Fetching the paper…
Reading the bibliography…
Solving the ground state and the ground-state properties of quantum many-body systems is generically a hard task for classical algorithms.
P. Jordan and E. Wigner, “Über das Paulische Äquivalenzverbot,” Z. Phys. 47
1928
Earlier work this paper cites.
E. Barouch and B. M. McCoy, “Statistical Mechanics of the X Y XY Model. II. Spin-Correlation Functions,” Phys. Rev. A 3
1971
Earlier work this paper cites.
C. W. Helstrom, Quantum Detection and Estimation Theory (Academic Press, New York, 1976)
1976
Earlier work this paper cites.
A. S. Holevo, Probabilistic and Statistical Aspects of Quantum Theory (North-Holland Publishing Company, Amsterdam, 1982)
1982
Earlier work this paper cites.
L. G. Valiant, “A theory of the learnable,” Communications of the ACM 27
1984
Earlier work this paper cites.
S. B. Bravyi and A. Y. Kitaev, “Fermionic Quantum Computation,” Ann. Phys. 298
2002
Earlier work this paper cites.
E. M. Stein and R. Shakarchi, Fourier Analysis: An Introduction (Princeton University Press, 2003)
2003
Earlier work this paper cites.
R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. W. Zucker, “Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps,” Proc. Natl. Acad. Sci. U.S.A. 102
2005
Earlier work this paper cites.
B. Nadler, S. Lafon, I. Kevrekidis, and R. R. Coifman, “Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators,” in Advances in neural information processing systems (2006) pp. 955–962
2006
Earlier work this paper cites.
I. Steinwart and A. Christmann, Support Vector Machines (Springer Publishing Company, Incorporated, 2008)
2008
Earlier work this paper cites.
S. Arora and B. Barak, Computational Complexity: A Modern Approach (Cambridge University Press, 2009)
2009
Earlier work this paper cites.
A. De Pasquale and P. Facchi, “ X Y XY model on the circle: Diagonalization, spectrum, and forerunners of the quantum phase transition,” Phys. Rev. A 80
2009
Earlier work this paper cites.
D. Gross, Y.-K. Liu, S. T. Flammia, S. Becker, and J. Eisert, “Quantum State Tomography via Compressed Sensing,” Phys. Rev. Lett. 105
2010
Earlier work this paper cites.
S. Sachdev, Quantum Phase Transitions (Cambridge University Press, 2011)
2011
Earlier work this paper cites.
J. Ma, X. Wang, C.P. Sun, and F. Nori, “Quantum spin squeezing,” Phys. Rep. 509
2011
Earlier work this paper cites.
S. Bachmann, S. Michalakis, B. Nachtergaele, and R. Sims, “Automorphic Equivalence within Gapped Phases of Quantum Lattice Systems,” Commun. Math. Phys. 309
2012
Earlier work this paper cites.
S. T. Flammia, D. Gross, Y.-K. Liu, and J. Eisert, “Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators,” New Journal of Physics 14
2012
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, England, 2012)
2012
Earlier work this paper cites.
I. M. Georgescu, S. Ashhab, and F. Nori, “Quantum simulation,” Rev. Mod. Phys. 86
2014
Earlier work this paper cites.
M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemp. Phys. 56
2014
Earlier work this paper cites.
M. Okuyama, Y. Yamanaka, H. Nishimori, and M. M. Rams, “Anomalous behavior of the energy gap in the one-dimensional quantum X Y XY model,” Phys. Rev. E 92
2015
Earlier work this paper cites.
L. Wang, “Discovering phase transitions with unsupervised learning,” Phys. Rev. B 94
2016
Earlier work this paper cites.
T. Ohtsuki and T. Ohtsuki, “Deep learning the quantum phase transitions in random two-dimensional electron systems,” J. Phys. Soc. Jpn. 85
2016
Earlier work this paper cites.
H. Q. Minh, L. Bazzani, and V. Murino, “A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning,” Journal of Machine Learning Research 17
2016
Earlier work this paper cites.
G. Carleo and M. Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science 355
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.
