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5 量子人工智能:第 8 章参考文献

[1] J. Cohen, P. Cohen, S. G. West, et al. Applied multiple regression/correlation analysis for the behavioral sciences, 3rd ed. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers, 2003. [M]

[2] Y. Saad. Iterative methods for sparse linear systems. SIAM, 2003. [M]

[3] U. Girish, R. Raz, W. Zhan. Quantum logspace algorithm for powering matrices with bounded norm 2020. [OL]

[4] A. W. Harrow, A. Hassidim, S. Lloyd. Quantum Algorithm for Linear Systems of Equations. Phys Rev Lett, 2009, 103(15): 150502. [J]

[5] X. Liu, H. Xie, Z. Liu, et al. Survey on the Improvement and Application of HHL Algorithm; proceedings of the Journal of Physics: Conference Series, F, 2022. IOP Publishing. [C]

[6] D. Dervovic, M. Herbster, P. Mountney, et al. Quantum linear systems algorithms: a primer 2018. [OL]

[7] J. R. Shewchuk. An introduction to the conjugate gradient method without the agonizing pain. Carnegie-Mellon University. Department of Computer Science Pittsburgh. 1994. [Z]


[8] A. Ambainis. Variable time amplitude amplification and a faster quantum algorithm for solving systems of linear equations 2010. [OL]

[9] A. M. Childs, R. Kothari, R. D. Somma. Quantum Algorithm for Systems of Linear Equations with Exponentially Improved Dependence on Precision. SIAM J Comput, 2015, 46: 1920-50. [J]

[10] L. Wossnig, Z. Zhao, A. Prakash. Quantum linear system algorithm for dense matrices. Physical review letters, 2018, 120(5): 050502. [J]

[11] P. Rebentrost, M. Mohseni, S. Lloyd. Quantum Support Vector Machine for Big Data Classification. Physical Review Letters, 2014, 113(13): 130503. [J]

[12] S. Lloyd, M. Mohseni, P. Rebentrost. Quantum algorithms for supervised and unsupervised machine learning 2013. [OL]

[13] G. Wang. Quantum algorithm for linear regression. Physical review A, 2017, 96(1): 012335. [J]

[14] N. Wiebe, D. Braun, S. Lloyd. Quantum Algorithm for Data Fitting. Physical Review Letters, 2012, 109(5): 050505. [J]

[15] T. Hofmann, B. Schölkopf, A. Smola. Kernel methods in machine learning. Annals of Statistics, 2007, 36: 1171-220. [J]

[16] K. R. Muller, S. Mika, G. Ratsch, et al. An introduction to kernel- based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181-201. [J]

[17] A. Ben-Israel, T. N. Greville. Generalized inverses: theory and applications. Springer Science & Business Media, 2003. [M]

[18] M. Schuld, I. Sinayskiy, F. Petruccione. Prediction by linear regression on a quantum computer. Physical Review A, 2016, 94(2): 022342. [J]


[19] C. Shao. Quantum speedup of Bayes’ classifiers. Journal of Physics A: Mathematical and Theoretical, 2020, 53(4): 045301. [J]

[20] G. H. Low, I. L. Chuang. Hamiltonian Simulation by Qubitization. Quantum-Austria, 2019, 3. [J]

[21] A. Gilyén, Y. Su, G. H. Low, et al. Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. Phoenix, AZ, USA; Association for Computing Machinery. 2019: 193– 204.10.1145/3313276.3316366. [Z]

[22] G. H. Low, I. L. Chuang. Optimal Hamiltonian Simulation by Quantum Signal Processing. Phys Rev Lett, 2017, 118(1): 010501. [J]

[23] M. Szegedy. Quantum speed-up of Markov chain based algorithms; proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science, F 17-19 Oct. 2004, 2004. [C]

[24] G. Brassard, P. Høyer, M. Mosca, et al. Quantum Amplitude Amplification and Estimation. AMS Contemporary Mathematics Series, 2002, 305: 53-74. [J]

