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Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise.
Input redundancy for parameterized quantum circuits
Francisco Javier Gil Vidal and Dirk Oliver Theis · 1901
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Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition
Gavin E Crooks · 1905
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Operator-schmidt decomposition and the geometrical edges of two-qubit gates
S Balakrishnan and R Sankaranarayanan · 2011
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Coordinate descent algorithms
Stephen J Wright · 2015
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Trading classical and quantum computational resources
Sergey Bravyi, Graeme Smith, and John A Smolin · 2016
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Product of estimates of mean values - concentration of measure inequality
js21 (https://mathoverflow.net/users/21724/js21) · 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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Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
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Differentiable learning of quantum circuit born machines
Jin-Guo Liu and Lei Wang · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Cited alongside, same era.
Quantum computing in the nisq era and beyond
John Preskill · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Quantum machine learning in feature hilbert spaces
Maria Schuld and Nathan Killoran · 2019
Cited alongside, same era.
Simulating large quantum circuits on a small quantum computer
Tianyi Peng, Aram W Harrow, Maris Ozols, and Xiaodi Wu · 2020
Cited alongside, same era.
Quantum advantage and noise reduction in distributed quantum computing
J Avron, Ofer Casper, and Ilan Rozen · 2021
Cited alongside, same era.
Quantum circuit cutting with maximum-likelihood tomography
Michael A Perlin, Zain H Saleem, Martin Suchara, and James C Osborn · 2021
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Machine learning of high dimensional data on a noisy quantum processor
Evan Peters, João Caldeira, Alan Ho, Stefan Leichenauer, Masoud Mohseni, Hartmut Neven, Panagiotis Spentzouris, Doug Strain, and Gabriel N Perdue · 2021
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Quantum divide and conquer for combinatorial optimization and distributed computing
Zain H Saleem, Teague Tomesh, Michael A Perlin, Pranav Gokhale, and Martin Suchara · 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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Cutqc: using small quantum computers for large quantum circuit evaluations
Wei Tang, Teague Tomesh, Martin Suchara, Jeffrey Larson, and Margaret Martonosi · 2021
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i i -qer: An intelligent approach towards quantum error reduction
Saikat Basu, Amit Saha, Amlan Chakrabarti, and Susmita Sur-Kolay · 2021
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Bringing the concepts of virtualization to gate-based quantum computing
Marvin Bechtold · 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
Cited alongside, same era.
Variational quantum policies for reinforcement learning
Sofiene Jerbi, Casper Gyurik, Simon Marshall, Hans J Briegel, and Vedran Dunjko · 2021
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Quantum federated learning through blind quantum computing
Weikang Li, Sirui Lu, and Dong-Ling Deng · 2021
Cited alongside, same era.
Overhead for simulating a non-local channel with local channels by quasiprobability sampling
Kosuke Mitarai and Keisuke Fujii · 2021
Cited alongside, same era.
Quantum simulation with hybrid tensor networks
Xiao Yuan, Jinzhao Sun, Junyu Liu, Qi Zhao, and You Zhou · 2021
Later among the works it cites.
Deep variational quantum eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers
Keisuke Fujii, Kaoru Mizuta, Hiroshi Ueda, Kosuke Mitarai, Wataru Mizukami, and Yuya O Nakagawa · 2022
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Avoiding barren plateaus using classical shadows
Stefan H Sack, Raimel A Medina, Alexios A Michailidis, Richard Kueng, and Maksym Serbyn · 2022
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If you listen carefully, you can hear the goat screaming “MNIST results!” But the dude isn’t listening carefully
LeCun Yann · 2022
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Quantum machine learning of large datasets using randomized measurements
Tobias Haug, Chris N Self, and M S Kim · 2023
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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 · 2041
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