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Variational quantum algorithms are viewed as promising candidates for demonstrating quantum advantage on near-term devices.
“Generalized swap networks for near-term quantum computing” (2019)
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M. J. D. Powell · 1964
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M. J. D. Powell · 1987
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“Gapless spin-fluid ground state in a random quantum heisenberg magnet”
Subir Sachdev and Jinwu Ye · 1993
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“An introduction to simulated evolutionary optimization”
D.B. Fogel · 1994
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S. K. Foong and S. Kanno · 1994
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“A direct search optimization method that models the objective and constraint functions by linear interpolation”
M. J. D. Powell · 1994
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“Iterative methods for optimization”
C.T. Kelley · 1999
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“Operator sampling for shot-frugal optimization in variational algorithms” (2020)
Andrew Arrasmith, Lukasz Cincio, Rolando D. Somma, and Patrick J. Coles · 2004
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“Parallel processing of powell’s optimization algorithm and its application to design of multi-way power dividers”
M. Kishihara, K. Yamane, and I. Ohta · 2005
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“Line search methods”
Jorge Nocedal and Stephen J. Wright · 2006
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“Geometry of quantum states: An introduction to quantum entanglement”
Ingemar Bengtsson and Karol Zyczkowski · 2006
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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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“Adam: A method for stochastic optimization” (2014)
Diederik P. Kingma and Jimmy Ba · 2014
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“Progress towards practical quantum variational algorithms”
Dave Wecker, Matthew B. Hastings, and Matthias Troyer · 2015
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“A simple model of quantum holography” (2015)
A. Kitaev · 2015
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“The theory of variational hybrid quantum-classical algorithms”
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
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“Efficient heat-bath sampling in fock space”
Adam A. Holmes, Hitesh J. Changlani, and C. J. Umrigar · 2016
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“Genetic algorithms for digital quantum simulations”
U. Las Heras, U. Alvarez-Rodriguez, E. Solano, and M. Sanz · 2016
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“Remarks on the sachdev-ye-kitaev model”
Juan Maldacena and Douglas Stanford · 2016
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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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“Potential of quantum computing for drug discovery”
Y. Cao, J. Romero, and A. Aspuru-Guzik · 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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“Application of fermionic marginal constraints to hybrid quantum algorithms”
Nicholas C Rubin, Ryan Babbush, and Jarrod McClean · 2018
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“Quantum chemistry in the age of quantum computing”
Yudong Cao, Jonathan Romero, Jonathan P. Olson, Matthias Degroote, Peter D. Johnson, Mária Kieferová, Ian D. Kivlichan, Tim Menke, Borja Peropadre, Nicolas P. D. Sawaya, Sukin Sim, Libor Veis, and Alán Aspuru-Guzik · 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
Cited alongside, same era.
“An initialization strategy for addressing barren plateaus in parametrized quantum circuits”
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
Cited alongside, same era.
“Eigenstate entanglement in the sachdev-ye-kitaev model”
Yichen Huang and Yingfei Gu · 2019
Cited alongside, same era.
https://github.com/iic-jku/ibm_qx_mapping (2019)
A. Paler A. Zulehner and R. Wille · 2019
Cited alongside, same era.
“Improving the performance of deep quantum optimization algorithms with continuous gate sets”
Nathan Lacroix, Christoph Hellings, Christian Kraglund Andersen, Agustin Di Paolo, Ants Remm, Stefania Lazar, Sebastian Krinner, Graham J. Norris, Mihai Gabureac, Johannes Heinsoo, Alexandre Blais, Christopher Eichler, and Andreas Wallraff · 2020
Cited alongside, same era.
“Large gradients via correlation in random parameterized quantum circuits”
Tyler Volkoff and Patrick J Coles · 2021
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“Local, expressive, quantum-number-preserving vqe ansätze for fermionic systems”
Gian-Luca R Anselmetti, David Wierichs, Christian Gogolin, and Robert M Parrish · 2021
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“Reoptimization of quantum circuits via hierarchical synthesis”
Xin-Chuan Wu, Marc Grau Davis, Frederic T. Chong, and Costin Iancu · 2021
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“The variational quantum eigensolver: A review of methods and best practices”
Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H. Booth, and Jonathan Tennyson · 2022
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“Perspective on the current state-of-the-art of quantum computing for drug discovery applications”
Nick S. Blunt, Joan Camps, Ophelia Crawford, Róbert Izsák, Sebastian Leontica, Arjun Mirani, Alexandra E. Moylett, Sam A. Scivier, Christoph Sünderhauf, Patrick Schopf, Jacob M. Taylor, and Nicole Holzmann · 2022
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“Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer”
David Wierichs, Christian Gogolin, and Michael Kastoryano · 2020
Cited alongside, same era.
“Sequential minimal optimization for quantum-classical hybrid algorithms”
Ken M. Nakanishi, Keisuke Fujii, and Synge Todo · 2020
Cited alongside, same era.
