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A promising approach to useful computational quantum advantage is to use variational quantum algorithms for optimisation problems.
A direct search optimization method that models the objective and constraint functions by linear interpolation
Michael JD Powell · 1994
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Selecting portfolios with fixed costs and minimum transaction lots
Hans Kellerer, Renata Mansini, and M Grazia Speranza · 2000
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Number partitioning
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Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
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Multi-way number partitioning
Richard E Korf · 2009
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Benchmarking derivative-free optimization algorithms
Jorge J Moré and Stefan M Wild · 2009
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Ising formulations of many np problems
Andrew Lucas · 2014
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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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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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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Quantum computing in the nisq era and beyond
John Preskill · 2018
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Information perspective to probabilistic modeling: Boltzmann machines versus born machines
Song Cheng, Jing Chen, and Lei Wang · 2018
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Adiabatic quantum computation
Tameem Albash and Daniel A Lidar · 2018
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Fernando GSL Brandao, Michael Broughton, Edward Farhi, Sam Gutmann, and Hartmut Neven · 2018
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Quantum approximate optimization algorithm for maxcut: A fermionic view
Zhihui Wang, Stuart Hadfield, Zhang Jiang, and Eleanor G Rieffel · 2018
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Performance of the quantum approximate optimization algorithm on the maximum cut problem
Gavin E Crooks · 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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Quantum optimization using variational algorithms on near-term quantum devices
Nikolaj Moll, Panagiotis Barkoutsos, Lev S Bishop, Jerry M Chow, Andrew Cross, Daniel J Egger, Stefan Filipp, Andreas Fuhrer, Jay M Gambetta, Marc Ganzhorn, et al · 2018
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Generalized unitary coupled cluster wave functions for quantum computation
Joonho Lee, William J Huggins, Martin Head-Gordon, and K Birgitta Whaley · 2018
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Towards practical quantum variational algorithms
Dave Wecker, Matthew Hastings, and Matthias Troyer · 2018
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Sequential minimal optimization for quantum-classical hybrid algorithms
Ken M Nakanishi, Keisuke Fujii, and Synge Todo · 2020
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Reinforcement learning assisted quantum optimization
Matteo M Wauters, Emanuele Panizon, Glen B Mbeng, and Giuseppe E Santoro · 2020
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Quantum optimization with a novel gibbs objective function and ansatz architecture search
Li Li, Minjie Fan, Marc Coram, Patrick Riley, Stefan Leichenauer, et al · 2020
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Improving variational quantum optimization using cvar
Panagiotis Kl Barkoutsos, Giacomo Nannicini, Anton Robert, Ivano Tavernelli, and Stefan Woerner · 2020
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Variational quantum algorithms
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al · 2020
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Quantum supremacy using a programmable superconducting processor
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Obstacles to state preparation and variational optimization from symmetry protection
Sergey Bravyi, Alexander Kliesch, Robert Koenig, and Eugene Tang · 2019
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A generative modeling approach for benchmarking and training shallow quantum circuits
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Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou · 2019
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Quantum computing for finance: Overview and prospects
Roman Orus, Samuel Mugel, and Enrique Lizaso · 2019
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Reverse quantum annealing approach to portfolio optimization problems
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Performance of hybrid quantum-classical variational heuristics for combinatorial optimization
Giacomo Nannicini · 2019
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To quantum or not to quantum: towards algorithm selection in near-term quantum optimization
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Quantum computing for finance: state of the art and future prospects
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Portfolio optimization of 40 stocks using the dwave quantum annealer
Jeffrey Cohen, Alex Khan, and Clark Alexander · 2020
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Samuel Mugel, Carlos Kuchkovsky, Escolastico Sanchez, Samuel Fernandez-Lorenzo, Jorge Luis-Hita, Enrique Lizaso, and Roman Orus · 2020
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Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices
Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, and Mikhail D Lukin · 2020
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Layerwise learning for quantum neural networks
Andrea Skolik, Jarrod R McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib · 2021
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Quantum annealing initialization of the quantum approximate optimization algorithm
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Quantum walk-based portfolio optimisation
N. Slate, E. Matwiejew, S. Marsh, and J. B. Wang · 2021
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Reachability Deficits in Quantum Approximate Optimization of Graph Problems
V. Akshay, H. Philathong, I. Zacharov, and J. Biamonte · 2021
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