[25] L. K. Grover. A fast quantum mechanical algorithm for database search. Proceedings of the twenty-eighth annual ACM symposium on Theory of Computing. Philadelphia, Pennsylvania, USA; Association for Computing Machinery. 1996: 212– 9.10.1145/237814.237866. [Z]

[26] S. Lloyd, M. Mohseni, P. Rebentrost. Quantum principal component analysis. Nature Physics, 2014, 10(9): 631-3. [J]

[27] N. Wiebe, R. S. S. Kumar. Hardening quantum machine learning


against adversaries. New Journal of Physics, 2018, 20(12): 123019. [J]

[28] A. M. Childs, R. Kothari, R. D. Somma. Quantum Algorithm for Systems of Linear Equations with Exponentially Improved Dependence on Precision. SIAM Journal on Computing, 2017, 46(6): 1920-50. [J]

[29] S. Chakraborty, A. Gilyén, S. Jeffery. The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation. CoRR, 2018, abs/1804.01973. [J]

[30] I. Kerenidis, A. Luongo. Classification of the MNIST data set with quantum slow feature analysis. Physical Review A, 2020, 101(6): 062327. [J]

[31] A. N. Chowdhury, R. D. Somma. Quantum algorithms for Gibbs sampling and hitting-time estimation. Quantum Info Comput, 2017, 17(1–2): 41–64. [J]

[32] J. V. Apeldoorn, A. Gilyén, S. Gribling, et al. Quantum SDP-Solvers: Better Upper and Lower Bounds; proceedings of the 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), F 15-17 Oct. 2017, 2017. [C]

[33] M. Kieferová, N. Wiebe. Tomography and generative training with quantum Boltzmann machines. Physical Review A, 2017, 96(6): 062327. [J]

[34] M. H. Amin, E. Andriyash, J. Rolfe, et al. Quantum Boltzmann Machine. Physical Review X, 2018, 8(2): 021050. [J]

[35] S. Kimmel, C. Y.-Y. Lin, G. H. Low, et al. Hamiltonian simulation with optimal sample complexity. npj Quantum Information, 2017, 3(1): 13. [J]


[36] D. A. Levin, Y. Peres. Markov chains and mixing times. American Mathematical Soc., 2017. [M]

[37] A. Anshu, S. Arunachalam, T. Kuwahara, et al. Sample-efficient learning of quantum many-body systems; proceedings of the 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), F 16-19 Nov. 2020, 2020. [C]

[38] E. Bairey, I. Arad, N. H. Lindner. Learning a Local Hamiltonian from Local Measurements. Physical Review Letters, 2019, 122(2): 020504. [J]

[39] J. Biamonte, P. Wittek, N. Pancotti, et al. Quantum machine learning. Nature, 2017, 549(7671): 195-202. [J]

[40] N.-H. Chia, A. P. Gilyén, T. Li, et al. Sampling-based Sublinear Low-rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning. J ACM, 2022, 69(5): Article 33. [J]

[41] D. Crawford, A. Levit, N. Ghadermarzy, et al. Reinforcement learning using quantum boltzmann machines. Quantum Info Comput, 2018, 18(1–2): 51–74. [J]

[42] G. Torlai, R. G. Melko. Machine-Learning Quantum States in the NISQ Era. Annual Review of Condensed Matter Physics, 2020, 11(1): 325-44. [J]

[43] F. G. S. L. Brandão, M. J. Kastoryano. Finite Correlation Length Implies Efficient Preparation of Quantum Thermal States. Communications in Mathematical Physics, 2019, 365(1): 1-16. [J]

[44] M. J. Kastoryano, F. G. S. L. Brandão. Quantum Gibbs Samplers: The Commuting Case. Communications in Mathematical Physics, 2016, 344(3): 915-57. [J]

[45] T. Kuwahara, Á. M. Alhambra, A. Anshu. Improved Thermal Area


Law and Quasilinear Time Algorithm for Quantum Gibbs States. Physical Review X, 2021, 11(1): 011047. [J]

