Fetching the paper…
Reading the bibliography…
Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term.
A Quantum Approximate Optimization Algorithm for continuous problems
Guillaume Verdon, Juan Miguel Arrazola, Kamil Brádler, and Nathan Killoran · 1902
Earlier work this paper cites.
Learning to learn with quantum neural networks via classical neural networks
Guillaume Verdon, Michael Broughton, Jarrod R. McClean, Kevin J. Sung, Ryan Babbush, Zhang Jiang, Hartmut Neven, and Masoud Mohseni · 1907
Earlier work this paper cites.
Quantum Graph Neural Networks, September 2019
Guillaume Verdon, Trevor McCourt, Enxhell Luzhnica, Vikash Singh, Stefan Leichenauer, and Jack Hidary · 1909
Earlier work this paper cites.
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, and Prasanna Balaprakash · 1911
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 1912
Earlier work this paper cites.
An application of combinatorial optimization to statistical physics and circuit layout design
Francisco Barahona, Martin Grötschel, Michael Jünger, and Gerhard Reinelt · 1988
Earlier work this paper cites.
Computers and Intractability; A Guide to the Theory of NP-Completeness
Michael R. Garey and David S. Johnson · 1990
Earlier work this paper cites.
Optimization, approximation, and complexity classes
Christos H. Papadimitriou and Mihalis Yannakakis · 1991
Earlier work this paper cites.
A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation
M. J. D. Powell · 1994
Earlier work this paper cites.
Semidefinite programming
Michael Overton and Henry Wolkowicz · 1997
Earlier work this paper cites.
Quantum annealing in the transverse Ising model
Tadashi Kadowaki and Hidetoshi Nishimori · 1998
Earlier work this paper cites.
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
Stuart Hadfield, Zhihui Wang, Bryan O’Gorman, Eleanor G. Rieffel, Davide Venturelli, and Rupak Biswas · 1999
Earlier work this paper cites.
On the power of unique 2-prover 1-round games
Subhash Khot · 2002
Earlier work this paper cites.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning, August 2021
Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao, Roeland Wiersema, Hsin-Yuan Huang, Jarrod R. McClean, Ryan Babbush, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, and Masoud Mohseni · 2003
Earlier work this paper cites.
Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts
Jan Poland and Thomas Zeugmann · 2006
Earlier work this paper cites.
Optimal Inapproximability Results for MAX-CUT and Other 2-Variable CSPs?
Subhash Khot, Guy Kindler, Elchanan Mossel, and Ryan O’Donnell · 2007
Earlier work this paper cites.
The Graph Neural Network Model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Quantum computation and quantum information
Michael A. Nielsen and Isaac L. Chuang · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Improving the Quantum Approximate Optimization Algorithm with postselection
Sami Boulebnane · 2011
Earlier work this paper cites.
ADADELTA: An Adaptive Learning Rate Method, December 2012
Matthew D. Zeiler · 2012
Earlier work this paper cites.
Spectral redemption in clustering sparse networks
Florent Krzakala, Cristopher Moore, Elchanan Mossel, Joe Neeman, Allan Sly, Lenka Zdeborová, and Pan Zhang · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
A Quantum Approximate Optimization Algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
The theory of variational hybrid quantum-classical algorithms
Jarrod R. McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
Earlier work this paper cites.
TensorFlow: A system for large-scale machine learning, May 2016
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
Learning to Optimize, June 2016
Ke Li and Jitendra Malik · 2016
Earlier work this paper cites.
Practical optimization for hybrid quantum-classical algorithms
Gian Giacomo Guerreschi and Mikhail Smelyanskiy · 2017
Earlier work this paper cites.
Learning Combinatorial Optimization Algorithms over Graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
Earlier work this paper cites.
Proximal Policy Optimization Algorithms, August 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Quantum Computing in the NISQ era and beyond
John Preskill · 2018
Earlier work this paper cites.
Quantum circuit learning
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii · 2018
Cited alongside, same era.
Classification with Quantum Neural Networks on Near Term Processors
Edward Farhi and Hartmut Neven · 2018
Cited alongside, same era.
Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Learning Heuristics for the TSP by Policy Gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
Cited alongside, same era.
Classical and quantum bounded depth approximation algorithms
Matthew B. Hastings · 2019
Exponentially Many Local Minima in Quantum Neural Networks
Xuchen You and Xiaodi Wu · 2021
Closest in time.
Avoiding local minima in Variational Quantum Algorithms with Neural Networks
Javier Rivera-Dean, Patrick Huembeli, Antonio Acín, and Joseph Bowles · 2021
Closest in time.
FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits, May 2021
Frederic Sauvage, Sukin Sim, Alexander A. Kunitsa, William A. Simon, Marta Mauri, and Alejandro Perdomo-Ortiz · 2021
Closest in time.
Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation
Alba Cervera-Lierta, Jakob S. Kottmann, and Alán Aspuru-Guzik · 2021
Closest in time.
Combinatorial Optimization and Reasoning with Graph Neural Networks
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Overview and Comparison of Gate Level Quantum Software Platforms
Ryan LaRose · 2019
Cited alongside, same era.
Experimental performance of graph neural networks on random instances of max-cut
Weichi Yao, Afonso S. Bandeira, and Soledad Villar · 2019
Cited alongside, same era.
Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski · 2019
Cited alongside, same era.
Supervised Community Detection with Line Graph Neural Networks
Zhengdao Chen, Lisha Li, and Joan Bruna · 2019
Cited alongside, same era.
Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
Cited alongside, same era.
Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Cited alongside, same era.
Closest in time.
End-to-End Constrained Optimization Learning: A Survey
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Bryan Wilder · 2021
Closest in time.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, May 2021
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Closest in time.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2021
Closest in time.
Learning TSP Requires Rethinking Generalization
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent · 2021
Closest in time.
Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
Julien Gacon, Christa Zoufal, Giuseppe Carleo, and Stefan Woerner · 2021
Closest in time.
Optimizing quantum heuristics with meta-learning
Max Wilson, Rachel Stromswold, Filip Wudarski, Stuart Hadfield, Norm M. Tubman, and Eleanor G. Rieffel · 2021
Closest in time.
The power of quantum neural networks
Amira Abbas, David Sutter, Christa Zoufal, Aurelien Lucchi, Alessio Figalli, and Stefan Woerner · 2021
Closest in time.
Noisy intermediate-scale quantum algorithms
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik · 2022
Closest in time.
The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou · 2022
Closest in time.
The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model
Joao Basso, Edward Farhi, Kunal Marwaha, Benjamin Villalonga, and Leo Zhou · 2022
Closest in time.
Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
Linghua Zhu, Ho Lun Tang, George S. Barron, F. A. Calderon-Vargas, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou · 2022
Closest in time.
Evolving objective function for improved variational quantum optimization
Ioannis Kolotouros and Petros Wallden · 2022
Closest in time.
A case study of variational quantum algorithms for a job shop scheduling problem
David Amaro, Matthias Rosenkranz, Nathan Fitzpatrick, Koji Hirano, and Mattia Fiorentini · 2022
Closest in time.
Johannes Weidenfeller, Lucia C. Valor, Julien Gacon, Caroline Tornow, Luciano Bello, Stefan Woerner, and Daniel J. Egger · 2022
Closest in time.
Diagnosing barren plateaus with tools from quantum optimal control, March 2022
Martin Larocca, Piotr Czarnik, Kunal Sharma, Gopikrishnan Muraleedharan, Patrick J. Coles, and M. Cerezo · 2022
Closest in time.
Combinatorial optimization with physics-inspired graph neural networks
Martin J. A. Schuetz, J. Kyle Brubaker, and Helmut G. Katzgraber · 2022
Closest in time.
Hybrid quantum-classical algorithms for approximate graph coloring
Sergey Bravyi, Alexander Kliesch, Robert Koenig, and Eugene Tang · 2022
Closest in time.
Group-Invariant Quantum Machine Learning
Martín Larocca, Frédéric Sauvage, Faris M. Sbahi, Guillaume Verdon, Patrick J. Coles, and M. Cerezo · 2022
Closest in time.
Equivariant quantum circuits for learning on weighted graphs, May 2022
Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, and Vedran Dunjko · 2022
Closest in time.
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2058
Closest in time.
Filtering variational quantum algorithms for combinatorial optimization
David Amaro, Carlo Modica, Matthias Rosenkranz, Mattia Fiorentini, Marcello Benedetti, and Michael Lubasch · 2058
Closest in time.
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, and et al · 2058
Closest in time.
Training the quantum approximate optimization algorithm without access to a quantum processing unit
Michael Streif and Martin Leib · 2058
Closest in time.
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Andrew Arrasmith, Zoe Holmes, Marco Cerezo, and Patrick J Coles · 2058
Closest in time.
Matrix product state pre-training for quantum machine learning
James Dborin, Fergus Barratt, Vinul Wimalaweera, Lewis Wright, and Andrew G. Green · 2058
Closest in time.
Using models to improve optimizers for variational quantum algorithms
Kevin J. Sung, Jiahao Yao, Matthew P. Harrigan, Nicholas C. Rubin, Zhang Jiang, Lin Lin, Ryan Babbush, and Jarrod R. McClean · 2058
Closest in time.