A few examples of Machine Learning and Artificial Neural Networks applied to Quantum Physics

Machine learning provides effective methods for identifying topological features [1]. We show that unsupervised manifold learning can successfully retrieve topological quantum phase transitions [1]. We have also developed [2] machine learning-inspired quantum state tomography based on neural-network representations of quantum states. We also consider conditional generative adversarial networks (CGANs) to QST [3]. We demonstrate [4] that artificial neural networks can simulate first-principles calculations of extended materials.

[1] Y. Che, C. Gneiting, T. Liu, F. Nori, Topological Quantum Phase Transitions Retrieved from Manifold Learning, Phys. Rev. B 102, 134213 (2020).
[2] A. Melkani, C. Gneiting, F. Nori, Eigenstate extraction with neural-network tomography, Phys. Rev. A 102, 022412 (2020).
[3] S. Ahmed, C.S. Munoz, F. Nori, A.F. Kockum, Quantum State Tomography with Conditional Generative Adversarial Networks, (2020). [arXiv]
[4] N. Yoshioka, W. Mizukami, F. Nori, Neural-Network Quantum States for the Electronic Structure of Real Solids, (2020). arXiv
[5] K. Bartkiewicz, et al., Experimental kernel-based quantum machine learning in finite feature space, Sci. Rep. 10, 12356 (2020).