[46] T. Kuwahara, K. Kato, F. G. S. L. Brandão. Clustering of Conditional Mutual Information for Quantum Gibbs States above a Threshold Temperature. Physical Review Letters, 2020, 124(22): 220601. [J]

[47] F. G. S. L. Brandao, K. M. Svore. Quantum Speed-Ups for Solving Semidefinite Programs. 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). IEEE Computer Society. 2017: 415-26.10.1109/focs.2017.45. [Z]

[48] F. A. Brando, Amir%ALi, Tongyang%ALin, Cedric%ASvore, Krysta%AWu, Xiaodi%BJournal Name: Leibniz international proceedings in informatics. Quantum SDP Solvers: Large Speed- Ups, Optimality, and Applications to Quantum Learning. Journal Name: Leibniz international proceedings in informatics, 2019: Medium: X. [J]

[49] J. Van Apeldoorn, A. Gilyén, S. Gribling, et al. Quantum SDP- solvers: Better upper and lower bounds. Quantum-Austria, 2020, 4:

230. [J]

[50] Y. Cao, J. Romero, J. P. Olson, et al. Quantum Chemistry in the Age of Quantum Computing. Chemical Reviews, 2019, 119(19): 10856- 915. [J]

[51] E. Bilgin, S. Boixo. Preparing Thermal States of Quantum Systems by Dimension Reduction. Physical Review Letters, 2010, 105(17): 170405. [J]

[52] D. Poulin, P. Wocjan. Sampling from the Thermal Quantum Gibbs State and Evaluating Partition Functions with a Quantum Computer.


Physical Review Letters, 2009, 103(22): 220502. [J]

[53] K. Temme, T. J. Osborne, K. G. Vollbrecht, et al. Quantum Metropolis sampling. Nature, 2011, 471(7336): 87-90. [J]

[54] K. Mitarai, M. Negoro, M. Kitagawa, et al. Quantum circuit learning. Physical Review A, 2018, 98(3): 032309. [J]

[55] V. Havlíček, A. D. Córcoles, K. Temme, et al. Supervised learning with quantum-enhanced feature spaces. Nature, 2019, 567(7747): 209-12. [J]

[56] M. Schuld, A. Bocharov, K. M. Svore, et al. Circuit-centric quantum classifiers. Physical Review A, 2020, 101(3): 032308. [J]

[57] J. S. Otterbach, R. Manenti, N. Alidoust, et al. Unsupervised machine learning on a hybrid quantum computer 2017. [OL]

[58] E. Farhi, J. Goldstone, S. Gutmann. A quantum approximate optimization algorithm 2014. [OL]

[59] J. Romero, J. P. Olson, A. Aspuru-Guzik. Quantum autoencoders for efficient compression of quantum data. Quantum Science and Technology, 2017, 2(4): 045001. [J]

[60] P.-L. Dallaire-Demers, N. Killoran. Quantum generative adversarial networks. Physical Review A, 2018, 98(1): 012324. [J]

[61] E. Farhi, H. Neven. Classification with quantum neural networks on near term processors 2018. [OL]

[62] K. Beer, D. Bondarenko, T. Farrelly, et al. Training deep quantum neural networks. Nature Communications, 2020, 11(1): 808. [J]

[63] I. Cong, S. Choi, M. D. Lukin. Quantum convolutional neural networks. Nature Physics, 2019, 15(12): 1273-8. [J]

[64] S. Y. C. Chen, C. H. H. Yang, J. Qi, et al. Variational Quantum Circuits for Deep Reinforcement Learning. IEEE Access, 2020, 8:


141007-24. [J]

[65] A. Kandala, A. Mezzacapo, K. Temme, et al. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 2017, 549(7671): 242-6. [J]

[66] A. Peruzzo, J. Mcclean, P. Shadbolt, et al. A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 2014, 5(1): 4213. [J]

