Neural-network Quantum States

Artificial intelligence is living truly exciting times thanks to the fast advancements in the field of machine learning. Machine-learning-based approaches, routinely adopted in cutting-edge industrial applications, are being increasingly adopted to study fundamental problems in science as well. Very recently, their effectiveness has been demonstrated also for many-body physics [1-3].
In this seminar I will present recent applications to quantum physics. First, I will discuss how a systematic machine learning of the many-body wave-function can be realized. This goal has been achieved in [1], introducing a variational representation of quantum states based on artificial neural networks. In conjunction with Monte Carlo schemes, this representation can be used to study both ground-state and unitary dynamics, with controlled accuracy. Moreover, I will show how a similar representation can be used to perform efficient Quantum State Tomography on highly-entangled states [4], previously inaccessible to state-of-the art tomographic approaches.

[1] Carleo, and Troyer -- Science 355, 602 (2017).
[2] Carrasquilla, and Melko -- Nat. Physics doi:10.1038/nphys4035 (2017)
[3] van Nieuwenburg, Liu, and Huber -- Nat. Physics doi:10.1038/nphys4037 (2017)
[4] Torlai, Mazzola, Carrasquilla, Troyer, Melko, and Carleo -- arXiv:1703.05334 (2017)