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Parameterized quantum circuits (PQCs) have been widely used as a machine learning model to explore the potential of achieving quantum advantages for various tasks.
Y.-Y. Shi, L.-M. Duan, and G. Vidal, Classical simulation of quantum many-body systems with a tree tensor network, Phys. Rev. A 74
2006
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Lucas, Ising formulations of many NP problems, Frontiers in Physics 2
2014
Earlier work this paper cites.
2014
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 J. Phys. 18
2016
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: a system for large-scale machine learning, in Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation , OSDI’16 (USENIX Association, USA, 2016) p. 265–283
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.
2017
Earlier work this paper cites.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, Quantum circuit learning, Phys. Rev. A 98
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Z. Jiang, K. J. Sung, K. Kechedzhi, V. N. Smelyanskiy, and S. Boixo, Quantum algorithms to simulate many-body physics of correlated fermions, Phys. Rev. Appl. 9
2018
Earlier work this paper cites.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nat. Commun. 9
2018
Earlier work this paper cites.
E. Grant, M. Benedetti, S. Cao, A. Hallam, J. Lockhart, V. Stojevic, A. G. Green, and S. Severini, Hierarchical quantum classifiers, npj Quantum Inf. 4
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Sci. Technol. 4
2019
Earlier work this paper cites.
X. Yuan, S. Endo, Q. Zhao, Y. Li, and S. C. Benjamin, Theory of variational quantum simulation, Quantum 3
2019
Earlier work this paper cites.
W. W. Ho and T. H. Hsieh, Efficient variational simulation of non-trivial quantum states, SciPost Phys. 6
2019
Earlier work this paper cites.
I. Cong, S. Choi, and M. D. Lukin, Quantum convolutional neural networks, Nat. Phys. 15
2019
Earlier work this paper cites.
S. Hadfield, Z. Wang, B. O’gorman, E. G. Rieffel, D. Venturelli, and R. Biswas, From the quantum approximate optimization algorithm to a quantum alternating operator ansatz, Algorithms 12
2019
Earlier work this paper cites.
E. Grant, L. Wossnig, M. Ostaszewski, and M. Benedetti, An initialization strategy for addressing barren plateaus in parametrized quantum circuits, Quantum 3
2019
Earlier work this paper cites.
Y. Cao, J. Romero, J. P. Olson, M. Degroote, P. D. Johnson, M. Kieferová, I. D. Kivlichan, T. Menke, B. Peropadre, N. P. Sawaya, et al. , Quantum chemistry in the age of quantum computing, Chemical reviews 119
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, PyTorch: An imperative style, high-performance deep learning library, 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.
I. Loshchilov and F. Hutter, Decoupled weight decay regularization, in International Conference on Learning Representations (2019)
2019
Earlier work this paper cites.
R. Sweke, F. Wilde, J. Meyer, M. Schuld, P. K. Fährmann, B. Meynard-Piganeau, and J. Eisert, Stochastic gradient descent for hybrid quantum-classical optimization, Quantum 4
2020
Earlier work this paper cites.
W. Lavrijsen, A. Tudor, J. Müller, C. Iancu, and W. De Jong, Classical optimizers for noisy intermediate-scale quantum devices, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (2020) pp. 267–277
2020
Cited alongside, same era.
R.-B. Wu, X. Cao, P. Xie, and Y.-x. Liu, End-to-end quantum machine learning implemented with controlled quantum dynamics, Phys. Rev. Appl. 14
2020
Cited alongside, same era.
E. Perrier, D. Tao, and C. Ferrie, Quantum geometric machine learning for quantum circuits and control, New J. Phys. 22
2020
Cited alongside, same era.
P. Vikstål, M. Grönkvist, M. Svensson, M. Andersson, G. Johansson, and G. Ferrini, Applying the quantum approximate optimization algorithm to the tail-assignment problem, Phys. Rev. Appl. 14
2020
Cited alongside, same era.
D. Wierichs, C. Gogolin, and M. Kastoryano, Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer, Phys. Rev. Res. 2
2022
Later among the works it cites.
C. Cao, C. Zhang, Z. Wu, M. Grassl, and B. Zeng, Quantum variational learning for quantum error-correcting codes, Quantum 6
2022
Later among the works it cites.
Z. Liang, H. Wang, J. Cheng, Y. Ding, H. Ren, Z. Gao, Z. Hu, D. S. Boning, X. Qian, S. Han, W. Jiang, and Y. Shi, Variational quantum pulse learning, in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (2022) pp. 556–565
2022
Later among the works it cites.
Z. Liu, L.-W. Yu, L.-M. Duan, and D.-L. Deng, Presence and absence of barren plateaus in tensor-network based machine learning, Phys. Rev. Lett. 129
2022
Later among the works it cites.
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
R. Wiersema, C. Zhou, Y. de Sereville, J. F. Carrasquilla, Y. B. Kim, and H. Yuen, Exploring entanglement and optimization within the Hamiltonian variational ansatz, PRX Quantum 1
2020
Cited alongside, same era.
