Fetching the paper…
Reading the bibliography…
Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware.
Worst-case analysis of a new heuristic for the travelling salesman problem
Christofides, N · 1976
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
LeCun, Y., Bengio, Y. et al · 1995
Earlier work this paper cites.
Why feed-forward networks are in a bad shape
Smagt, P. v. d. & Hirzinger, G · 1998
Earlier work this paper cites.
A quantum approximate optimization algorithm
Farhi, E., Goldstone, J. & Gutmann, S · 2014
Earlier work this paper cites.
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A. et al · 2014
Earlier work this paper cites.
Ising formulations of many np problems
Lucas, A · 2014
Earlier work this paper cites.
Vinyals, O., Fortunato, M. & Jaitly, N · 2015
Earlier work this paper cites.
sin ( ω x ) \mathrm{sin}(\omega x) can approximate almost every finite set of samples
Harman, G. H., Kulkarni, S. R. & Narayanan, H · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V. et al · 2015
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
Silver, D. et al · 2016
Earlier work this paper cites.
Quantum supremacy through the quantum approximate optimization algorithm
Farhi, E. & Harrow, A. W · 2016
Earlier work this paper cites.
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Kandala, A. et al · 2017
Earlier work this paper cites.
Learning combinatorial optimization algorithms over graphs
Dai, H., Khalil, E. B., Zhang, Y., Dilkina, B. & Song, L · 2017
Earlier work this paper cites.
Adapt-vqe: An exact variational algorithm for fermionic simulations on a quantum computer
Grimsley, H. R., Economou, S. E., Barnes, E. & Mayhall, N. J · 2018
Earlier work this paper cites.
Barren plateaus in quantum neural network training landscapes
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R. & Neven, H · 2018
Earlier work this paper cites.
Reinforcement learning for solving the vehicle routing problem
Nazari, M., Oroojlooy, A., Snyder, L. V. & Takáč, M · 2018
Earlier work this paper cites.
Quantum approximate optimization is computationally universal
Lloyd, S · 2018
Earlier work this paper cites.
Brandao, F. G., Broughton, M., Farhi, E., Gutmann, S. & Neven, H · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction (MIT press, 2018)
Sutton, R. S. & Barto, A. G · 2018
Earlier work this paper cites.
Parameterized quantum circuits as machine learning models
Benedetti, M., Lloyd, E., Sack, S. & Fiorentini, M · 2019
Earlier work this paper cites.
Verdon, G. et al · 2019
Earlier work this paper cites.
Graph neural networks for social recommendation
Fan, W. et al · 2019
Cited alongside, same era.
What do qaoa energies reveal about graphs?
Szegedy, M · 2019
Cited alongside, same era.
Recurrent neural networks (rnns): A gentle introduction and overview
Schmidt, R. M · 2019
Cited alongside, same era.
Molecular geometry prediction using a deep generative graph neural network
Mansimov, E., Mahmood, O., Kang, S. & Cho, K · 2019
Cited alongside, same era.
Quantum convolutional neural networks
Cong, I., Choi, S. & Lukin, M. D · 2019
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Zhou, J. et al · 2020
Combinatorial optimization and reasoning with graph neural networks
Cappart, Q. et al · 2021
Later among the works it cites.
Quantum evolution kernel: Machine learning on graphs with programmable arrays of qubits
Henry, L.-P., Thabet, S., Dalyac, C. & Henriet, L · 2021
Later among the works it cites.
Quantum graph convolutional neural networks
Zheng, J., Gao, Q. & Lü, Y · 2021
Later among the works it cites.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Bronstein, M. M., Bruna, J., Cohen, T. & Veličković, P · 2021
Later among the works it cites.
Cost function dependent barren plateaus in shallow parametrized quantum circuits
Cerezo, M., Sone, A., Volkoff, T., Cincio, L. & Coles, P. J · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Chip placement with deep reinforcement learning
Mirhoseini, A. et al · 2020
Cited alongside, same era.
On the universality of the quantum approximate optimization algorithm
Morales, M. E., Biamonte, J. D. & Zimborás, Z · 2020
Cited alongside, same era.
The quantum approximate optimization algorithm needs to see the whole graph: A typical case
Farhi, E., Gamarnik, D. & Gutmann, S · 2020
Cited alongside, same era.
Obstacles to variational quantum optimization from symmetry protection
Bravyi, S., Kliesch, A., Koenig, R. & Tang, E · 2020
Cited alongside, same era.
Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices
Zhou, L., Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D · 2020
Cited alongside, same era.
A comprehensive survey on graph neural networks
Wu, Z. et al · 2020
Cited alongside, same era.
Noise-induced barren plateaus in variational quantum algorithms
Wang, S. et al · 2021
Later among the works it cites.
Learning with invariances in random features and kernel models
Mei, S., Misiakiewicz, T. & Montanari, A · 2021
Later among the works it cites.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Bengio, Y., Lodi, A. & Prouvost, A · 2021
Later among the works it cites.
Local classical max-cut algorithm outperforms p = 2 p=2 qaoa on high-girth regular graphs
Marwaha, K · 2021
Later among the works it cites.
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K. et al · 2021
Later among the works it cites.
A graph neural network for superpixel image classification
Long, J. et al · 2021
Later among the works it cites.
Equivariant quantum graph circuits
Mernyei, P., Meichanetzidis, K. & Ceylan, I. I · 2022
Closest in time.
Expectation values from the single-layer quantum approximate optimization algorithm on ising problems
Ozaeta, A., van Dam, W. & McMahon, P. L · 2022
Closest in time.
Theoretical guarantees for permutation-equivariant quantum neural networks
Schatzki, L., Larocca, M., Sauvage, F. & Cerezo, M · 2022
Closest in time.
Diagnosing barren plateaus with tools from quantum optimal control
Larocca, M. et al · 2022
Closest in time.
Generalization in quantum machine learning from few training data
Caro, M. C. et al · 2022
Closest in time.
Parameter transfer for quantum approximate optimization of weighted maxcut
Shaydulin, R., Lotshaw, P. C., Larson, J., Ostrowski, J. & Humble, T. S · 2022
Closest in time.
Group-invariant quantum machine learning
Larocca, M. et al · 2022
Closest in time.
Novel architecture of parameterized quantum circuit for graph convolutional network
Chen, Y., Wang, C., Guo, H. et al · 2022
Closest in time.
Quantum agents in the gym: a variational quantum algorithm for deep q-learning
Skolik, A., Jerbi, S. & Dunjko, V · 2022
Closest in time.
Space-efficient binary optimization for variational quantum computing
Glos, A., Krawiec, A. & Zimborás, Z · 2022
Closest in time.