Fetching the paper…
Reading the bibliography…
Recent breakthroughs in generative machine learning, powered by massive computational resources, have demonstrated unprecedented human-like capabilities.
C. H. Bennett, “Logical reversibility of computation,” IBM Journal of Research and Development 17
1973
Earlier work this paper cites.
D. E. Knuth and A. C.-C. Yao, “The complexity of nonuniform random number generation,” (1976)
1976
Earlier work this paper cites.
T. Toffoli, “Reversible computing,” in International colloquium on automata, languages, and programming (Springer, 1980) pp. 632–644
1980
Earlier work this paper cites.
S. Toda, “Pp is as hard as the polynomial-time hierarchy,” SIAM Journal on Computing 20
1991
Earlier work this paper cites.
E. Bernstein and U. Vazirani, “Quantum complexity theory,” in Proceedings of the twenty-fifth annual ACM symposium on Theory of computing (1993) pp. 11–20
1993
Earlier work this paper cites.
N. H. Bshouty and J. C. Jackson, “Learning dnf over the uniform distribution using a quantum example oracle,” in Proceedings of the eighth annual conference on Computational learning theory (1995) pp. 118–127
1995
Earlier work this paper cites.
Y. Han, L. A. Hemaspaandra, and T. Thierauf, “Threshold computation and cryptographic security,” SIAM Journal on Computing 26
1997
Earlier work this paper cites.
J. Bochnak, M. Coste, and M. Roy, Real Algebraic Geometry , Ergebnisse der Mathematik und ihrer Grenzgebiete (3), Vol. 36 (Springer, Berlin, Heidelberg, 1998)
1998
Earlier work this paper cites.
R. A. Servedio and S. J. Gortler, “Equivalences and separations between quantum and classical learnability,” SIAM J. Comput. 33
2004
Earlier work this paper cites.
A. Atici and R. A. Servedio, “Improved bounds on quantum learning algorithms,” Quantum Information Processing 4
2005
Earlier work this paper cites.
J. Emerson, R. Alicki, and K. Życzkowski, “Scalable noise estimation with random unitary operators,” Journal of Optics B: Quantum and Semiclassical Optics 7
2005
Earlier work this paper cites.
S. Aaronson, “Quantum computing, postselection, and probabilistic polynomial-time,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 461
2005
Earlier work this paper cites.
A. Atıcı and R. A. Servedio, “Quantum algorithms for learning and testing juntas,” Quantum Information Processing 6
2007
Earlier work this paper cites.
H. J. Briegel, D. E. Browne, W. Dür, R. Raussendorf, and M. Van den Nest, “Measurement-based quantum computation,” Nature Physics 5
2009
Earlier work this paper cites.
C. Zhang, “An improved lower bound on query complexity for quantum pac learning,” Information Processing Letters 111
2010
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum computation and quantum information (Cambridge university press, 2010)
2010
Earlier work this paper cites.
S. Aaronson, “Read the fine print,” Nat. Phys. 11
2015
Earlier work this paper cites.
A. W. Cross, G. Smith, and J. A. Smolin, “Quantum learning robust against noise,” Physical Review A 92
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT press, 2016)
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems 30
2017
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.
S. Arunachalam and R. de Wolf, “Guest column: A survey of quantum learning theory,” ACM SIGACT News 48
2017
Earlier work this paper cites.
A. Brutzkus and A. Globerson, “Globally optimal gradient descent for a convnet with gaussian inputs,” in International conference on machine learning (PMLR, 2017) pp. 605–614
2017
Earlier work this paper cites.
X. Gao, S.-T. Wang, and L.-M. Duan, “Quantum supremacy for simulating a translation-invariant ising spin model,” Phys. Rev. Lett. 118
2017
Earlier work this paper cites.
G. H. Low and I. L. Chuang, “Optimal hamiltonian simulation by quantum signal processing,” Phys. Rev. Lett. 118
2017
Earlier work this paper cites.
S. Arunachalam and R. De Wolf, “Optimal quantum sample complexity of learning algorithms,” Journal of Machine Learning Research 19
2018
Earlier work this paper cites.
I. Safran and O. Shamir, “Spurious local minima are common in two-layer ReLU neural networks,” in International Conference on Machine Learning (PMLR, 2018) pp. 4433–4441
2018
Cited alongside, same era.
S. Boixo, S. V. Isakov, V. N. Smelyanskiy, R. Babbush, N. Ding, Z. Jiang, M. J. Bremner, J. M. Martinis, and H. Neven, “Characterizing quantum supremacy in near-term devices,” Nature Physics 14
2018
Cited alongside, same era.
A. Perdomo-Ortiz, M. Benedetti, J. Realpe-Gómez, and R. Biswas, “Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers,” Quantum Science and Technology 3
2018
Cited alongside, same era.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,” Nature communications 9
2018
Cited alongside, same era.
X. Gao, E. R. Anschuetz, S.-T. Wang, J. I. Cirac, and M. D. Lukin, “Enhancing generative models via quantum correlations,” Physical Review X 12
2022
Later among the works it cites.
E. Y. Zhu, S. Johri, D. Bacon, M. Esencan, J. Kim, M. Muir, N. Murgai, J. Nguyen, N. Pisenti, A. Schouela, et al. , “Generative quantum learning of joint probability distribution functions,” Physical Review Research 4
2022
Later among the works it cites.
