Fetching the paper…
Reading the bibliography…
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential.
On random graphs i
Erdos, P. & Renyi, A · 1959
Earlier work this paper cites.
Linear representations of finite groups , vol. 42 (Springer, 1977)
Serre, J.-P. et al · 1977
Earlier work this paper cites.
Representation Theory: A First Course (Springer, 1991)
Fulton, W. & Harris, J · 1991
Earlier work this paper cites.
Spin squeezing and reduced quantum noise in spectroscopy
Wineland, D. J., Bollinger, J. J., Itano, W. M., Moore, F. & Heinzen, D. J · 1992
Earlier work this paper cites.
Squeezed spin states
Kitagawa, M. & Ueda, M · 1993
Earlier work this paper cites.
A regularity condition of the information matrix of a multilayer perceptron network
Fukumizu, K · 1996
Earlier work this paper cites.
Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization
Zhu, C., Byrd, R. H., Lu, P. & Nocedal, J · 1997
Earlier work this paper cites.
Overparameterization in the seminonparametric density estimation
Liu, M. & Zhang, H. H · 1998
Earlier work this paper cites.
Uniform Central Limit Theorems (Cambridge University Press, 1999)
Dudley, R. M · 1999
Earlier work this paper cites.
The symmetric group: representations, combinatorial algorithms, and symmetric functions , vol. 203 (Springer Science & Business Media, 2001)
Sagan, B · 2001
Earlier work this paper cites.
Representation theory of semisimple groups: an overview based on examples (Princeton university press, Princeton, 2001)
Knapp, A. W · 2001
Earlier work this paper cites.
Measurement-based quantum computation on cluster states
Raussendorf, R., Browne, D. E. & Briegel, H. J · 2003
Earlier work this paper cites.
Multiparty entanglement in graph states
Hein, M., Eisert, J. & Briegel, H. J · 2004
Earlier work this paper cites.
Quantum entanglement
Horodecki, R., Horodecki, P., Horodecki, M. & Horodecki, K · 2009
Earlier work this paper cites.
Symmetry, representations, and invariants , vol. 255 (Springer, 2009)
Goodman, R. & Wallach, N. R · 2009
Earlier work this paper cites.
Quantum geometric tensor (fubini-study metric) in simple quantum system: A pedagogical introduction
Cheng, R · 2010
Earlier work this paper cites.
Symmetry principles in quantum systems theory
Zeier, R. & Schulte-Herbrüggen, T · 2011
Earlier work this paper cites.
The church of the symmetric subspace
Harrow, A. W · 2013
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.
Understanding machine learning: From theory to algorithms (Cambridge university press, 2014)
Shalev-Shwartz, S. & Ben-David, S · 2014
Earlier work this paper cites.
An introduction to quantum machine learning
Schuld, M., Sinayskiy, I. & Petruccione, F · 2015
Earlier work this paper cites.
Group equivariant convolutional networks
Cohen, T. & Welling, M · 2016
Earlier work this paper cites.
Multipartite entanglement
Walter, M., Gross, D. & Eisert, J · 2016
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. T. et al · 2017
Earlier work this paper cites.
Quantum machine learning
Biamonte, J. et al · 2017
Earlier work this paper cites.
Deep sets
Zaheer, M. et al · 2017
Earlier work this paper cites.
Unsupervised machine learning on a hybrid quantum computer
Otterbach, J. S. et al · 2017
Earlier work this paper cites.
On loss functions for deep neural networks in classification
Janocha, K. & Czarnecki, W. M · 2017
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.
Symbolic integration with respect to the haar measure on the unitary groups
Puchala, Z. & Miszczak, J. A · 2017
Earlier work this paper cites.
Reducing duplicate filters in deep neural networks
RoyChowdhury, A., Sharma, P., Learned-Miller, E. & Roy, A · 2017
Earlier work this paper cites.
On the generalization of equivariance and convolution in neural networks to the action of compact groups
Kondor, R. & Trivedi, S · 2018
Earlier work this paper cites.
Roto-translation covariant convolutional networks for medical image analysis
Bekkers, E. J. et al · 2018
Earlier work this paper cites.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N. et al · 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.
Quantum generative adversarial networks
Dallaire-Demers, P.-L. & Killoran, N · 2018
Earlier work this paper cites.
Supervised learning with quantum computers , vol. 17 (Springer, 2018)
Schuld, M. & Petruccione, F · 2018
Earlier work this paper cites.
Controllability of symmetric spin networks
Albertini, F. & D’Alessandro, D · 2018
Earlier work this paper cites.
Learning the quantum algorithm for state overlap
Cincio, L., Subaşı, Y., Sornborger, A. T. & Coles, P. J · 2018
Earlier work this paper cites.