[67] S. B. Bravyi, A. Y. Kitaev. Fermionic quantum computation. Annals of Physics, 2002, 298(1): 210-26. [J]

[68] J. Lee, W. J. Huggins, M. Head-Gordon, et al. Generalized Unitary Coupled Cluster Wave functions for Quantum Computation. Journal of Chemical Theory and Computation, 2019, 15(1): 311-24. [J]

[69] H. R. Grimsley, S. E. Economou, E. Barnes, et al. An adaptive variational algorithm for exact molecular simulations on a quantum computer. Nature Communications, 2019, 10(1): 3007. [J]

[70] M. Schuld, V. Bergholm, C. Gogolin, et al. Evaluating analytic gradients on quantum hardware. Physical Review A, 2019, 99(3): 032331. [J]

[71] G. E. Crooks. Performance of the quantum approximate optimization algorithm on the maximum cut problem 2018. [OL]

[72] S. Ebadi, A. Keesling, M. Cain, et al. Quantum optimization of maximum independent set using Rydberg atom arrays. Science, 2022, 376(6598): 1209-15. [J]

[73] Y. J. Zhang, X. D. Mu, X. W. Liu, et al. Applying the quantum approximate optimization algorithm to the minimum vertex cover problem. Applied Soft Computing, 2022, 118: 108554. [J]

[74] X. Qiang, X. Zhou, J. Wang, et al. Large-scale silicon quantum


photonics implementing arbitrary two-qubit processing. Nature Photonics, 2018, 12(9): 534-9. [J]

[75] G. Pagano, A. Bapat, P. Becker, et al. Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator. Proceedings of the National Academy of Sciences, 2020, 117(41): 25396-401. [J]

[76] A. Bengtsson, P. Vikstål, C. Warren, et al. Improved Success Probability with Greater Circuit Depth for the Quantum Approximate Optimization Algorithm. Physical Review Applied, 2020, 14(3): 034010. [J]

[77] M. P. Harrigan, K. J. Sung, M. Neeley, et al. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Nature Physics, 2021, 17(3): 332-6. [J]

[78] R. A. Servedio, S. J. Gortler. Equivalences and separations between quantum and classical learnability. SIAM Journal on Computing, 2004, 33(5): 1067-92. [J]

[79] N. H. Bshouty, R. Cleve, S. Kannan, et al. Oracles and queries that are sufficient for exact learning; proceedings of the Proceedings of the seventh annual conference on Computational learning theory, F, 1994. [C]

[80] R. Kothari. An optimal quantum algorithm for the oracle identification problem 2013. [OL]

[81] A. Atici, R. A. Servedio. Improved bounds on quantum learning algorithms. Quantum Information Processing, 2005, 4(5): 355-86. [J]

[82] M. Hunziker, D. A. Meyer, J. Park, et al. The geometry of quantum learning. Quantum Information Processing, 2010, 9: 321-41. [J]

[83] N. Alon, L. Rónyai, T. Szabó. Norm-Graphs: Variations and


Applications. Journal of Combinatorial Theory, Series B, 1999, 76(2): 280-90. [J]

[84] A. Ambainis, K. Iwama, A. Kawachi, et al. Improved algorithms for quantum identification of Boolean oracles. Theoretical computer science, 2007, 378(1): 41-53. [J]

[85] A. Ambainis, K. Iwama, M. Nakanishi, et al. Average/worst-case gap of quantum query complexities by on-set size 2009. [OL]

[86] N. H. Bshouty, J. C. Jackson. Learning DNF over the uniform distribution using a quantum example oracle; proceedings of the Proceedings of the eighth annual conference on Computational learning theory, F, 1995. [C]

[87] V. N. Vapnik, A. Y. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Measures of complexity: festschrift for alexey chervonenkis, 2015: 11-30. [J]

[88] A. Blumer, A. Ehrenfeucht, D. Haussler, et al. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM (JACM), 1989, 36(4): 929-65. [J]

[89] S. Hanneke. The optimal sample complexity of PAC learning. The Journal of Machine Learning Research, 2016, 17(1): 1319-33. [J]

