Fetching the paper…
Reading the bibliography…
Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies.
The use of multiple measurements in taxonomic problems
Ronald A Fisher · 1936
Earlier work this paper cites.
The species problem in iris
Edgar Anderson · 1936
Earlier work this paper cites.
A fast quantum mechanical algorithm for database search
Lov K Grover · 1996
Earlier work this paper cites.
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Peter W Shor · 1999
Earlier work this paper cites.
Quantum computation and quantum information, 2002
Michael A Nielsen and Isaac Chuang · 2002
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
Earlier work this paper cites.
Quantum random access memory
Vittorio Giovannetti, Seth Lloyd, and Lorenzo Maccone · 2008
Earlier work this paper cites.
Measurement-based quantum computation
Hans J Briegel, David E Browne, Wolfgang Dür, Robert Raussendorf, and Maarten Van den Nest · 2009
Earlier work this paper cites.
Quantum computation by local measurement
Robert Raussendorf and Tzu-Chieh Wei · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
Quantum speed-up for unsupervised learning
Esma Aïmeur, Gilles Brassard, and Sébastien Gambs · 2013
Earlier work this paper cites.
Quantum algorithms for supervised and unsupervised machine learning
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2013
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Quantum machine learning: what quantum computing means to data mining
Peter Wittek · 2014
Earlier work this paper cites.
Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning
Nathan Wiebe, Ashish Kapoor, and Krysta Svore · 2014
Earlier work this paper cites.
Quantum support vector machine for big data classification
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd · 2014
Earlier work this paper cites.
The quest for a quantum neural network
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
Quantum computational supremacy
Aram W Harrow and Ashley Montanaro · 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.
Unsupervised machine learning on a hybrid quantum computer
JS Otterbach, R Manenti, N Alidoust, A Bestwick, M Block, B Bloom, S Caldwell, N Didier, E Schuyler Fried, S Hong, et al · 2017
Cited alongside, same era.
Error mitigation for short-depth quantum circuits
Kristan Temme, Sergey Bravyi, and Jay M Gambetta · 2017
Cited alongside, same era.
Deep Learning for Computer Vision: A Brief Review
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis · 2018
q-means: A quantum algorithm for unsupervised machine learning
Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, and Anupam Prakash · 2019
Later among the works it cites.
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2019
Later among the works it cites.
A new quantum approach to binary classification
Giuseppe Sergioli, Roberto Giuntini, and Hector Freytes · 2019
Later among the works it cites.
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
Later among the works it cites.
Hybrid quantum-classical convolutional neural networks
Junhua Liu, Kwan Hui Lim, Kristin L Wood, Wei Huang, Chu Guo, and He-Liang Huang · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Supervised learning with quantum computers
Maria Schuld and Francesco Petruccione · 2018
Cited alongside, same era.
Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
Cited alongside, same era.
Quantum circuit learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
Cited alongside, same era.
Hierarchical quantum classifiers
Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G Green, and Simone Severini · 2018
Cited alongside, same era.
Quantum hopfield neural network
Patrick Rebentrost, Thomas R Bromley, Christian Weedbrook, and Seth Lloyd · 2018
Cited alongside, same era.
Maria Schuld and Nathan Killoran · 2019
Later among the works it cites.
Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
Later among the works it cites.
Detector tomography on ibm 5-qubit quantum computers and mitigation of imperfect measurement
Yanzhu Chen, Maziar Farahzad, Shinjae Yoo, and Tzu-Chieh Wei · 2019
Later among the works it cites.
Circuit-centric quantum classifiers
Maria Schuld, Alex Bocharov, Krysta M Svore, and Nathan Wiebe · 2020
Closest in time.
Quantum adversarial machine learning
Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng · 2020
Closest in time.
On the learnability of quantum neural networks
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, and Dacheng Tao · 2020
Closest in time.
Experimental quantum generative adversarial networks for image generation
He-Liang Huang, Yuxuan Du, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, et al · 2020
Closest in time.
Quantum embeddings for machine learning
Seth Lloyd, Maria Schuld, Aroosa Ijaz, Josh Izaac, and Nathan Killoran · 2020
Closest in time.
Training deep quantum neural networks
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, and Ramona Wolf · 2020
Closest in time.
Data re-uploading for a universal quantum classifier
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I Latorre · 2020
Closest in time.
An entanglement enhanced training algorithm for supervised quantum classifiers
Soumik Adhikary · 2020
Closest in time.
Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits
Shuxiang Cao, Leonard Wossnig, Brian Vlastakis, Peter Leek, and Edward Grant · 2020
Closest in time.
Quantum classifier with tailored quantum kernel
Carsten Blank, Daniel K Park, June-Koo Kevin Rhee, and Francesco Petruccione · 2020
Closest in time.
Mitigating measurement errors in multi-qubit experiments
Sergey Bravyi, Sarah Sheldon, Abhinav Kandala, David C Mckay, and Jay M Gambetta · 2020
Closest in time.