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Quantum machine learning has become an area of growing interest but has certain theoretical and hardware-specific limitations.
Quantum measurements and the Abelian stabilizer problem
Alexei Y. Kitaev · 1996
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Quantum t-designs: t-wise independence in the quantum world
Andris Ambainis and Joseph Emerson · 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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Exact and approximate unitary 2-designs and their application to fidelity estimation
Christoph Dankert, Richard Cleve, Joseph Emerson, and Etera Livine · 2009
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Random quantum circuits are approximate 2-designs
Aram W. Harrow and Richard A. Low · 2009
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Mathematical foundations for a compositional distributional model of meaning
Bob Coecke, Mehrnoosh Sadrzadeh, and Stephen Clark · 2010
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Interacting quantum observables: categorical algebra and diagrammatics
Bob Coecke and Ross Duncan · 2011
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Composite parameterization and Haar measure for all unitary and special unitary groups
Christoph Spengler, Marcus Huber, and Beatrix C. Hiesmayr · 2012
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A quantum information processor with trapped ions
Philipp Schindler, Daniel Nigg, Thomas Monz, Julio T. Barreiro, Esteban Martinez, Shannon X. Wang, Stephan Quint, Matthias F. Brandl, Volckmar Nebendahl, Christian F. Roos, Michael Chwalla, Markus Hennrich, and Rainer Blatt · 2013
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. O’Brien · 2014
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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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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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Pennylane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, et al · 2018
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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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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
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Benchmarking an 11-qubit quantum computer
K. Wright, K. M. Beck, S. Debnath, J. M. Amini, Y. Nam, N. Grzesiak, J.-S. Chen, N. C. Pisenti, M. Chmielewski, C. Collins, K. M. Hudek, J. Mizrahi, J. D. Wong-Campos, S. Allen, J. Apisdorf, P. Solomon, M. Williams, A. M. Ducore, A. Blinov, S. M. Kreikemeier, V. Chaplin, M. Keesan, C. Monroe, and J. Kim · 2019
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Data re-uploading for a universal quantum classifier
Adriá n Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre · 2020
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The power of quantum neural networks
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, and Stefan Woerner · 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
Cited alongside, same era.
Is quantum advantage the right goal for quantum machine learning?
Maria Schuld and Nathan Killoran · 2022
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Quantum variational algorithms are swamped with traps
Eric R. Anschuetz and Bobak T. Kiani · 2022
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QMware — The first global quantum cloud
QMware · 2022
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Fock state-enhanced expressivity of quantum machine learning models
Beng Yee Gan, Daniel Leykam, and Dimitris G. Angelakis · 2022
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Generalization despite overfitting in quantum machine learning models
Evan Peters and Maria Schuld · 2022
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Quantum machine learning: from physics to software engineering
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Mohammad Kordzanganeh, Aydin Utting, and Anna Scaife · 2021
Cited alongside, same era.
Noise-induced barren plateaus in variational quantum algorithms
Samson Wang, Enrico Fontana, M. Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J. Coles · 2021
Cited alongside, same era.
Layerwise learning for quantum neural networks
Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib · 2021
Cited alongside, same era.
Cost function dependent barren plateaus in shallow parametrized quantum circuits
M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles · 2021
Cited alongside, same era.
Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
Chen Zhao and Xiao-Shan Gao · 2021
Cited alongside, same era.
Exponentially many local minima in quantum neural networks
Xuchen You and Xiaodi Wu · 2021
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Supervised quantum machine learning models are kernel methods
Maria Schuld · 2021
Cited alongside, same era.
Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants, and Ray-Kuang Lee · 2023
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Parallel hybrid networks: an interplay between quantum and classical neural networks
Mohammad Kordzanganeh, Daria Kosichkina, and Alexey Melnikov · 2023
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Quantum machine learning for image classification
Arsenii Senokosov, Alexander Sedykh, Asel Sagingalieva, and Alexey Melnikov · 2023
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Hyperparameter optimization of hybrid quantum neural networks for car classification
Asel Sagingalieva, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, et al · 2023
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Hybrid quantum neural network for drug response prediction
Asel Sagingalieva, Mohammad Kordzanganeh, Nurbolat Kenbayev, Daria Kosichkina, Tatiana Tomashuk, and Alexey Melnikov · 2023
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Quantum algorithms applied to satellite mission planning for Earth observation
Serge Rainjonneau, Igor Tokarev, Sergei Iudin, Saaketh Rayaprolu, Karan Pinto, Daria Lemtiuzhnikova, Miras Koblan, Egor Barashov, Mo Kordzanganeh, Markus Pflitsch, and Alexey Melnikov · 2023
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Forecasting the steam mass flow in a powerplant using the parallel hybrid network
Andrii Kurkin, Jonas Hegemann, Mo Kordzanganeh, and Alexey Melnikov · 2023
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Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
Mohammad Kordzanganeh, Markus Buchberger, Basil Kyriacou, Maxim Povolotskii, Wilhelm Fischer, Andrii Kurkin, Wilfrid Somogyi, Asel Sagingalieva, Markus Pflitsch, and Alexey Melnikov · 2023
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Exponential data encoding for quantum supervised learning
S. Shin, Y. S. Teo, and H. Jeong · 2023
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