X. Gao and L.-M. Duan, “Efficient representation of quantum many-body states with deep neural networks,” Nat. Commun. 8
2017
Earlier work this paper cites.
D.-L. Deng, X. Li, and S. Das Sarma, “Machine learning topological states,” Phys. Rev. B 96
2017
Earlier work this paper cites.
S. J. Wetzel, “Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders,” Phys. Rev. E 96
2017
Earlier work this paper cites.
P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, “Machine learning quantum phases of matter beyond the fermion sign problem,” Sci. Rep. 7
2017
Earlier work this paper cites.
W. Hu, R. R. P. Singh, and R. T. Scalettar, “Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination,” Phys. Rev. E 95
2017
Earlier work this paper cites.
P. Ponte and R. G. Melko, “Kernel methods for interpretable machine learning of order parameters,” Phys. Rev. B 96
2017
Earlier work this paper cites.
J. Carrasquilla and R. G. Melko, “Machine learning phases of matter,” Nat. Phys. 13
2017
Earlier work this paper cites.
E. P. L. Van Nieuwenburg, Y.-H. Liu, and S. D. Huber, “Learning phase transitions by confusion,” Nat. Phys. 13
2017
Earlier work this paper cites.
Y. Zhang and E.-A. Kim, “Quantum Loop Topography for Machine Learning,” Phys. Rev. Lett. 118
2017
Earlier work this paper cites.
K. Ch’ng, J. Carrasquilla, R. G. Melko, and E. Khatami, “Machine learning phases of strongly correlated fermions,” Phys. Rev. X 7
2017
Earlier work this paper cites.
R. Chatterjee and T. Yu, “Generalized coherent states, reproducing kernels, and quantum support vector machines,” Quantum Inf. Commun. 17
2017
Earlier work this paper cites.
T. Albash and D. A. Lidar, “Adiabatic quantum computation,” Rev. Mod. Phys. 90
2018
Earlier work this paper cites.
V. Dunjko and H. J. Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Rep. Prog. Phys. 81
2018
Earlier work this paper cites.
J. Chen, S. Cheng, H. Xie, L. Wang, and T. Xiang, “Equivalence of restricted Boltzmann machines and tensor network states,” Phys. Rev. B 97
2018
Earlier work this paper cites.
G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R. Melko, and G. Carleo, “Neural-network quantum state tomography,” Nat. Phys. 14
2018
Earlier work this paper cites.
K. Choo, G. Carleo, N. Regnault, and T. Neupert, “Symmetries and Many-Body Excitations with Neural-Network Quantum States,” Phys. Rev. Lett. 121
2018
Cited alongside, same era.
J. Gao, L.-F. Qiao, Z.-Q. Jiao, Y.-C. Ma, C.-Q. Hu, R.-J. Ren, A.-L. Yang, H. Tang, M.-H. Yung, and X.-M. Jin, “Experimental Machine Learning of Quantum States,” Phys. Rev. Lett. 120
2018
Cited alongside, same era.
K. Ch’ng, N. Vazquez, and E. Khatami, “Unsupervised machine learning account of magnetic transitions in the Hubbard model,” Phys. Rev. E 97
2018
Cited alongside, same era.
M. J. S. Beach, A. Golubeva, and R. G. Melko, “Machine learning vortices at the Kosterlitz-Thouless transition,” Phys. Rev. B 97
2018
Cited alongside, same era.
W.-J. Rao, Z. Li, Q. Zhu, M. Luo, and X. Wan, “Identifying product order with restricted Boltzmann machines,” Phys. Rev. B 97
2018
O. Sharir, Y. Levine, N. Wies, G. Carleo, and A. Shashua, “Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems,” Phys. Rev. Lett. 124
2020
Later among the works it cites.
A. Glielmo, Y. Rath, G. Csányi, A. De Vita, and G. H. Booth, “Gaussian Process States: A Data-Driven Representation of Quantum Many-Body Physics,” Phys. Rev. X 10
2020
Later among the works it cites.
A. Borin and D. A. Abanin, “Approximating power of machine-learning ansatz for quantum many-body states,” Phys. Rev. B 101
2020
Later among the works it cites.
