dx.doi.org/10.23919/vlsicircuits52068.2021.9492397.

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Ono, Tatsuki, Song Bian, and Takashi Sato. "Automatic Parallelism Tuning for Module Learning with Errors Based Post-Quantum Key Exchanges on GPUs." In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021. http://dx.doi.org/10.1109/iscas51556.2021.9401575.

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Reports on the topic "Quantum circuit learning"

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

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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. W
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Guy, Khalil, and Gabriel Perdue. Using Reinforcement Learning to Optimize Quantum Circuits in thePresence of Noise. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1661681.

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Journal articles
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