[1] Jack Y Araz and Michael Spannowsky. Classical versus quantum: comparing tensor network-based quantum circuits on lhc data. arXiv preprint arXiv:2202.10471, 2022.
[2] F Arute, K Arya, R Babbush, et al. Supplementary information for 'quantum supremacy using a programmable superconducting processor', Nat. Int. Wkly. J. Sci, 574:505-505, 2020.
[3] Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al. Quantum supremacy using a programmable superconducting processor. Nature, 574(7779):505-510, 2019.
[4] Reinhard Bauer, Tobias Columbus, Ignaz Rutter, and Dorothea Wagner. Search-space size in contraction hierarchies. Theoretical Computer Science, 645:112-127, 2016.
[5] Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In International Conference on Machine Learning (ICML), pages 41-48, 2009.
[6] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. Quantum machine learning. Nature, 549(7671):195-202, 2017.
[7] Christian Blum and Xiaodong Li. Swarm intelligence in optimization. In Swarm intelligence: introduction and applications, pages 43-85. Springer, 2008.
[8] Sima E Borujeni, Saideep Nannapaneni, Nam H Nguyen, Elizabeth C Behrman, and James E Steck. Quantum circuit representation of bayesian networks. Expert Systems with Applications, 176:114768, 2021.
[9] Lukas Burgholzer, Alexander Ploier, and Robert Wille. Simulation paths for quantum circuit simulation with decision diagrams. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022.
[10] Tianlong Chen, Xiaohan Chen, Wuyang Chen, Zhangyang Wang, Howard Heaton, Jialin Liu, and Wotao Yin. Learning to optimize: A primer and a benchmark. The Journal of Machine Learning Research, 23(1):8562-8620, 2022.
[11] Barry A Cipra. An introduction to the Ising model. The American Mathematical Monthly, 94(10):937-959, 1987.
[12] Nadav Cohen, Or Sharir, and Amnon Shashua. On the expressive power of deep learning: A tensor analysis. In Conference on Learning Theory, pages 698-728. PMLR, 2016.
[13] G Daniel, Johnnie Gray, et al. Opt\`einsum-a python package for optimizing contraction order for einsum-like expressions. Journal of Open Source Software, 3(26):753, 2018.
[14] Glen Evenbly and Guifre Vidal. Quantum criticality with the multi-scale entanglement renormalization ansatz. Strongly correlated systems: numerical methods, pages 99-130, 2013.
[15] Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, and J Ignacio Cirac. Expressive power of tensor-network factorizations for probabilistic modeling. In Thirty-third Conference on Neural Information Processing Systems (NeurIPS), 2019.
[16] Li-Hua Gong, Ling-Zhi Xiang, Si-Hang Liu, and Nan-Run Zhou. Born machine model based on matrix product state quantum circuit. Physica A: Statistical Mechanics and its Applications, 593:126907, 2022.
[17] Kelam Goutam, S Balasubramanian, Darshan Gera, and R Raghunatha Sarma. Layerout: Freezing layers in deep neural networks. SN Computer Science, 1(5):295, 2020.
[18] Johnnie Gray and Stefanos Kourtis. Hyper-optimized tensor network contraction. Quantum, 5:410, 2021.
[19] Diego Guala, Shaoming Zhang, Esther Cruz, Carlos A Riofrio, Johannes Klepsch, and Juan Miguel Arrazola. Practical overview of image classification with tensor-network quantum circuits. Scientific Reports, 13(1):4427, 2023.