講師： Dr. Giuseppe Carleo
題目：Neural-network Quantum States
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 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 , 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 , 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, and Troyer — Science 355, 602 (2017).
 Carrasquilla, and Melko — Nat. Physics doi:10.1038/nphys4035 (2017)
 Wang — Phys. Rev. B 94, 195105 (2016)
 van Nieuwenburg, Liu, and Huber — Nat. Physics doi:10.1038/nphys4037 (2017)
 Torlai, Mazzola, Carrasquilla, Troyer, Melko, and Carleo — arXiv:1703.05334 (2017)
紹介教員：今田正俊 教授、山地洋平 特任准教授（ともに物理工学専攻）