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We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD).
Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alex Smola, John Shawe-Taylor, and John Platt · 1999
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Quantum Computation and Quantum Information
Michael A. Nielsen and Isaac L. Chuang · 2000
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Efficient algorithms for mining outliers from large data sets
Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim · 2000
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Lof: Identifying density-based local outliers
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander · 2000
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Support vector data description
David M.J. Tax and Robert P.W. Duin · 2004
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Dissipationless flow and sharp threshold of a polariton condensate with long lifetime
Bryan Nelsen, Gangqiang Liu, Mark Steger, David W. Snoke, Ryan Balili, Ken West, and Loren Pfeiffer · 2013
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Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
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Kernel methods and machine learning
Sun Yuan Kung · 2014
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Fundamental scaling laws in nanophotonics
Ke Liu, Shuai Sun, Arka Majumdar, and Volker J. Sorger · 2016
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Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, and Georg Langs · 2017
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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta · 2017
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Simulating quantum field theory with a quantum computer, 2018
John Preskill · 2018
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Quantum circuit learning
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii · 2018
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Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez, and Rupak Biswas · 2018
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Differentiable learning of quantum circuit born machines
Jin-Guo Liu and Lei Wang · 2018
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Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
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Cyber security in the quantum era
Petros Wallden and Elham Kashefi · 2019
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2019
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Quantum computing for finance: Overview and prospects
Román Orús, Samuel Mugel, and Enrique Lizaso · 2019
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Quantum machine learning in feature hilbert spaces
Maria Schuld and Nathan Killoran · 2019
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Supervised learning with quantum-enhanced feature spaces
Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta · 2019
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Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D. Lukin · 2019
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Hybrid classical-quantum linear solver using noisy intermediate-scale quantum machines
Chih-Chieh Chen, Shiue-Yuan Shiau, Ming-Feng Wu, and Yuh-Renn Wu · 2019
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Learning and inference on generative adversarial quantum circuits
Jinfeng Zeng, Yufeng Wu, Jin-Guo Liu, Lei Wang, and Jiangping Hu · 2019
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Quantum generative adversarial networks for learning and loading random distributions
Christa Zoufal, Aurélien Lucchi, and Stefan Woerner · 2019
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q-means: A quantum algorithm for unsupervised machine learning
Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, and Anupam Prakash · 2019
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Forecasting financial crashes with quantum computing
Román Orús, Samuel Mugel, and Enrique Lizaso · 2019
Cited alongside, same era.
f-anogan: Fast unsupervised anomaly detection with generative adversarial networks
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-Erfurth · 2019
Cited alongside, same era.
Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Cited alongside, same era.
Calibration of a cross-resonance two-qubit gate between directly coupled transmons
A.D. Patterson, J. Rahamim, T. Tsunoda, P.A. Spring, S. Jebari, K. Ratter, M. Mergenthaler, G. Tancredi, B. Vlastakis, M. Esposito, and P.J. Leek · 2019
Cited alongside, same era.
Quantum computational chemistry
Sam McArdle, Suguru Endo, Alán Aspuru-Guzik, Simon C. Benjamin, and Xiao Yuan · 2020
Cited alongside, same era.
Strategies for solving the fermi-hubbard model on near-term quantum computers
Training quantum embedding kernels on near-term quantum computers, 2021
Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster, Peter-Jan H. S. Derks, Paul K. Faehrmann, and Johannes Jakob Meyer · 2021
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Covariant quantum kernels for data with group structure, 2021
Jennifer R. Glick, Tanvi P. Gujarati, Antonio D. Corcoles, Youngseok Kim, Abhinav Kandala, Jay M. Gambetta, and Kristan Temme · 2021
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Effect of data encoding on the expressive power of variational quantum-machine-learning models
Maria Schuld, Ryan Sweke, and Johannes Jakob Meyer · 2021
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Generalized quantum circuit differentiation rules
Oleksandr Kyriienko and Vincent E. Elfving · 2021
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Power of data in quantum machine learning
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R. McClean · 2021
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Chris Cade, Lana Mineh, Ashley Montanaro, and Stasja Stanisic · 2020
Cited alongside, same era.
Variational quantum algorithms for nonlinear problems
Michael Lubasch, Jaewoo Joo, Pierre Moinier, Martin Kiffner, and Dieter Jaksch · 2020
Cited alongside, same era.
Circuit-centric quantum classifiers
Maria Schuld, Alex Bocharov, Krysta M. Svore, and Nathan Wiebe · 2020
Cited alongside, same era.
Data re-uploading for a universal quantum classifier
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre · 2020
Cited alongside, same era.
The born supremacy: quantum advantage and training of an ising born machine
Brian Coyle, Daniel Mills, Vincent Danos, and Elham Kashefi · 2020
Cited alongside, same era.
An Adaptive Optimizer for Measurement-Frugal Variational Algorithms
Jonas M. Kübler, Andrew Arrasmith, Lukasz Cincio, and Patrick J. Coles · 2020
Cited alongside, same era.
Benchmarking a high-fidelity mixed-species entangling gate
A. C. Hughes, V. M. Schäfer, K. Thirumalai, D. P. Nadlinger, S. R. Woodrow, D. M. Lucas, and C. J. Ballance · 2020
Cited alongside, same era.