M. Y. Niu, A. Zlokapa, M. Broughton, S. Boixo, M. Mohseni, V. Smelyanskyi, and H. Neven, “Entangling quantum generative adversarial networks,” Physical Review Letters 128
2022
Later among the works it cites.
E. R. Anschuetz and B. T. Kiani, “Quantum variational algorithms are swamped with traps,” Nature Communications 13
2022
Later among the works it cites.
M. C. Caro, H.-Y. Huang, M. Cerezo, K. Sharma, A. Sornborger, L. Cincio, and P. J. Coles, “Generalization in quantum machine learning from few training data,” Nature communications 13
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Neill, P. Roushan, K. Kechedzhi, S. Boixo, S. V. Isakov, V. Smelyanskiy, A. Megrant, B. Chiaro, A. Dunsworth, K. Arya, et al. , “A blueprint for demonstrating quantum supremacy with superconducting qubits,” Science 360
2018
Cited alongside, same era.
S. Bravyi, D. Gosset, and R. Koenig, “Quantum advantage with shallow circuits,” Science 362
2018
Cited alongside, same era.
J. Bermejo-Vega, D. Hangleiter, M. Schwarz, R. Raussendorf, and J. Eisert, “Architectures for quantum simulation showing a quantum speedup,” Physical Review X 8
2018
Cited alongside, same era.
2018
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. , “Quantum supremacy using a programmable superconducting processor,” Nature 574
2019
Cited alongside, same era.
M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz, “A generative modeling approach for benchmarking and training shallow quantum circuits,” npj Quantum Information 5
2019
Cited alongside, same era.
A. B. Watts, R. Kothari, L. Schaeffer, and A. Tal, “Exponential separation between shallow quantum circuits and unbounded fan-in shallow classical circuits,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing , STOC 2019 (Association for Computing Machinery, New York, NY, USA, 2019) p. 515–526
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al. , “Language models are unsupervised multitask learners,” OpenAI blog 1
2019
Cited alongside, same era.
2022
Later among the works it cites.
A. Abbas, R. King, H.-Y. Huang, W. J. Huggins, R. Movassagh, D. Gilboa, and J. McClean, “On quantum backpropagation, information reuse, and cheating measurement collapse,” Advances in Neural Information Processing Systems 36
2023
Later among the works it cites.
S. Jaques and A. G. Rattew, “Qram: A survey and critique,” arXiv preprint arXiv:2305.10310 (2023)
2023
Later among the works it cites.
R. Movassagh, “The hardness of random quantum circuits,” Nature Physics 19
2023
Later among the works it cites.
A. Elben, S. T. Flammia, H.-Y. Huang, R. Kueng, J. Preskill, B. Vermersch, and P. Zoller, “The randomized measurement toolbox,” Nature Reviews Physics 5
2023
Later among the works it cites.
M. C. Caro, H.-Y. Huang, N. Ezzell, J. Gibbs, A. T. Sornborger, L. Cincio, P. J. Coles, and Z. Holmes, “Out-of-distribution generalization for learning quantum dynamics,” Nature Communications 14
2023
Later among the works it cites.
B. T. Kiani, J. Wang, and M. Weber, “Hardness of learning neural networks under the manifold hypothesis,” arXiv [cs.LG] (2024)
2024
Later among the works it cites.
A. Morvan, B. Villalonga, X. Mi, S. Mandra, A. Bengtsson, P. Klimov, Z. Chen, S. Hong, C. Erickson, I. Drozdov, et al. , “Phase transitions in random circuit sampling,” Nature 634
2024
Later among the works it cites.
C. A. Riofrio, O. Mitevski, C. Jones, F. Krellner, A. Vuckovic, J. Doetsch, J. Klepsch, T. Ehmer, and A. Luckow, “A characterization of quantum generative models,” ACM Transactions on Quantum Computing 5
2024
Later among the works it cites.
M. S. Rudolph, S. Lerch, S. Thanasilp, O. Kiss, O. Shaya, S. Vallecorsa, M. Grossi, and Z. Holmes, “Trainability barriers and opportunities in quantum generative modeling,” npj Quantum Information 10
2024
Later among the works it cites.
M. Hibat-Allah, M. Mauri, J. Carrasquilla, and A. Perdomo-Ortiz, “A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models,” Communications Physics 7
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.
T. Bergamaschi, C.-F. Chen, and Y. Liu, “Quantum computational advantage with constant-temperature gibbs sampling,” in 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS) (IEEE, 2024) pp. 1063–1085
2024
Later among the works it cites.
2024
Later among the works it cites.
2025
Closest in time.
D. Gao, D. Fan, C. Zha, J. Bei, G. Cai, J. Cai, S. Cao, F. Chen, J. Chen, K. Chen, et al. , “Establishing a new benchmark in quantum computational advantage with 105-qubit zuchongzhi 3.0 processor,” Physical Review Letters 134
2025
Closest in time.
M. Larocca, S. Thanasilp, S. Wang, K. Sharma, J. Biamonte, P. J. Coles, L. Cincio, J. R. McClean, Z. Holmes, and M. Cerezo, “Barren plateaus in variational quantum computing,” Nature Reviews Physics , 1 (2025)
2025
Closest in time.
J. Haah, “Short remarks on shallow unitary circuits,” arXiv preprint arXiv:2504.14005 (2025)
2025
Closest in time.
2025
Closest in time.
C. Developers, Cirq (Zenodo, 2025)
2025
Closest in time.