Fetching the paper…
Reading the bibliography…
We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron.
The perceptron - a perceiving and recognizing automaton
F. Rosenblatt · 1957
Earlier work this paper cites.
Storage capacity of memory networks with binary couplings
Krauth, Werner and Mézard, Marc · 1989
Earlier work this paper cites.
Dynamics of learning for the binary perceptron problem
Heinz Horner · 1992
Earlier work this paper cites.
Quantum annealing: A new method for minimizing multidimensional functions
A. B. Finnila, M. A. Gomez, C. Sebenik, C. Stenson, and J. D. Doll · 1994
Earlier work this paper cites.
Universal quantum simulators
Seth Lloyd · 1996
Earlier work this paper cites.
Quantum annealing in the transverse ising model
Tadashi Kadowaki and Hidetoshi Nishimori · 1998
Earlier work this paper cites.
Quantum annealing of a disordered magnet
J. Brooke, D. Bitko, T. F. Rosenbaum, and G. Aeppli · 1999
Earlier work this paper cites.
Quantum Computation and Quantum Information
M. Nielsen and I. L. Chuang · 2000
Earlier work this paper cites.
A quantum adiabatic evolution algorithm applied to random instances of an NP-Complete problem
E. Farhi, J. Goldstone, S. Gutmann, J. Lapan, A. Lundgren, and D. Preda · 2001
Earlier work this paper cites.
Theory of quantum annealing of an Ising spin glass
Giuseppe E. Santoro, R. Martoňák, E. Tosatti, and R. Car · 2002
Earlier work this paper cites.
Optimization using quantum mechanics: Quantum annealing through adiabatic evolution
Giuseppe E. Santoro and Erio Tosatti · 2006
Earlier work this paper cites.
Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
Earlier work this paper cites.
Supervised machine learning: A review of classification techniques
S. B. Kotsiantis · 2007
Earlier work this paper cites.
Quantum annealing with manufactured spins
M. W. Johnson et al · 2011
Earlier work this paper cites.
The quantum adiabatic algorithm applied to random optimization problems: The quantum spin glass perspective
V. Bapst, L. Foini, F. Krzakala, G. Semerjian, and F. Zamponi · 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.
Defining and detecting quantum speedup
Troels F. Rønnow, Zhihui Wang, Joshua Job, Sergio Boixo, Sergei V. Isakov, David Wecker, John M. Martinis, Daniel A. Lidar, and Matthias Troyer · 2014
Earlier work this paper cites.
Ising formulations of many np problems
Andrew Lucas · 2014
Earlier work this paper cites.
Undecidability of the spectral gap
Toby S Cubitt, David Perez-Garcia, and Michael M Wolf · 2015
Earlier work this paper cites.
Simulating a perceptron on a quantum computer
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione · 2015
Cited alongside, same era.
The theory of variational hybrid quantum-classical algorithms
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
Cited alongside, same era.
Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes
Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, and Riccardo Zecchina · 2016
Cited alongside, same era.
Digitized adiabatic quantum computing with a superconducting circuit
R. Barends, A. Shabani, L. Lamata, J. Kelly, A. Mezzacapo, U. Las Heras, R. Babbush, A. G. Fowler, B. Campbell, Yu Chen, Z. Chen, B. Chiaro, A. Dunsworth, E. Jeffrey, E. Lucero, A. Megrant, J. Y. Mutus, M. Neeley, C. Neill, P. J. J. O’Malley, C. Quintana, P. Roushan, D. Sank, A. Vainsencher, J. Wenner, T. C. White, E. Solano, H. Neven, and John M. Martinis · 2016
Cited alongside, same era.
Quantum algorithms for fixed qubit architectures
E. Farhi, J. Goldstone, S. Gutmann, and H. Neven · 2017
Cited alongside, same era.
Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2019
Later among the works it cites.
Optimal quantum control with digitized quantum annealing
Glen Bigan Mbeng, Rosario Fazio, and Giuseppe E. Santoro · 2019
Later among the works it cites.
Training the quantum approximate optimization algorithm without access to a quantum processing unit
Michael Streif and Martin Leib · 2019
Later among the works it cites.
Optimal working point in digitized quantum annealing
Glen Bigan Mbeng, Luca Arceci, and Giuseppe E. Santoro · 2019
Later among the works it cites.
An artificial neuron implemented on an actual quantum processor
Francesco Tacchino, Chiara Macchiavello, Dario Gerace, and Daniele Bajoni · 2019
Later among the works it cites.
On the universality of the quantum approximate optimization algorithm
M. E. S. Morales, J. D. Biamonte, and Z. Zimborás · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Quantum machine learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd · 2017
Cited alongside, same era.
Inverse statistical problems: from the inverse ising problem to data science
H Chau Nguyen, Riccardo Zecchina, and Johannes Berg · 2017
Cited alongside, same era.
Adiabatic quantum computation
Tameem Albash and Daniel A. Lidar · 2018
Cited alongside, same era.
Quantum approximate optimization is computationally universal
Seth Lloyd · 2018
Cited alongside, same era.
Quantum Computing in the NISQ era and beyond
John Preskill · 2018
Cited alongside, same era.
Efficiency of quantum vs. classical annealing in nonconvex learning problems
Carlo Baldassi and Riccardo Zecchina · 2018
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Exploring entanglement and optimization within the hamiltonian variational ansatz
Roeland Wiersema, Cunlu Zhou, Yvette de Sereville, Juan Felipe Carrasquilla, Yong Baek Kim, and Henry Yuen · 2020
Later among the works it cites.
Polynomial scaling of the quantum approximate optimization algorithm for ground-state preparation of the fully connected p p -spin ferromagnet in a transverse field
Matteo M. Wauters, Glen B. Mbeng, and Giuseppe E. Santoro · 2020
Later among the works it cites.
The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick model at infinite size
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Leo Zhou · 2020
Later among the works it cites.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
Later among the works it cites.
Variational quantum algorithms
M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles · 2021
Closest in time.
Behavior of analog quantum algorithms
Lucas T. Brady, Lucas Kocia, Przemyslaw Bienias, Aniruddha Bapat, Yaroslav Kharkov, and Alexey V. Gorshkov · 2021
Closest in time.
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 · 2021
Closest in time.
Learning through atypical”phase transitions”in overparameterized neural networks
Carlo Baldassi, Clarissa Lauditi, Enrico M Malatesta, Rosalba Pacelli, Gabriele Perugini, and Riccardo Zecchina · 2021
Closest in time.
Transferability of optimal qaoa parameters between random graphs
Alexey Galda, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, and Ilya Safro · 2021
Closest in time.
Parameter concentrations in quantum approximate optimization
V. Akshay, D. Rabinovich, E. Campos, and J. Biamonte · 2021
Closest in time.
Cost function dependent barren plateaus in shallow parametrized quantum circuits
M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles · 2021
Closest in time.
Variational neural annealing
Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, and Juan Carrasquilla · 2021
Closest in time.