“Predicting many properties of a quantum system from very few measurements”
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2020
Cited alongside, same era.
“Think fast: a tensor streaming processor (tsp) for accelerating deep learning workloads”
Dennis Abts, Jonathan Ross, Jonathan Sparling, Mark Wong-VanHaren, Max Baker, Tom Hawkins, Andrew Bell, John Thompson, Temesghen Kahsai, Garrin Kimmell, Jennifer Hwang, Rebekah Leslie-Hurd, Michael Bye, E. R. Creswick, Matthew Boyd, Mahitha Venigalla, Evan Laforge, Jon Purdy, Purushotham Kamath, Dinesh Maheshwari, Michael Beidler, Geert Rosseel, Omar Ahmad, Gleb Gagarin, Richard Czekalski, Ashay Rane, Sahil Parmar, Jeff Werner, Jim Sproch, Adrian Macias, and Brian Kurtz · 2020
Cited alongside, same era.
“Advances and opportunities in materials science for scalable quantum computing”
Vincenzo Lordi and John M. Nichol · 2021
Cited alongside, same era.
“Nearest centroid classification on a trapped ion quantum computer”
Sonika Johri, Shantanu Debnath, Avinash Mocherla, Alexandros SINGK, Anupam Prakash, Jungsang Kim, and Iordanis Kerenidis · 2021
Cited alongside, same era.
“Training variational quantum algorithms is np-hard”
Lennart Bittel and Martin Kliesch · 2021
Cited alongside, same era.
“Detecting and quantifying entanglement on near-term quantum devices”
Kun Wang, Zhixin Song, Xuanqiang Zhao, Zihe Wang, and Xin Wang · 2022
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“Trainability of dissipative perceptron-based quantum neural networks”
Kunal Sharma, M. Cerezo, Lukasz Cincio, and Patrick J. Coles · 2022
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“Matrix product state pre-training for quantum machine learning”
James Dborin, Fergus Barratt, Vinul Wimalaweera, Lewis Wright, and Andrew G Green · 2022
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“Connecting ansatz expressibility to gradient magnitudes and barren plateaus”
Zoë Holmes, Kunal Sharma, M. Cerezo, and Patrick J. Coles · 2022
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“Entanglement diagnostics for efficient vqa optimization”
Joonho Kim and Yaron Oz · 2022
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“Quantum energy landscape and circuit optimization”
Joonho Kim and Yaron Oz · 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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“Quantum circuit architecture search for variational quantum algorithms”
Yuxuan Du, Tao Huang, Shan You, Min-Hsiu Hsieh, and Dacheng Tao · 2022
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“Evolutionary quantum architecture search for parametrized quantum circuits”
Li Ding and Lee Spector · 2022
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“Efficient quantum gate decomposition via adaptive circuit compression” (2022)
Péter Rakyta and Zoltán Zimborás · 2022
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“General parameter-shift rules for quantum gradients”
David Wierichs, Josh Izaac, Cody Wang, and Cedric Yen-Yu Lin · 2022
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“Barren plateaus in quantum tensor network optimization”
Enrique Cervero Martín, Kirill Plekhanov, and Michael Lubasch · 2023
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“Training variational quantum algorithms with random gate activation”
Shuo Liu, Shi-Xin Zhang, Shao-Kai Jian, and Hong Yao · 2023
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“Measurement-induced entanglement phase transitions in variational quantum circuits”
Roeland Wiersema, Cunlu Zhou, Juan Felipe Carrasquilla, and Yong Baek Kim · 2023
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“Using differential evolution to avoid local minima in variational quantum algorithms”
Daniel Faílde, José Daniel Viqueira, Mariamo Mussa Juane, and Andrés Gómez · 2023
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“Quantum reinforcement learning for quantum architecture search”
Samuel Yen-Chi Chen · 2023
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“Evolutionary Algorithms for Parameter Optimization—Thirty Years Later”
Thomas H. W. Bäck, Anna V. Kononova, Bas van Stein, Hao Wang, Kirill A. Antonov, Roman T. Kalkreuth, Jacob de Nobel, Diederick Vermetten, Roy de Winter, and Furong Ye · 2023
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“Efficient variational synthesis of quantum circuits with coherent multi-start optimization”
Nikita A. Nemkov, Evgeniy O. Kiktenko, Ilia A. Luchnikov, and Aleksey K. Fedorov · 2023
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“Molecular Quantum Circuit Design: A Graph-Based Approach”
Jakob S. Kottmann · 2023
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“Qfactor: A domain-specific optimizer for quantum circuit instantiation” (2023)
Alon Kukliansky, Ed Younis, Lukasz Cincio, and Costin Iancu · 2023
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“Highly optimized quantum circuits synthesized via data-flow engines”
Péter Rakyta, Gregory Morse, Jakab Nádori, Zita Majnay-Takács, Oskar Mencer, and Zoltán Zimborás · 2024
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