Berlin 2018 – scientific programme

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DY: Fachverband Dynamik und Statistische Physik

DY 2: Focus Session: Recent Developments in Computational Many Body Physics (joint session TT/DY)

DY 2.6: Invited Talk

Monday, March 12, 2018, 12:15–12:45, H 0104

Machine Learning Methods for Quantum Many-Body Physics — •Giuseppe Carleo — ETH Zurich, Institute for Theoretical Physics Wolfgang-Pauli-Str. 27 8093 Zurich - Switzerland

Machine-learning-based approaches are being increasingly adopted in a wide variety of domains, and very recently their effectiveness has been demonstrated also for many-body physics [1-4]. In this talk 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 [5], previously inaccessible to state-of-the art tomographic approaches.

I will then briefly discuss, recent developments in quantum information theory, concerning the high representational power of neural-network quantum states.
Carleo, Troyer, Science 355, 602 (2017).
Carrasquilla, Melko, Nature Physics 13, 431 (2017).
Wang, Physical Review B 94, 195105 (2016).
van Nieuwenburg, Liu, Huber, Nature Physics 13, 435 (2017).
Torlai, Mazzola, Carrasquilla, Troyer, Melko, Carleo, arXiv: 1703.05334.