SGD learns over-parameterized networks that provably generalize on linearly separable data
Brutzkus, A., Globerson, A., Malach, E. & Shalev-Shwartz, S · 2018
Earlier work this paper cites.
High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge University Press, 2018)
Vershynin, R · 2018
Earlier work this paper cites.
Rezende, D. J., Racanière, S., Higgins, I. & Toth, P · 2019
Earlier work this paper cites.
Hamiltonian generative networks
Toth, P. et al · 2019
Earlier work this paper cites.
Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S. & Kondor, R · 2019
Earlier work this paper cites.
Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
Sim, S., Johnson, P. D. & Aspuru-Guzik, A · 2019
Earlier work this paper cites.
Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N. & Lipman, Y · 2019
Earlier work this paper cites.
Universal invariant and equivariant graph neural networks
Keriven, N. & Peyré, G · 2019
Earlier work this paper cites.
Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H. & Lipman, Y · 2019
Earlier work this paper cites.
Verdon, G. et al · 2019
Earlier work this paper cites.
On the universality of invariant networks
Maron, H., Fetaya, E., Segol, N. & Lipman, Y · 2019
Earlier work this paper cites.
From the quantum approximate optimization algorithm to a quantum alternating operator ansatz
Hadfield, S. et al · 2019
Cited alongside, same era.
Quantum convolutional neural networks
Cong, I., Choi, S. & Lukin, M. D · 2019
Cited alongside, same era.
q-means: A quantum algorithm for unsupervised machine learning
Kerenidis, I., Landman, J., Luongo, A. & Prakash, A · 2019
Cited alongside, same era.
A generative modeling approach for benchmarking and training shallow quantum circuits
Benedetti, M. et al · 2019
Cited alongside, same era.
Supervised learning with quantum-enhanced feature spaces
Havlíček, V. et al · 2019
Cited alongside, same era.
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Grant, E., Wossnig, L., Ostaszewski, M. & Benedetti, M · 2019
Power of data in quantum machine learning
Huang, H.-Y. et al · 2021
Later among the works it cites.
Ece 543: Statistical learning theory (2021)
Hajek, B. & Raginsky, M · 2021
Later among the works it cites.
Fisher Information in Noisy Intermediate-Scale Quantum Applications
Meyer, J. J · 2021
Later among the works it cites.
Expressibility of the alternating layered ansatz for quantum computation
Nakaji, K. & Yamamoto, N · 2021
Later among the works it cites.
Deep learning: a statistical viewpoint
Bartlett, P. L., Montanari, A. & Rakhlin, A · 2021
Later among the works it cites.
Symmetry group equivariant architectures for physics
Bogatskiy, A. et al · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y. & Song, Z · 2019
Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Allen-Zhu, Z., Li, Y. & Liang, Y · 2019
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Du, S. S., Zhai, X., Poczos, B. & Singh, A · 2019
Cited alongside, same era.
Equivariant flows: Exact likelihood generative learning for symmetric densities
Köhler, J., Klein, L. & Noé, F · 2020
Cited alongside, same era.
Lorentz group equivariant neural network for particle physics
Bogatskiy, A. et al · 2020
Cited alongside, same era.
Exploring entanglement and optimization within the Hamiltonian variational ansatz
Wiersema, R. et al · 2020
Cited alongside, same era.
Challenges and opportunities in quantum machine learning
Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L. & Coles, P. J · 2022
Closest in time.
Provably efficient machine learning for quantum many-body problems
Huang, H.-Y., Kueng, R., Torlai, G., Albert, V. V. & Preskill, J · 2022
Closest in time.
Group-invariant quantum machine learning
Larocca, M. et al · 2022
Closest in time.
Building spatial symmetries into parameterized quantum circuits for faster training
Sauvage, F., Larocca, M., Coles, P. J. & Cerezo, M · 2022
Closest in time.
Zheng, H., Li, Z., Liu, J., Strelchuk, S. & Kondor, R · 2022
Closest in time.
A theory for equivariant quantum neural networks
Nguyen, Q. T. et al · 2022
Closest in time.
Symmetric pruning in quantum neural networks
Wang, X. et al · 2022
Closest in time.
Representation theory for geometric quantum machine learning
Ragone, M. et al · 2022
Closest in time.
An analytic theory for the dynamics of wide quantum neural networks
Liu, J. et al · 2022
Closest in time.
Beyond barren plateaus: Quantum variational algorithms are swamped with traps
Anschuetz, E. R. & Kiani, B. T · 2022
Closest in time.
Non-trivial symmetries in quantum landscapes and their resilience to quantum noise
Fontana, E., Cerezo, M., Arrasmith, A., Rungger, I. & Coles, P. J · 2022
Closest in time.
Trainability of dissipative perceptron-based quantum neural networks
Sharma, K., Cerezo, M., Cincio, L. & Coles, P. J · 2022
Closest in time.
