Fetching the paper…
Reading the bibliography…
Several important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup to training classical classifiers and applications to data analytics in quantum physics that can be implemented on the near future quantum computers.
Detection theory and quantum mechanics
Carl W Helstrom · 1967
Earlier work this paper cites.
Exact solutions of some nonconvex quadratic optimization problems via sdp and socp relaxations
Sunyoung Kim and Masakazu Kojima · 2003
Earlier work this paper cites.
Discriminating states: The quantum chernoff bound
Koenraad MR Audenaert, John Calsamiglia, Ramón Munoz-Tapia, Emilio Bagan, Ll Masanes, Antonio Acin, and Frank Verstraete · 2007
Earlier work this paper cites.
High-fidelity readout of trapped-ion qubits
AH Myerson, DJ Szwer, SC Webster, DTC Allcock, MJ Curtis, G Imreh, JA Sherman, DN Stacey, AM Steane, and DM Lucas · 2008
Earlier work this paper cites.
Bloch vectors for qudits
Reinhold A Bertlmann and Philipp Krammer · 2008
Earlier work this paper cites.
Semidefinite programs for completely bounded norms
John Watrous · 2009
Earlier work this paper cites.
Quantum computation and quantum information
Michael A Nielsen and Isaac L Chuang · 2010
Earlier work this paper cites.
Scalable simultaneous multiqubit readout with 99. 99% single-shot fidelity
AH Burrell, DJ Szwer, SC Webster, and DM Lucas · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
Adversarial machine learning
Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and J Doug Tygar · 2011
Earlier work this paper cites.
Smooth composite pulses for high-fidelity quantum information processing
Boyan T Torosov and Nikolay V Vitanov · 2011
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Measuring neural net robustness with constraints
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya V. Nori, and Antonio Criminisi · 2016
Earlier work this paper cites.
CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
Earlier work this paper cites.
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo and Matthias Troyer · 2017
Cited alongside, same era.
Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Tom B Brown, Dandelion Mané, Aurko Roy, Martin Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
An approach to reachability analysis for feed-forward relu neural networks
Alessio Lomuscio and Lalit Maganti · 2017
Cited alongside, same era.
Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Y. Xiao, and Russ Tedrake · 2019
Later among the works it cites.
Tensorflow quantum: A software framework for quantum machine learning
Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J Martinez, Jae Hyeon Yoo, Sergei V Isakov, Philip Massey, Murphy Yuezhen Niu, Ramin Halavati, Evan Peters, et al · 2020
Closest in time.
Verification of deep convolutional neural networks using imagestars
Hoang-Dung Tran, Stanley Bak, Weiming Xiang, and Taylor T Johnson · 2020
Closest in time.
An abstraction-based framework for neural network verification
Yizhak Yisrael Elboher, Justin Gottschlich, and Guy Katz · 2020
Closest in time.
Formal analysis and redesign of a neural network-based aircraft taxiing system with verifai
Daniel J Fremont, Johnathan Chiu, Dragos D Margineantu, Denis Osipychev, and Sanjit A Seshia · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
Cited alongside, same era.
How distinguishable are two quantum processes? or what is the error rate of a quantum gate?
Robin J Blume-Kohout · 2017
Cited alongside, same era.
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Vedran Dunjko and Hans J Briegel · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
On the suitability of lp-norms for creating and preventing adversarial examples
Mahmood Sharif, Lujo Bauer, and Michael K Reiter · 2018
Cited alongside, same era.
Sparse semidefinite programs with near-linear time complexity
Richard Y Zhang and Javad Lavaei · 2018
Cited alongside, same era.
Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
Cited alongside, same era.
Nnv: The neural network verification tool for deep neural networks and learning-enabled cyber-physical systems
Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak, and Taylor T Johnson · 2020
Closest in time.
Quantum adversarial machine learning
Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng · 2020
Closest in time.
Quantum noise protects quantum classifiers against adversaries
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Dacheng Tao, and Nana Liu · 2020
Closest in time.
Vulnerability of quantum classification to adversarial perturbations
Nana Liu and Peter Wittek · 2020
Closest in time.
Optimal provable robustness of quantum classification via quantum hypothesis testing
Maurice Weber, Nana Liu, Bo Li, Ce Zhang, and Zhikuan Zhao · 2020
Closest in time.
Classification with quantum neural networks on near term processors
Edward Farhi, Hartmut Neven, et al · 2020
Closest in time.
A tutorial on quantum convolutional neural networks (qcnn)
Seunghyeok Oh, Jaeho Choi, and Joongheon Kim · 2020
Closest in time.
Robustness verification of quantum classifiers
Ji Guan, Wang Fang, and Mingsheng Ying · 2020
Closest in time.
Information-theoretic bounds on quantum advantage in machine learning
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2021
Closest in time.
Engineering fast high-fidelity quantum operations with constrained interactions
T Figueiredo Roque, Aashish A Clerk, and Hugo Ribeiro · 2021
Closest in time.