[90] H. U. Simon. An almost optimal PAC algorithm; proceedings of the Conference on Learning Theory, F, 2015. PMLR. [C]

[91] S. Arunachalam, R. De Wolf. Optimal quantum sample complexity of learning algorithms. The Journal of Machine Learning Research, 2018, 19(1): 2879-8. [J]

[92] C. Zhang. An improved lower bound on query complexity for quantum PAC learning. Information Processing Letters, 2010, 111(1): 40-5. [J]


[93] H. U. Simon. General bounds on the number of examples needed for learning probabilistic concepts; proceedings of the Proceedings of the sixth annual conference on Computational learning theory, F, 1993. [C]

[94] M. Talagrand. Sharper bounds for Gaussian and empirical processes. The Annals of Probability, 1994: 28-76. [J]

[95] S. Shalev-Shwartz, S. Ben-David. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014. [M]

[96] M. A. Nielsen, I. L. Chuang. Quantum computation and quantum information. Cambridge university press, 2010. [M]

[97] I. Kerenidis, J. Landman. Quantum spectral clustering. Physical Review A, 2021, 103(4): 042415. [J]

[98] Q. Li, Y. Huang, S. Jin, et al. Quantum spectral clustering algorithm for unsupervised learning. Science China Information Sciences, 2022, 65(10): 200504. [J]

[99] D. Volya, P. Mishra. Quantum Spectral Clustering of Mixed Graphs; proceedings of the 2021 58th ACM/IEEE Design Automation Conference (DAC), F 5-9 Dec. 2021, 2021. [C]

[100] I. Kerenidis, J. Landman, A. Luongo, et al. q-means: a quantum algorithm for unsupervised machine learning. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc. 2019: Article 372. [M]

[101] E. Aïmeur, G. Brassard, S. Gambs. Quantum speed-up for unsupervised learning. Machine Learning, 2013, 90(2): 261-87. [J]

[102] G. Brassard, P. Hoyer, M. Mosca, et al. Quantum amplitude amplification and estimation. Contemporary Mathematics, 2002, 305: 53-74. [J]


[103] A. Ahuja, S. Kapoor. A quantum algorithm for finding the maximum 1999. [OL]

[104] Y. Xue, X. Chen, T. Li, et al. Near-Optimal Quantum Coreset Construction Algorithms for Clustering. arXiv preprint arXiv:230602826, 2023. [J]

[105] N. Wiebe, A. Kapoor, K. Svore. Quantum algorithms for nearest- neighbor methods for supervised and unsupervised learning 2014. [OL]

[106] G. Torlai, C. J. Wood, A. Acharya, et al. Quantum process tomography with unsupervised learning and tensor networks. Nature Communications, 2023, 14(1): 2858. [J]

[107] Y. Chen, Y. Pan, G. Zhang, et al. Detecting quantum entanglement with unsupervised learning. Quantum Science and Technology, 2022, 7(1): 015005. [J]

[108] A. Lidiak, Z. Gong. Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps. Physical Review Letters, 2020, 125(22): 225701. [J]

[109] K. Kottmann, F. Metz, J. Fraxanet, et al. Variational quantum anomaly detection: Unsupervised mapping of phase diagrams on a physical quantum computer. Physical Review Research, 2021, 3(4): 043184. [J]

[110] D. Bondarenko, P. Feldmann. Quantum Autoencoders to Denoise Quantum Data. Physical Review Letters, 2020, 124(13): 130502. [J]

[111] A. Barenco, A. Berthiaume, D. Deutsch, et al. Stabilization of Quantum Computations by Symmetrization. SIAM Journal on Computing, 1997, 26(5): 1541-57. [J]

[112] H. Buhrman, R. Cleve, J. Watrous, et al. Quantum Fingerprinting.