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Holmes, Z., Sharma, K., Cerezo, M. & Coles, P. J · 2022
Closest in time.
Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
Larocca, M. et al · 2022
Closest in time.
Equivariant quantum graph circuits
Mernyei, P., Meichanetzidis, K. & Ceylan, I. I · 2022
Closest in time.
Permutation equivariant layers for higher order interactions
Pan, H. & Kondor, R · 2022
Closest in time.
Generalization in quantum machine learning from few training data
Caro, M. C. et al · 2022
Closest in time.
A hierarchy of multipartite correlations based on concentratable entanglement
Schatzki, L., Liu, G., Cerezo, M. & Chitambar, E · 2022
Closest in time.
Inference-based quantum sensing
Huerta Alderete, C. et al · 2022
Closest in time.
Noisy intermediate-scale quantum algorithms
Bharti, K. et al · 2022
Closest in time.
A quantum processor based on coherent transport of entangled atom arrays
Bluvstein, D. et al · 2022
Closest in time.
Optimal metrology with programmable quantum sensors
Marciniak, C. D. et al · 2022
Closest in time.
Avoiding barren plateaus using classical shadows
Sack, S. H., Medina, R. A., Michailidis, A. A., Kueng, R. & Serbyn, M · 2022
Closest in time.
Surviving the barren plateau in variational quantum circuits with bayesian learning initialization
Rad, A., Seif, A. & Linke, N. M · 2022
Closest in time.
Avoiding barren plateaus with classical deep neural networks
Friedrich, L. & Maziero, J · 2022
Closest in time.
Beinit: Avoiding barren plateaus in variational quantum algorithms
Kulshrestha, A. & Safro, I · 2022
Closest in time.
Avoiding barren plateaus via transferability of smooth solutions in Hamiltonian variational ansatz
Mele, A. A., Mbeng, G. B., Santoro, G. E., Collura, M. & Torta, P · 2022
Closest in time.
Gaussian initializations help deep variational quantum circuits escape from the barren plateau
Zhang, K., Hsieh, M.-H., Liu, L. & Tao, D · 2022
Closest in time.
Variational quantum state eigensolver
Cerezo, M., Sharma, K., Arrasmith, A. & Coles, P. J · 2022
Closest in time.
The presence and absence of barren plateaus in tensor-network based machine learning
Liu, Z., Yu, L.-W., Duan, L.-M. & Deng, D.-L · 2022
Closest in time.
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Arrasmith, A., Holmes, Z., Cerezo, M. & Coles, P. J · 2022
Closest in time.
Out-of-distribution generalization for learning quantum dynamics
Caro, M. C. et al · 2022
Closest in time.
Efficient measure for the expressivity of variational quantum algorithms
Du, Y., Tu, Z., Yuan, X. & Tao, D · 2022
Closest in time.
Exponential concentration and untrainability in quantum kernel methods
Thanasilp, S., Wang, S., Cerezo, M. & Holmes, Z · 2022
Closest in time.
Efficient classical algorithms for simulating symmetric quantum systems
Anschuetz, E. R., Bauer, A., Kiani, B. T. & Lloyd, S · 2022
Closest in time.
Quantum computational phase transition in combinatorial problems
Zhang, B., Sone, A. & Zhuang, Q · 2022
Closest in time.
Understanding implicit regularization in over-parameterized single index model
Fan, J., Yang, Z. & Yu, M · 2022
Closest in time.
Exploiting symmetry in variational quantum machine learning
Meyer, J. J. et al · 2023
Closest in time.
Speeding up learning quantum states through group equivariant convolutional quantum ansätze
Zheng, H., Li, Z., Liu, J., Strelchuk, S. & Kondor, R · 2023
Closest in time.
Theory of overparametrization in quantum neural networks
Larocca, M., Ju, N., García-Martín, D., Coles, P. J. & Cerezo, M · 2023
Closest in time.
Equivariant quantum circuits for learning on weighted graphs
Skolik, A., Cattelan, M., Yarkoni, S., Bäck, T. & Dunjko, V · 2023
Closest in time.
Adaptive, problem-tailored variational quantum eigensolver mitigates rough parameter landscapes and barren plateaus
Grimsley, H. R., Mayhall, N. J., Barron, G. S., Barnes, E. & Economou, S. E · 2023
Closest in time.
On the universality of s n s_{n} -equivariant k k -body gates
Kazi, S., Larocca, M. & Cerezo, M · 2023
Closest in time.
A unified theory of barren plateaus for deep parametrized quantum circuits
Ragone, M. et al · 2023
Closest in time.
The adjoint is all you need: Characterizing barren plateaus in quantum ansätze
Fontana, E. et al · 2023
Closest in time.