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Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabilities.
S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi, “Optimization by simulated annealing,” science
1983
Earlier work this paper cites.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural computation
1991
Earlier work this paper cites.
J. Baxter, “A model of inductive bias learning,” Journal of artificial intelligence research
2000
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in neural information processing systems
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature communications
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Lucas, “Ising formulations of many NP problems,” Frontiers in physics
2014
Earlier work this paper cites.
I. S. Maria Schuld and F. Petruccione, “An introduction to quantum machine learning,” Contemporary Physics
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in Neural Information Processing Systems
2015
Earlier work this paper cites.
J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, “Molecular graph convolutions: moving beyond fingerprints,” Journal of computer-aided molecular design
2016
Earlier work this paper cites.
D. Hendrycks and K. Gimpel, “Gaussian error linear units (GELUs),” arXiv preprint arXiv:1606.08415
2016
Earlier work this paper cites.
J. Lei Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450
2016
Earlier work this paper cites.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems
2017
Earlier work this paper cites.
N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean, “Outrageously large neural networks: The sparsely-gated Mixture-of-Experts layer,” in International Conference on Learning Representations (ICLR)
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al
2017
Earlier work this paper cites.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Physical Review A
2018
Earlier work this paper cites.
P. K. Barkoutsos, J. F. Gonthier, I. Sokolov, N. Moll, G. Salis, A. Fuhrer, M. Ganzhorn, D. J. Egger, M. Troyer, A. Mezzacapo, S. Filipp, and I. Tavernelli, “Quantum algorithms for electronic structure calculations: Particle-hole Hamiltonian and optimized wave-function expansions,” Physical Review A
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence
2018
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al
2019
Cited alongside, same era.
F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandao, D. A. Buell, et al
2019
Cited alongside, same era.
2019
Cited alongside, same era.
P. Soviany, R. T. Ionescu, P. Rota, and N. Sebe, “Curriculum learning: A survey,” International Journal of Computer Vision
2022
Later among the works it cites.
J. Topping, F. D. Giovanni, B. P. Chamberlain, X. Dong, and M. M. Bronstein, “Understanding over-squashing and bottlenecks on graphs via curvature,” in International Conference on Learning Representations
2022
Later among the works it cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al
2022
Later among the works it cites.
V. Sivak, A. Eickbusch, B. Royer, S. Singh, I. Tsioutsios, S. Ganjam, A. Miano, B. Brock, A. Ding, L. Frunzio, et al
2023
Later among the works it cites.
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn, “Direct preference optimization: Your language model is secretly a reward model,” in Advances in Neural Information Processing Systems
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A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre, “Data re-uploading for a universal quantum classifier,” Quantum
2020
Cited alongside, same era.
F. J. Gil Vidal and D. O. Theis, “Input redundancy for parameterized quantum circuits,” Frontiers in Physics
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y.-H. Zhang, P.-L. Zheng, Y. Zhang, and D.-L. Deng, “Topological quantum compiling with reinforcement learning,” Phys. Rev. Lett
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of Machine Learning Research
2020
Cited alongside, same era.
S. Li, Y. Zhao, R. Varma, O. Salpekar, P. Noordhuis, T. Li, A. Paszke, J. Smith, B. Vaughan, P. Damania, and S. Chintala, “PyTorch distributed: experiences on accelerating data parallel training,” Proc. VLDB Endow
2020
Cited alongside, same era.
M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al
2021
Cited alongside, same era.
2023
Later among the works it cites.
Y. Kim, A. Eddins, S. Anand, K. X. Wei, E. Van Den Berg, S. Rosenblatt, H. Nayfeh, Y. Wu, M. Zaletel, K. Temme, et al
2023
Later among the works it cites.
Z. He, X. Zhang, C. Chen, Z. Huang, Y. Zhou, and H. Situ, “A GNN-based predictor for quantum architecture search,” Quantum Information Processing
2023
Later among the works it cites.
D. Bluvstein, S. J. Evered, A. A. Geim, S. H. Li, H. Zhou, T. Manovitz, S. Ebadi, M. Cain, M. Kalinowski, D. Hangleiter, J. P. Bonilla Ataides, N. Maskara, I. Cong, X. Gao, P. Sales Rodriguez, T. Karolyshyn, G. Semeghini, M. J. Gullans, M. Greiner, V. Vuletić, and M. D. Lukin, “Logical quantum processor based on reconfigurable atom arrays,” Nature
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
R. Acharya, L. Aghababaie-Beni, I. Aleiner, T. I. Andersen, M. Ansmann, F. Arute, K. Arya, A. Asfaw, N. Astrakhantsev, J. Atalaya, et al
2024
Later among the works it cites.
S. Bravyi, A. W. Cross, J. M. Gambetta, D. Maslov, P. Rall, and T. J. Yoder, “High-threshold and low-overhead fault-tolerant quantum memory,” Nature
2024
Later among the works it cites.
Q. Xu, J. P. Bonilla Ataides, C. A. Pattison, N. Raveendran, D. Bluvstein, J. Wurtz, B. Vasić, M. D. Lukin, L. Jiang, and H. Zhou, “Constant-overhead fault-tolerant quantum computation with reconfigurable atom arrays,” Nature Physics
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
F. Fürrutter, G. Muñoz-Gil, and H. J. Briegel, “Quantum circuit synthesis with diffusion models,” Nature Machine Intelligence
2024
Later among the works it cites.
S. Daimon and Y. Matsushita, “Quantum circuit generation for amplitude encoding using a transformer decoder,” Phys. Rev. Appl
2024
Later among the works it cites.
F. Preti, M. Schilling, S. Jerbi, L. M. Trenkwalder, H. P. Nautrup, F. Motzoi, and H. J. Briegel, “Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning,” Quantum
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
H. Xu, A. Sharaf, Y. Chen, W. Tan, L. Shen, B. V. Durme, K. Murray, and Y. J. Kim, “Contrastive preference optimization: Pushing the boundaries of LLM performance in machine translation,” in ICML
2024
Later among the works it cites.