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Quantum computing has the potential to improve our ability to solve certain optimization problems that are computationally difficult for classical computers, by offering new algorithmic approaches that may provide speedups under specific conditions.
Some simplified np-complete problems
Michael R Garey, David S Johnson, and Larry Stockmeyer · 1974
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Rank-two relaxation heuristics for max-cut and other binary quadratic programs
Samuel Burer, Renato Monteiro, and Yin Zhang · 2001
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Exploring network structure, dynamics, and function using networkx
A. Hagberg, D.A. Schult, and P.J. Swart · 2008
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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A tutorial on formulating qubo models
Fred Glover and Gary Kochenberger · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 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 PD Sawaya, Sukin Sim, Libor Veis, and Alan Aspuru-Guzik · 2019
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A hybrid approach for solving optimization problems on small quantum computers
Ruslan Shaydulin, Hayato Ushijima-Mwesigwa, Christian F. A. Negre, Ilya Safro, Susan M. Mniszewski, and Yuri Alexeev · 2019
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An adaptive variational algorithm for exact molecular simulations on a quantum computer
Harper R Grimsley, Sophia E Economou, Edwin Barnes, and Nicholas J Mayhall · 2019
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Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models
Benedek Rozemberczki and Rik Sarkar · 2020
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Multilevel combinatorial optimization across quantum architectures
Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Christian FA Negre, Susan M Mniszewski, Yuri Alexeev, and Ilya Safro · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Classical symmetries and the quantum approximate optimization algorithm
Ruslan Shaydulin, Stuart Hadfield, Tad Hogg, and Ilya Safro · 2021
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Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
Linghua Zhu, Ho Lun Tang, George S Barron, FA Calderon-Vargas, Nicholas J Mayhall, Edwin Barnes, and Sophia E Economou · 2022
Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, and et al · 2024
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A review on quantum approximate optimization algorithm and its variants
Kostas Blekos, Dean Brand, Andrea Ceschini, Chiao-Hui Chou, Rui-Hao Li, Komal Pandya, and Alessandro Summer · 2024
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Grovergpt: A large language model with 8 billion parameters for quantum searching
Haoran Wang, Pingzhi Li, Min Chen, Jinglei Cheng, Junyu Liu, and Tianlong Chen · 2024
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The generative quantum eigensolver (gqe) and its application for ground state search
Kouhei Nakaji, Lasse Bjørn Kristensen, Jorge A Campos-Gonzalez-Angulo, Mohammad Ghazi Vakili, Haozhe Huang, Mohsen Bagherimehrab, Christoph Gorgulla, FuTe Wong, Alex McCaskey, Jin-Sung Kim, et al · 2024
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Layer VQE: A variational approach for combinatorial optimization on noisy quantum computers
Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin, Ilya Safro, Yuri Alexeev, and Lukasz Cincio · 2022
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Quantum bridge analytics i: a tutorial on formulating and using qubo models
Fred Glover, Gary Kochenberger, Rick Hennig, and Yu Du · 2022
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Hybrid quantum-classical algorithms for approximate graph coloring
Sergey Bravyi, Alexander Kliesch, Robert Koenig, and Eugene Tang · 2022
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Quantum computing for finance
Dylan Herman, Cody Googin, Xiaoyuan Liu, Yue Sun, Alexey Galda, Ilya Safro, Marco Pistoia, and Yuri Alexeev · 2023
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cuquantum sdk: A high-performance library for accelerating quantum science
Harun Bayraktar, Ali Charara, David Clark, Saul Cohen, Timothy Costa, Yao-Lung L Fang, Yang Gao, Jack Guan, John Gunnels, Azzam Haidar, et al · 2023
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Similarity-based parameter transferability in the quantum approximate optimization algorithm
Alexey Galda, Eesh Gupta, Jose Falla, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, and Ilya Safro · 2023
Cited alongside, same era.
NVIDIA CUDA-Q framework
NVIDIA Corporation
Cited in the paper.
Boris Tsvelikhovskiy, Ilya Safro, and Yuri Alexeev · 2024
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Graph representation learning for parameter transferability in quantum approximate optimization algorithm
Jose Falla, Quinn Langfitt, Yuri Alexeev, and Ilya Safro · 2024
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Mlqaoa: Graph learning accelerated hybrid quantum-classical multilevel qaoa
Bao Bach, Jose Falla, and Ilya Safro · 2024
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Scaling up the quantum divide and conquer algorithm for combinatorial optimization
Cameron Ibrahim, Teague Tomesh, Zain Saleem, and Ilya Safro · 2024
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Shunya Minami, Kouhei Nakaji, Yohichi Suzuki, Alán Aspuru-Guzik, and Tadashi Kadowaki · 2025
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NVIDIA CUDA-Q
NVIDIA Corporation · 2025
Closest in time.
NVIDIA DGX Quantum
NVIDIA Corporation · 2025
Closest in time.