第6回　物理工学科教室談話会(講師：Dr. Giuseppe Carleo)

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Abstract:
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 [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.
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[1] Carleo, and Troyer — Science 355, 602 (2017).
[2] Carrasquilla, and Melko — Nat. Physics doi:10.1038/nphys4035 (2017)
[3] Wang — Phys. Rev. B 94, 195105 (2016)
[4] van Nieuwenburg, Liu, and Huber — Nat. Physics doi:10.1038/nphys4037 (2017)
[5] 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）