Fetching the paper…
Reading the bibliography…
The importance of symmetries has recently been recognized in quantum machine learning from the simple motto: if a task exhibits a symmetry (given by a group $\mathfrak{G}$), the learning model should respect said symmetry.
J.-P. Serre et al. , Linear representations of finite groups , Vol. 42 (Springer, 1977)
1977
Earlier work this paper cites.
D. P. DiVincenzo, Two-bit gates are universal for quantum computation, Physical Review A 51
1995
Earlier work this paper cites.
S. Lloyd, Almost any quantum logic gate is universal, Physical Review Letters 75
1995
Earlier work this paper cites.
2000
Earlier work this paper cites.
B. Sagan, The symmetric group: representations, combinatorial algorithms, and symmetric functions , Vol. 203 (Springer Science & Business Media, 2001)
2001
Earlier work this paper cites.
S. G. Schirmer, I. C. Pullen, and A. I. Solomon, Controllability of quantum systems, IFAC Proceedings Volumes 36
2003
Earlier work this paper cites.
D. d’Alessandro, Introduction to quantum control and dynamics (CRC press, 2007)
2007
Earlier work this paper cites.
R. Goodman and N. R. Wallach, Symmetry, representations, and invariants , Vol. 255 (Springer, 2009)
2009
Earlier work this paper cites.
R. Zeier and T. Schulte-Herbrüggen, Symmetry principles in quantum systems theory, Journal of mathematical physics 52
2011
Earlier work this paper cites.
M. Schuld, I. Sinayskiy, and F. Petruccione, An introduction to quantum machine learning, Contemporary Physics 56
2015
Earlier work this paper cites.
Z. Zimborás, R. Zeier, T. Schulte-Herbrüggen, and D. Burgarth, Symmetry criteria for quantum simulability of effective interactions, Physical Review A 92
2015
Earlier work this paper cites.
X. Wang, D. Burgarth, and S. Schirmer, Subspace controllability of spin-1 2 chains with symmetries, Physical Review A 94
2016
Earlier work this paper cites.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature 549
2017
Earlier work this paper cites.
A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets, Nature 549
2017
Earlier work this paper cites.
F. Albertini and D. D’Alessandro, Controllability of symmetric spin networks, Journal of Mathematical Physics 59
2018
Earlier work this paper cites.
H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman, Invariant and equivariant graph networks, in International Conference on Learning Representations (2019)
2019
Earlier work this paper cites.
N. Keriven and G. Peyré, Universal invariant and equivariant graph neural networks, in Advances in Neural Information Processing Systems , Vol. 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019)
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
I. Cong, S. Choi, and M. D. Lukin, Quantum convolutional neural networks, Nature Physics 15
2019
Cited alongside, same era.
S. Sim, P. D. Johnson, and A. Aspuru-Guzik, Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, Advanced Quantum Technologies 2
2019
Cited alongside, same era.
H. Maron, O. Litany, G. Chechik, and E. Fetaya, On learning sets of symmetric elements, in Proceedings of the 37th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 119, edited by H. D. III and A. Singh (PMLR, 2020) pp. 6734–6744
2020
Cited alongside, same era.
E. R. Anschuetz, A. Bauer, B. T. Kiani, and S. Lloyd, Efficient classical algorithms for simulating symmetric quantum systems, Quantum 7
2023
Closest in time.
2023
Closest in time.
M. T. West, M. Sevior, and M. Usman, Reflection equivariant quantum neural networks for enhanced image classification, Machine Learning: Science and Technology 4
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Guo, C. R. Breum, J. Borregaard, S. Izumi, M. V. Larsen, T. Gehring, M. Christandl, J. S. Neergaard-Nielsen, and U. L. Andersen, Distributed quantum sensing in a continuous-variable entangled network, Nature Physics 16
2020
Cited alongside, same era.
J. Kübler, S. Buchholz, and B. Schölkopf, The inductive bias of quantum kernels, Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. L. Beckey, N. Gigena, P. J. Coles, and M. Cerezo, Computable and operationally meaningful multipartite entanglement measures, Phys. Rev. Lett. 127
2021
Cited alongside, same era.
S. Wang, E. Fontana, M. Cerezo, K. Sharma, A. Sone, L. Cincio, and P. J. Coles, Noise-induced barren plateaus in variational quantum algorithms, Nature Communications 12
2021
Cited alongside, same era.
M. Cerezo, G. Verdon, H.-Y. Huang, L. Cincio, and P. J. Coles, Challenges and opportunities in quantum machine learning, Nature Computational Science 10.1038/s43588-022-00311-3 (2022)
2022
Cited alongside, same era.
Z. Holmes, K. Sharma, M. Cerezo, and P. J. Coles, Connecting ansatz expressibility to gradient magnitudes and barren plateaus, PRX Quantum 3
2022
Cited alongside, same era.
2023
Closest in time.
H. Zheng, C. Kang, G. S. Ravi, H. Wang, K. Setia, F. T. Chong, and J. Liu, Sncqa: A hardware-efficient equivariant quantum convolutional circuit architecture, in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) , Vol. 1 (IEEE, 2023) pp. 236–245
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Q. T. Nguyen, L. Schatzki, P. Braccia, M. Ragone, P. J. Coles, F. Sauvage, M. Larocca, and M. Cerezo, Theory for equivariant quantum neural networks, PRX Quantum 5
2024
Closest in time.
L. Schatzki, M. Larocca, Q. T. Nguyen, F. Sauvage, and M. Cerezo, Theoretical guarantees for permutation-equivariant quantum neural networks, npj Quantum Information 10
2024
Closest in time.
F. Sauvage, M. Larocca, P. J. Coles, and M. Cerezo, Building spatial symmetries into parameterized quantum circuits for faster training, Quantum Science and Technology 9
2024
Closest in time.
I. Marvian, H. Liu, and A. Hulse, Rotationally invariant circuits: Universality with the exchange interaction and two ancilla qubits, Physical Review Letters 132
2024
Closest in time.
Z. Dong, M. Comajoan Cara, G. R. Dahale, R. T. Forestano, S. Gleyzer, D. Justice, K. Kong, T. Magorsch, K. T. Matchev, K. Matcheva, et al. , Z2 × \times z2 equivariant quantum neural networks: Benchmarking against classical neural networks, Axioms 13
2024
Closest in time.
R. T. Forestano, M. Comajoan Cara, G. R. Dahale, Z. Dong, S. Gleyzer, D. Justice, K. Kong, T. Magorsch, K. T. Matchev, K. Matcheva, et al. , A comparison between invariant and equivariant classical and quantum graph neural networks, Axioms 13
2024
Closest in time.
2024
Closest in time.
D. García-Martín, M. Larocca, and M. Cerezo, Effects of noise on the overparametrization of quantum neural networks, Phys. Rev. Res. 6
2024
Closest in time.