Ruslan Shaydulin and Stefan M. Wild · 2021
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Quantum evolution kernel: Machine learning on graphs with programmable arrays of qubits
Louis-Paul Henry, Slimane Thabet, Constantin Dalyac, and Loïc Henriet · 2021
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A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2021
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Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, and Ryan Sweke · 2021
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Generalization in quantum machine learning from few training data, 2021
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J. Coles · 2021
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Demonstration of quantum volume 64 on a superconducting quantum computing system
Petar Jurcevic, Ali Javadi-Abhari, Lev S Bishop, Isaac Lauer, Daniela F Bogorin, Markus Brink, Lauren Capelluto, Oktay Günlük, Toshinari Itoko, Naoki Kanazawa, Abhinav Kandala, George A Keefe, Kevin Krsulich, William Landers, Eric P Lewandowski, Douglas T McClure, Giacomo Nannicini, Adinath Narasgond, Hasan M Nayfeh, Emily Pritchett, Mary Beth Rothwell, Srikanth Srinivasan, Neereja Sundaresan, Cindy Wang, Ken X Wei, Christopher J Wood, Jeng-Bang Yau, Eric J Zhang, Oliver E Dial, Jerry M Chow, and Jay M Gambetta · 2021
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Strong quantum computational advantage using a superconducting quantum processor
Yulin Wu, Wan-Su Bao, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, Ming Gong, Cheng Guo, Chu Guo, Shaojun Guo, Lianchen Han, Linyin Hong, He-Liang Huang, Yong-Heng Huo, Liping Li, Na Li, Shaowei Li, Yuan Li, Futian Liang, Chun Lin, Jin Lin, Haoran Qian, Dan Qiao, Hao Rong, Hong Su, Lihua Sun, Liangyuan Wang, Shiyu Wang, Dachao Wu, Yu Xu, Kai Yan, Weifeng Yang, Yang Yang, Yangsen Ye, Jianghan Yin, Chong Ying, Jiale Yu, Chen Zha, Cha Zhang, Haibin Zhang, Kaili Zhang, Yiming Zhang, Han Zhao, Youwei Zhao, Liang Zhou, Qingling Zhu, Chao-Yang Lu, Cheng-Zhi Peng, Xiaobo Zhu, and Jian-Wei Pan · 2021
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Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits
A. D. Córcoles, Maika Takita, Ken Inoue, Scott Lekuch, Zlatko K. Minev, Jerry M. Chow, and Jay M. Gambetta · 2021
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Large-scale quantum machine learning, 2021
Tobias Haug, Chris N. Self, and M. S. Kim · 2021
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Fault-tolerant control of an error-corrected qubit
Laird Egan, Dripto M. Debroy, Crystal Noel, Andrew Risinger, Daiwei Zhu, Debopriyo Biswas, Michael Newman, Muyuan Li, Kenneth R. Brown, Marko Cetina, and Christopher Monroe · 2021
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A survey of quantum computing for finance, 2022
Dylan Herman, Cody Googin, Xiaoyuan Liu, Alexey Galda, Ilya Safro, Yue Sun, Marco Pistoia, and Yuri Alexeev · 2022
Closest in time.
Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms
Nikitas Stamatopoulos, Guglielmo Mazzola, Stefan Woerner, and William J. Zeng · 2022
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Noisy intermediate-scale quantum algorithms
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik · 2022
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Review of some existing qml frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets
N. Schetakis, D. Aghamalyan, P. Griffin, and M. Boguslavsky · 2022
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Quantum convolutional neural networks for high energy physics data analysis
Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, and Shinjae Yoo · 2022
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Quantum kernel methods for solving differential equations, 2022
Annie E. Paine, Vincent E. Elfving, and Oleksandr Kyriienko · 2022
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Variational quantum and quantum-inspired clustering, 2022
Pablo Bermejo and Roman Orus · 2022
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Review of machine learning approach on credit card fraud detection
Rejwan Bin Sulaiman, Vitaly Schetinin, and Paul Sant · 2022
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https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
Kaggle credit card fraud detection: Anonymized credit card transactions labeled as fraudulent or genuine · 2022
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Protocols for Trainable and Differentiable Quantum Generative Modelling
Oleksandr Kyriienko, Annie E. Paine, and Vincent E. Elfving · 2022
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Realizing repeated quantum error correction in a distance-three surface code
Sebastian Krinner, Nathan Lacroix, Ants Remm, Agustin Di Paolo, Elie Genois, Catherine Leroux, Christoph Hellings, Stefania Lazar, Francois Swiadek, Johannes Herrmann, Graham J. Norris, Christian Kraglund Andersen, Markus Müller, Alexandre Blais, Christopher Eichler, and Andreas Wallraff · 2022
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Few-photon all-optical phase rotation in a quantum-well micropillar cavity
Tintu Kuriakose, Paul M. Walker, Toby Dowling, Oleksandr Kyriienko, Ivan A. Shelykh, Phillipe St-Jean, Nicola Carlon Zambon, Aristide Lemaître, Isabelle Sagnes, Luc Legratiet, Abdelmounaim Harouri, Sylvain Ravets, Maurice S. Skolnick, Alberto Amo, Jacqueline Bloch, and Dmitry N. Krizhanovskii · 2022
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