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Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies.
Jonathan Romero and Alan Aspuru-Guzik · 1901
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Quantum Generative Adversarial Networks for learning and loading random distributions
Christa Zoufal, Aurélien Lucchi, and Stefan Woerner · 1904
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Generative training of quantum Boltzmann machines with hidden units
Nathan Wiebe and Leonard Wossnig · 1905
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Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms
Sukin Sim, Peter D. Johnson, and Alán Aspuru-Guzik · 1905
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Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
Guillaume Verdon, Jacob Marks, Sasha Nanda, Stefan Leichenauer, and Jack Hidary · 1910
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An approximate description of quantum states
Marco Paini and Amir Kalev · 1910
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Error-mitigated data-driven circuit learning on noisy quantum hardware
Kathleen E. Hamilton and Raphael C. Pooser · 1911
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Quantum unary approach to option pricing
Sergi Ramos-Calderer, Adrián Pérez-Salinas, Diego García-Martín, Carlos Bravo-Prieto, Jorge Cortada, Jordi Planagumà, and José I. Latorre · 1912
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On Information and Sufficiency
S Kullback and R A Leibler · 1951
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A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices
Richard Sinkhorn · 1964
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Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
Peter W Shor · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Global entanglement in multiparticle systems
David A. Meyer and Nolan R. Wallach · 2002
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Thomas Hubregtsen, Josef Pichlmeier, and Koen Bertels · 2003
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An observable measure of entanglement for pure states of multi-qubit systems
Gavin K. Brennen · 2003
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Hartree-Fock on a superconducting qubit quantum computer
Frank Arute et. al · 2004
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Variational Quantum State Eigensolver
M. Cerezo, Kunal Sharma, Andrew Arrasmith, and Patrick J. Coles · 2004
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Operator Sampling for Shot-frugal Optimization in Variational Algorithms
Andrew Arrasmith, Lukasz Cincio, Rolando D. Somma, and Patrick J. Coles · 2004
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Classical Optimizers for Noisy Intermediate-Scale Quantum Devices
Wim Lavrijsen, Ana Tudor, Juliane Müller, Costin Iancu, and Wibe de Jong · 2004
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Expressibility and trainability of parameterized analog quantum systems for machine learning applications
Jirawat Tangpanitanon, Supanut Thanasilp, Ninnat Dangniam, Marc-Antoine Lemonde, and Dimitris G. Angelakis · 2005
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Integrating structured biological data by Kernel Maximum Mean Discrepancy
Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola · 2006
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Experimental demonstration of a quantum generative adversarial network for continuous distributions
Abhinav Anand, Jonathan Romero, Matthias Degroote, and Alán Aspuru-Guzik · 2006
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Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks
Samuel Mugel, Carlos Kuchkovsky, Escolastico Sanchez, Samuel Fernandez-Lorenzo, Jorge Luis-Hita, Enrique Lizaso, and Roman Orus · 2007
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On the Quantum versus Classical Learnability of Discrete Distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, and Jens Eisert · 2007
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A Kernel Method for the Two-Sample-Problem
Arthur Gretton, Karsten M Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex J Smola · 2007
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Quantum Algorithm for Linear Systems of Equations
Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
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Optimal Transport: Old and New
Cédric Villani · 2009
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Scikit-learn: Machine Learning in Python
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot, and E Duchesnay · 2011
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A Practical Guide to Training Restricted Boltzmann Machines
Geoffrey E Hinton · 2012
Cited alongside, same era.
A Quantum Approximate Optimization Algorithm
E Farhi, J Goldstone, and S Gutmann · 2014
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Quantum Circuit Learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
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Learning the quantum algorithm for state overlap
Lukasz Cincio, Yiğit Subaşı, Andrew T Sornborger, and Patrick J Coles · 2018
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Quantum Generative Adversarial Learning
Seth Lloyd and Christian Weedbrook · 2018
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Quantum generative adversarial networks
Pierre-Luc Dallaire-Demers and Nathan Killoran · 2018
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Quantum supremacy using a programmable superconducting processor
Frank Arute et. al · 2019
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A generative modeling approach for benchmarking and training shallow quantum circuits
Marcello Benedetti, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz · 2019
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Generative Adversarial Networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Training restricted Boltzmann machines: An introduction
Asja Fischer and Christian Igel · 2014
Cited alongside, same era.
Quantum Inspired Training for Boltzmann Machines
Nathan Wiebe, Ashish Kapoor, Christopher Granade, and Krysta M Svore · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
Aaditya Ramdas, Nicolas Garcia, and Marco Cuturi · 2015
Cited alongside, same era.
Learning in Implicit Generative Models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
Cited alongside, same era.
The Market Generator
Alexei Kondratyev and Christian Schwarz · 2019
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Geometry and symmetry in the quantum Boltzmann machine
Hai Jing Song, Tieling Song, Qi Kai He, Yang Liu, and D. L. Zhou · 2019
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Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouve, and Gabriel Peyré · 2019
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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QxSQA: GPGPU-Accelerated Simulated Quantum Annealer within a Non-Linear Optimization and Boltzmann Sampling Framework
D. Padilha, S. Weinstock, and M. Hodson · 2019
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Generation of industry-relevant synthetic data using simulated quantum annealing-trained Boltzmann machines
Maxwell P. Henderson and Justin Chan Jin Le · 2019
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Quantum mean embedding of probability distributions
Jonas M Kübler, Krikamol Muandet, and Bernhard Schölkopf · 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 computational chemistry
Sam McArdle, Suguru Endo, Alán Aspuru-Guzik, Simon C. Benjamin, and Xiao Yuan · 2020
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Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
Frank Arute et. al · 2020
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Non-Differentiable Learning of Quantum Circuit Born Machine with Genetic Algorithm
Alexei Kondratyev · 2020
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A quantum-classical cloud platform optimized for variational hybrid algorithms
Peter J Karalekas, Nikolas A Tezak, Eric C Peterson, Colm A Ryan, Marcus P da Silva, and Robert S Smith · 2020
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The Born supremacy: quantum advantage and training of an Ising Born machine
Brian Coyle, Daniel Mills, Vincent Danos, and Elham Kashefi · 2020
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Classical versus quantum models in machine learning: insights from a finance application
Javier Alcazar, Vicente Leyton-Ortega, and Alejandro Perdomo-Ortiz · 2020
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Circuit-centric quantum classifiers
Maria Schuld, Alex Bocharov, Krysta M. Svore, and Nathan Wiebe · 2020
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An Adaptive Optimizer for Measurement-Frugal Variational Algorithms
Jonas M. Kübler, Andrew Arrasmith, Lukasz Cincio, and Patrick J. Coles · 2020
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