Physical Review Letters, 2001, 87(16): 167902. [J]

[113] D. Dong, C. Chen, H. Li, et al. Quantum Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008, 38(5): 1207-20. [J]

[114] D. Dong, C. Chen, J. Chu, et al. Robust Quantum-Inspired Reinforcement Learning for Robot Navigation. IEEE/ASME Transactions on Mechatronics, 2012, 17(1): 86-97. [J]

[115] Q. Wei, H. Ma, C. Chen, et al. Deep reinforcement learning with quantum-inspired experience replay. IEEE Transactions on Cybernetics, 2021, 52(9): 9326-38. [J]

[116] V. Dunjko, J. M. Taylor, H. J. Briegel. Quantum-enhanced machine learning. Physical review letters, 2016, 117(13): 130501. [J]

[117] V. Dunjko, J. M. Taylor, H. J. Briegel. Advances in quantum reinforcement learning; proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), F, 2017. IEEE. [C]

[118] Y. Kwak, W. J. Yun, S. Jung, et al. Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation, F 2021. IEEE. [C]

[119] O. Lockwood, M. Si. Reinforcement Learning with Quantum Variational Circuit. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020, 16(1): 245-51. [J]

[120] O. Lockwood, M. Si. Playing Atari with Hybrid Quantum-Classical Reinforcement Learning //LUCA B, JOÃO F H, SAMUEL A, et al. NeurIPS 2020 Workshop on Pre-registration in Machine Learning. Proceedings of Machine Learning Research; PMLR. 2021: 285--301.


[Z]

[121] S. Wu, S. Jin, D. Wen, et al. Quantum reinforcement learning in continuous action space 2021, https://arxiv.org/abs/2012.10711. [OL]

[122] A. Skolik, S. Jerbi, V. Dunjko. Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning. Quantum- Austria, 2022, 6: 720. [J]

[123] S. Y.-C. Chen, C.-M. Huang, C.-W. Hsing, et al. Variational quantum reinforcement learning via evolutionary optimization. Machine Learning: Science and Technology, 2022, 3(1): 015025. [J]

[124] S. Jerbi, C. Gyurik, S. Marshall, et al. Parametrized quantum policies for reinforcement learning. Advances in Neural Information Processing Systems, 2021, 34: 28362-75. [J]

[125] A. Sequeira, L. P. Santos, L. S. Barbosa. Policy gradients using variational quantum circuits. Quantum Machine Intelligence, 2023, 5(1): 18. [J]

[126] W. J. Yun, Y. Kwak, J. P. Kim, et al. Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design; proceedings of the 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS), F 10-13 July 2022, 2022. [C]

[127] S. Y. C. Chen. Quantum Deep Recurrent Reinforcement Learning; proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), F 4-10 June 2023, 2023. [C]

[128] T. Kimura, K. Shiba, C.-C. Chen, et al. Variational Quantum Circuit- Based Reinforcement Learning for POMDP and Experimental


Implementation. Mathematical Problems in Engineering, 2021, 2021: 3511029. [J]

[129] Q. Lan. Variational quantum soft actor-critic 2021. [OL]

[130] S. Y.-C. Chen. Asynchronous training of quantum reinforcement learning 2023. [OL]

[131] W. Hu, J. Hu. Q Learning with Quantum Neural Networks. Natural Science, 2019, 11(01): 31. [J]

[132] N. Killoran, T. R. Bromley, J. M. Arrazola, et al. Continuous- variable quantum neural networks. Physical Review Research, 2019, 1(3): 033063. [J]

[133] W. Hu, J. Hu. Distributional Reinforcement Learning with Quantum Neural Networks. Intelligent Control and Automation, 2019, 10(02):

63. [J]

[134] J.-Y. Hsiao, Y. Du, W.-Y. Chiang, et al. Unentangled quantum reinforcement learning agents in the OpenAI Gym 2022. [OL]

[135] D. Crawford, A. Levit, N. Ghadermarzy, et al. Reinforcement learning using quantum Boltzmann machines 2016. [OL]

[136] A. Levit, D. Crawford, N. Ghadermarzy, et al. Free energy-based reinforcement learning using a quantum processor 2017. [OL]