C.-Y. Park and M. J. Kastoryano, “Geometry of learning neural quantum states,” Phys. Rev. Res. 2
2020
Later among the works it cites.
T. Ohtsuki and T. Mano, “Drawing phase diagrams of random quantum systems by deep learning the wave functions,” J. Phys. Soc. Jpn. 89
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Y.-H. Liu and E. P. L. van Nieuwenburg, “Discriminative Cooperative Networks for Detecting Phase Transitions,” Phys. Rev. Lett. 120
2018
Cited alongside, same era.
P. Zhang, H. Shen, and H. Zhai, “Machine Learning Topological Invariants with Neural Networks,” Phys. Rev. Lett. 120
2018
Cited alongside, same era.
N. Sun, J. Yi, P. Zhang, H. Shen, and H. Zhai, “Deep learning topological invariants of band insulators,” Phys. Rev. B 98
2018
Cited alongside, same era.
P. Huembeli, A. Dauphin, and P. Wittek, “Identifying quantum phase transitions with adversarial neural networks,” Phys. Rev. B 97
2018
Cited alongside, same era.
N. Yoshioka, Y. Akagi, and H. Katsura, “Learning disordered topological phases by statistical recovery of symmetry,” Phys. Rev. B 97
2018
Cited alongside, same era.
L. Pilozzi, F. A. Farrelly, G. Marcucci, and C. Conti, “Machine learning inverse problem for topological photonics,” Commun. Phys. 1
2018
Cited alongside, same era.
J. Venderley, V. Khemani, and E.-A. Kim, “Machine Learning Out-of-Equilibrium Phases of Matter,” Phys. Rev. Lett. 120
2018
Cited alongside, same era.
Later among the works it cites.
R. Zen, L. My, R. Tan, F. Hébert, Ma. Gattobigio, C. Miniatura, D. Poletti, and S. Bressan, “Transfer learning for scalability of neural-network quantum states,” Phys. Rev. E 101
2020
Later among the works it cites.
A. Barr, W. Gispen, and A. Lamacraft, “Quantum Ground States from Reinforcement Learning,” PMLR 107
2020
Later among the works it cites.
A. Melkani, C. Gneiting, and F. Nori, “Eigenstate extraction with neural-network tomography,” Phys. Rev. A 102
2020
Later among the works it cites.
A. M. Palmieri, E. Kovlakov, F. Bianchi, D. Yudin, S. Straupe, J. D. Biamonte, and S. Kulik, “Experimental neural network enhanced quantum tomography,” npj Quantum Information 6
2020
Later among the works it cites.
S. Lohani, B. T. Kirby, M. Brodsky, O. Danaci, and R. T. Glasser, “Machine learning assisted quantum state estimation,” Mach. Learn.: Sci. Technol. 1
2020
Later among the works it cites.
M. Neugebauer, L. Fischer, A. Jäger, S. Czischek, S. Jochim, M. Weidemüller, and M. Gärttner, “Neural-network quantum state tomography in a two-qubit experiment,” Phys. Rev. A 102
2020
Later among the works it cites.
Y. Che, C. Gneiting, T. Liu, and F. Nori, “Topological quantum phase transitions retrieved through unsupervised machine learning,” Phys. Rev. B 102
2020
Later among the works it cites.
M. S. Scheurer and R.-J. Slager, “Unsupervised Machine Learning and Band Topology,” Phys. Rev. Lett. 124
2020
Later among the works it cites.
Y. Long, J. Ren, and H. Chen, “Unsupervised Manifold Clustering of Topological Phononics,” Phys. Rev. Lett. 124
2020
Later among the works it cites.
A. Lidiak and Z. Gong, “Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps,” Phys. Rev. Lett. 125
2020
Later among the works it cites.
O. Balabanov and M. Granath, “Unsupervised learning using topological data augmentation,” Phys. Rev. Research 2
2020
Later among the works it cites.
E. Greplova, A. Valenti, G. Boschung, F. Schäfer, N. Lörch, and S. D. Huber, “Unsupervised identification of topological phase transitions using predictive models,” New Journal of Physics 22
2020
Later among the works it cites.