L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. D. Lukin, Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices, Phys. Rev. X 10
2020
Cited alongside, same era.
S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, and X. Yuan, Quantum computational chemistry, Rev. Mod. Phys. 92
2020
Cited alongside, same era.
Y. Liu, S. Arunachalam, and K. Temme, A rigorous and robust quantum speed-up in supervised machine learning, Nat. Phys. 17
2021
Cited alongside, same era.
X. Xu, S. C. Benjamin, and X. Yuan, Variational circuit compiler for quantum error correction, Phys. Rev. Appl. 15
2021
Cited alongside, same era.
E. Altman, K. R. Brown, G. Carleo, L. D. Carr, E. Demler, C. Chin, B. DeMarco, S. E. Economou, M. A. Eriksson, K.-M. C. Fu, M. Greiner, K. R. A. Hazzard, R. G. Hulet, A. J. Kollár, B. L. Lev, M. D. Lukin, R. Ma, X. Mi, S. Misra, C. Monroe, K. Murch, Z. Nazario, K.-K. Ni, A. C. Potter, P. Roushan, M. Saffman, M. Schleier-Smith, I. Siddiqi, R. Simmonds, M. Singh, I. B. Spielman, K. Temme, D. S. Weiss, J. Vučković, V. Vuletić, J. Ye, and M. Zwierlein, Quantum simulators: Architectures and opportunities, PRX Quantum 2
2021
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
Later among the works it cites.
K. Sharma, M. Cerezo, L. Cincio, and P. J. Coles, Trainability of dissipative perceptron-based quantum neural networks, Phys. Rev. Lett. 128
2022
Later among the works it cites.
E. R. Anschuetz and B. T. Kiani, Quantum variational algorithms are swamped with traps, Nat. Commun. 13
2022
Later among the works it cites.
X. Ge, R.-B. Wu, and H. Rabitz, The optimization landscape of hybrid quantum–classical algorithms: From quantum control to NISQ applications, Annual Reviews in Control 54
2022
Later among the works it cites.
2022
Later among the works it cites.
E. R. Anschuetz, Critical points in quantum generative models, in International Conference on Learning Representations (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
K. Zhang, L. Liu, M.-H. Hsieh, and D. Tao, Escaping from the barren plateau via gaussian initializations in deep variational quantum circuits, in Advances in Neural Information Processing Systems , edited by A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho (2022)
2022
Later among the works it cites.
M. S. Alam, F. A. Wudarski, M. J. Reagor, J. Sud, S. Grabbe, Z. Wang, M. Hodson, P. A. Lott, E. G. Rieffel, and D. Venturelli, Practical verification of quantum properties in quantum-approximate-optimization runs, Phys. Rev. Appl. 17
2022
Later among the works it cites.
J. Wright and Y. Ma, High-dimensional data analysis with low-dimensional models: Principles, computation, and applications (Cambridge University Press, 2022)
2022
Later among the works it cites.
S. Jerbi, L. J. Fiderer, H. Poulsen Nautrup, J. M. Kübler, H. J. Briegel, and V. Dunjko, Quantum machine learning beyond kernel methods, Nat. Commun. 14
2023
Closest in time.
C. Tabares, A. Muñoz de las Heras, L. Tagliacozzo, D. Porras, and A. González-Tudela, Variational quantum simulators based on waveguide QED, Phys. Rev. Lett. 131
2023
Closest in time.
M. Svensson, M. Andersson, M. Grönkvist, P. Vikstål, D. Dubhashi, G. Ferrini, and G. Johansson, Hybrid quantum-classical heuristic to solve large-scale integer linear programs, Phys. Rev. Appl. 20
2023
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2023
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C. Lyu, X. Xu, M.-H. Yung, and A. Bayat, Symmetry enhanced variational quantum spin eigensolver, Quantum 7
2023
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J. Liu, K. Najafi, K. Sharma, F. Tacchino, L. Jiang, and A. Mezzacapo, Analytic theory for the dynamics of wide quantum neural networks, Phys. Rev. Lett. 130
2023
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M. Larocca, N. Ju, D. García-Martín, P. J. Coles, and M. Cerezo, Theory of overparametrization in quantum neural networks, Nature Computational Science 3
2023
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S.-X. Zhang, J. Allcock, Z.-Q. Wan, S. Liu, J. Sun, H. Yu, X.-H. Yang, J. Qiu, Z. Ye, Y.-Q. Chen, C.-K. Lee, Y.-C. Zheng, S.-K. Jian, H. Yao, C.-Y. Hsieh, and S. Zhang, TensorCircuit: a quantum software framework for the NISQ era, Quantum 7
2023
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2024
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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
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X. Wang, B. Qi, Y. Wang, and D. Dong, Entanglement-variational hardware-efficient ansatz for eigensolvers, Phys. Rev. Appl. 21
2024
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