[137] S. Jerbi, L. M. Trenkwalder, H. P. Nautrup, et al. Quantum enhancements for deep reinforcement learning in large spaces. PRX Quantum, 2021, 2(1): 010328. [J]

[138] H. J. Briegel, G. De Las Cuevas. Projective simulation for artificial intelligence. Scientific reports, 2012, 2(1): 1-16. [J]

[139] G. D. Paparo, V. Dunjko, A. Makmal, et al. Quantum speedup for active learning agents. Physical Review X, 2014, 4(3): 031002. [J]

[140] V. Dunjko, N. Friis, H. J. Briegel. Quantum-enhanced deliberation


of learning agents using trapped ions. New Journal of Physics, 2015, 17(2): 023006. [J]

[141] M. Tiersch, E. J. Ganahl, H. J. Briegel. Adaptive quantum computation in changing environments using projective simulation. Scientific reports, 2015, 5(1): 1-18. [J]

[142] A. A. Melnikov, H. Poulsen Nautrup, M. Krenn, et al. Active learning machine learns to create new quantum experiments. Proceedings of the National Academy of Sciences, 2018, 115(6): 1221-6. [J]

[143] O. M. Pires, E. I. Duzzioni, J. Marchi, et al. Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era 2021. [OL]

[144] C. Kokail, R. Van Bijnen, A. Elben, et al. Entanglement Hamiltonian tomography in quantum simulation. Nature Physics, 2021, 17(8): 936-42. [J]

[145] M. Cramer, M. B. Plenio, S. T. Flammia, et al. Efficient quantum state tomography. Nature communications, 2010, 1(1): 149. [J]

[146] S. T. Flammia, D. Gross, Y.-K. Liu, et al. Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators. New Journal of Physics, 2012, 14(9): 095022. [J]

[147] R. Kueng, H. Rauhut, U. Terstiege. Low rank matrix recovery from rank one measurements. Applied and Computational Harmonic Analysis, 2017, 42(1): 88-116. [J]

[148] J. Haah, A. W. Harrow, Z. Ji, et al. Sample-optimal tomography of quantum states; proceedings of the Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, F, 2016. [C]

[149] R. O'donnell, J. Wright. Efficient quantum tomography; proceedings


of the Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, F, 2016. [C]

[150] H. Yuen. An improved sample complexity lower bound for quantum state tomography 2022. [OL]

[151] A. Lowe, A. Nayak. Lower bounds for learning quantum states with single-copy measurements 2022. [OL]

[152] S. Chen, B. Huang, J. Li, et al. Tight bounds for state tomography with incoherent measurements 2022. [OL]

[153] R. O'donnell, J. Wright. Quantum spectrum testing; proceedings of the Proceedings of the forty-seventh annual ACM symposium on Theory of computing, F, 2015. [C]

[154] J. Wright. How to learn a quantum state; Carnegie Mellon University, 2016. [D]

[155] R. O'donnell, J. Wright. Efficient quantum tomography II; proceedings of the Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, F, 2017. [C]

[156] K.-H. Han, J.-H. Kim. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. Evolutionary Computation, IEEE Transactions on, 2003, 6: 580-93. [J]

[157] X. F. He, L. Ma. Quantum-inspired ant colony algorithm for vehicle routing problem with time windows. Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2013, 33: 1255-

61. [J]

[158] N. Koide-Majima, K. Majima. Quantum-inspired canonical correlation analysis for exponentially large dimensional data. Neural Networks, 2021, 135: 55-67. [J]

[159] R. Giuntini, F. Holik, D. K. Park, et al. Quantum-inspired algorithm


for direct multi-class classification. Applied Soft Computing, 2023, 134: 109956. [J]

[160] G. Sergioli, E. Santucci, L. Didaci, et al. A quantum-inspired version of the nearest mean classifier. Soft Computing, 2018, 22(3): 691- 705. [J]