A. Berezutskii, M. Beketov, D. Yudin, Z. Zimborás, and J. D. Biamonte, “Probing criticality in quantum spin chains with neural networks,” Journal of Physics: Complexity 1
2020
Later among the works it cites.
H.-Y. Huang, R. Kueng, and J. Preskill, “Predicting many properties of a quantum system from very few measurements,” Nat. Phys. 16
2020
Later among the works it cites.
K. Bartkiewicz, C. Gneiting, A. Černoch, K. Jiráková, K. Lemr, and F. Nori, “Experimental kernel-based quantum machine learning in finite feature space,” Sci. Rep. 10
2020
Later among the works it cites.
J. Liu, H. Yuan, X.-M. Lu, and X. Wang, “Quantum Fisher information matrix and multiparameter estimation,” J. Phys. A: Math. Theor. 53
2020
Later among the works it cites.
N. Yoshioka, W. Mizukami, and F. Nori, “Solving quasiparticle band spectra of real solids using neural-network quantum states,” Commun. Phys. 4
2021
Later among the works it cites.
J. Yao, L. Lin, and M. Bukov, “Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving,” Phys. Rev. X 11
2021
Later among the works it cites.
Y. Nomura, N. Yoshioka, and F. Nori, “Purifying Deep Boltzmann Machines for Thermal Quantum States,” Phys. Rev. Lett. 127
2021
Later among the works it cites.
Q. H. Tran and K. Nakajima, “Learning Temporal Quantum Tomography,” Phys. Rev. Lett. 127
2021
Later among the works it cites.
L.-W. Yu and D.-L. Deng, “Unsupervised Learning of Non-Hermitian Topological Phases,” Phys. Rev. Lett. 126
2021
Later among the works it cites.
H.-Y. Huang, R. Kueng, and J. Preskill, “Efficient Estimation of Pauli Observables by Derandomization,” Phys. Rev. Lett. 127
2021
Later among the works it cites.
M. Schuld, “Supervised quantum machine learning models are kernel methods,” arxiv:2101.11020 (2021)
2021
Later among the works it cites.
H.-Y. Huang, R. Kueng, G. Torlai, V. V. Albert, and J. Preskill, “Provably efficient machine learning for quantum many-body problems,” Science 377
2022
Later among the works it cites.
E. Rinaldi, X. Han, M. Hassan, Y. Feng, F. Nori, M. McGuigan, and M. Hanada, “Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo,” PRX Quantum 3
2022
Later among the works it cites.
P.-L. Zheng, S.-J. Du, and Y. Zhang, “Ground-state properties via machine learning quantum constraints,” Phys. Rev. Res. 4
2022
Later among the works it cites.
Y. Che, C. Gneiting, and F. Nori, “Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths,” Phys. Rev. B 105
2022
Later among the works it cites.
Y. Zeng, Z.-Y. Zhou, E. Rinaldi, C. Gneiting, and F. Nori, “Approximate Autonomous Quantum Error Correction with Reinforcement Learning,” Phys. Rev. Lett. 131
2023
Closest in time.
2023
Closest in time.
Y. Long and B. Zhang, “Unsupervised Data-Driven Classification of Topological Gapped Systems with Symmetries,” Phys. Rev. Lett. 130
2023
Closest in time.
2023
Closest in time.
E. Smolina, L. Smirnov, D. Leykam, F. Nori, and D. Smirnova, “Identifying topology of leaky photonic lattices with machine learning,” Nanophotonics 13
2024
Closest in time.
Z.-C. Shi, J.-T. Ding, Y.-H. Chen, J. Song, Y. Xia, X.X. Yi, and F. Nori, “Supervised learning for robust quantum control in composite-pulse systems,” Phys. Rev. Appl. 21
2024
Closest in time.
E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz, “Parameter estimation from quantum-jump data using neural networks,” Quantum Science and Technology 9
2024
Closest in time.
L. Lewis, H.-Y. Huang, V. T. Tran, S. Lehner, R. Kueng, and J. Preskill, “Improved machine learning algorithm for predicting ground state properties,” Nat. Commun. 15
2024
Closest in time.