[161] E. Tang. A quantum-inspired classical algorithm for recommendation systems. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. Phoenix, AZ, USA; Association for Computing Machinery. 2019: 217– 28.10.1145/3313276.3316310. [Z]

[162] A. Gilyén, Z. Song, E. Tang. An improved quantum-inspired algorithm for linear regression. Quantum-Austria, 2022, 6: 754. [J]

[163] E. Tang. Quantum-inspired classical algorithms for principal component analysis and supervised clustering. 2018. [M]

[164] A. Dey, S. Dey, S. Bhattacharyya, et al. Quantum Inspired Automatic Clustering Algorithms: A comparative study of Genetic Algorithm and Bat Algorithm. 2020. [M]

[165] Q. Wei, H. Ma, C. Chen, et al. Deep Reinforcement Learning With Quantum-Inspired Experience Replay. IEEE Transactions on Cybernetics, 2022, 52(9): 9326-38. [J]

[166] Z. Chen, Y. Li, X. Sun, et al. A Quantum-inspired Classical Algorithm for Separable Non-negative Matrix Factorization. 2019. [M]

[167] N.-H. Chia, A. Gilyén, T. Li, et al. Sampling-based sublinear low- rank matrix arithmetic framework for dequantizing Quantum machine learning. Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing. Chicago, IL, USA;


image


Association for Computing Machinery. 2020: 387– 400.10.1145/3357713.3384314. [Z]

[168] N.-H. Chia, A. Gilyén, H.-H. Lin, et al. Quantum-Inspired Algorithms for Solving Low-Rank Linear Equation Systems with Logarithmic Dependence on the Dimension; proceedings of the International Symposium on Algorithms and Computation, F, 2020. [C]

[169] N.-H. Chia, T. Li, H.-H. Lin, et al. Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches. 2019. [M]

[170] D. Jethwani, F. L. Gall, S. K. Singh. Quantum-Inspired Classical Algorithms for Singular Value Transformation; proceedings of the International Symposium on Mathematical Foundations of Computer Science, F, 2019. [C]

[171] A. Narayanan, M. Moore. Quantum-inspired genetic algorithms; proceedings of the Proceedings of IEEE International Conference on Evolutionary Computation, F 20-22 May 1996, 1996. [C]

[172] S. Jun, F. Bin, X. Wenbo. Particle swarm optimization with particles having quantum behavior; proceedings of the Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat No04TH8753), F 19-23 June 2004, 2004. [C]

[173] S. Jun, X. Wenbo, F. Bin. A global search strategy of quantum- behaved particle swarm optimization; proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, 2004, F 1-3 Dec. 2004, 2004. [C]

[174] L. Wang, Q. Niu, M. Fei. A Novel Quantum Ant Colony Optimization Algorithm; proceedings of the Bio-Inspired


Computational Intelligence and Applications, Berlin, Heidelberg, F 2007//, 2007. Springer Berlin Heidelberg. [C]

[175] L. Wang, Q. Niu, M. Fei. A novel quantum ant colony optimization algorithm and its application to fault diagnosis. Transactions of the Institute of Measurement and Control, 2008, 30(3-4): 313-29. [J]

[176] Z. Cao, Y. Zhang, J. Guan, et al. Link Prediction based on Quantum- Inspired Ant Colony Optimization. Scientific Reports, 2018, 8(1): 13389. [J]

[177] J. Khudair Madhloom, H. N. Abd Ali, H. A. Hasan, et al. A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks. Electronics, 2023, 12(5): 1171. [J]

[178] X.-l. Ma, Y.-g. Li. An Improved Quantum Ant Colony Algorithm and its Application. IERI Procedia, 2012, 2: 522-7. [J]

[179] M. H. Silva, R. Schirru, J. A. C. C. Medeiros. An approach using quantum ant colony optimization applied to the problem of identification of nuclear power plant transients; proceedings of the INAC 2009: International nuclear atlantic conference Innovations in nuclear technology for a sustainable future, Brazil, F, 2009. [C]