09:30  10:30

EunAh Kim
(Cornell University)
overview talk
Learning Quantum Emergence with AI
The application of artificial neural network to central questions in the theory of quantum matter is a rapidly developing field. So far, much more progress has been in using AI to solve computational bottlenecks either through speed up or through an efficient representation of the wave function or probability. In this talk, I will emphasize the advantage of AIhuman intelligence synergy in dealing with both computational and experimental data. In the computational front, I will discuss the use of quantum loop topography that bridges theoretical concepts such as response function with the artificial neural network. In the experimental front, I will discuss how we can use AI to gain new theoretical insight from experimental data.

10:30  11:00

coffee break

11:00  11:25

Everard van Nieuwenburg
(California Institute of Technology)
Machine learning the dynamics of quantum systems
In this contribution I will focus on the possibilities of using machine learning methods to classify and/or predict the dynamics of a quantum system. In particular, the classification of (dynamical) phases from time traces of observables has proven to be useful in the study of manybody localized systems. On the other hand, the prediction of quantum dynamics using machine learning is an open problem for which I will present one possible approach.

11:25  11:50

Alexandre Dauphin
(The Institute of Photonic Sciences)
Adversarial neural networks to manybody phase transitions
Neural networks hold the promise for progress on some of the computationally most demanding problems in condensed matter physics, such as the investigation of the manybody localization transition. The main challenges in this case are the lack of a good and universally accepted order parameter (instead a whole zoo of quantities has been proposed to study and pin down the transition) and the need for an immense amount of disorder averaging. We follow a more radical ansatz to work around these problems. We avoid the definition of any order parameter or observable and instead exploit a stateoftheart technique in deep learning and unsupervised learning to find the phase transition as the point at which the eigenfunctions of the Hamiltonian undergo a qualitative change. Domain adversarial neural networks work directly on the eigenstates of a quantum system, which is a spin1/2 Heisenberg chain in a random magnetic field in this case. We enforce the extraction of invariant features from the states with the adversarial approach and classify the states by studying the properties of invariant features with unsupervised machine learning methods. Through the use of convolutional filters, our method is scalable and needs much less disorder averaging than traditional numerical methods. This reduces the numerical effort for mapping out the phase diagram by a factor of ~50. Since our method is unsupervised, it is capable of uncovering physics that other deep learning networks are blind to. We show that our approach overcomes a weakness of transfer learning and opens the door to more accurate prediction of phase transitions. Moreover, we demonstrate the importance of an automatically extracted invariant representation of the physical data to study complex problems.

11:50  12:15

Nicolas Regnault
(CNRS ENS)
Many body localization and thermalization: (machine) learning from the entanglement spectrum

12:15  13:15

lunch

13:15  14:30

discussion

14:30  14:55

Nobuyuki Yoshioka
(The University of Tokyo)
Learning Disordered Topological Phases by Statistical Recovery of Symmetry
We apply the artificial neural network in a supervised manner to map out the quantum phase diagram of disordered topological superconductor in class DIII. Given the disorder that keeps the discrete symmetries of the ensemble as a whole, translational symmetry which is broken in the quasiparticle distribution individually is recovered statistically by taking an ensemble average. By using this, we classify the phases by the artificial neural network that learned the quasiparticle distribution in the clean limit and show that the result is totally consistent with the calculation by the transfer matrix method or noncommutative geometry approach. If all
three phases, namely the Z2, trivial, and the thermal metal phases appear in the clean limit, the machine can classify them with high confidence over the entire phase diagram. If only the former two phases are present, we find that the machine remains confused in the certain region, leading us to conclude the detection of the unknown phase which is eventually identified as the thermal metal phase.

14:55  15:20

Yi (Frank) Zhang
(Cornell University)
Quantum Loop Topography for Machine learning
Despite the rapidly growing interest in harnessing machine learning to identify quantum phases in manybody systems, a key challenge is in extracting essential, nonlocal information and efficiently passing it to the artificial neural network. We show that human intelligence can complement artificial intelligence and preselect the relevant information from the 'big data.' For instance, we introduce Quantum Loop Topography that consists of operators taking semilocal loop structures and determined by the characteristics of the targeted phases, such as physical responses or quasiparticle statistics. Therefore, Quantum Loop Topography offers a coherent, featureselection interface between the artificial neural network and the sample states. With this architecture, we demonstrate that even a simple neural network with a single fullyconnected hidden layer can be trained to distinguish Chern insulator and fractional Chern insulator, as well as $Z_2$ quantum spin liquid, with high efficiency and fidelity. I’ll also summarize recent progress on tackling other quantum phases, reverse engineering artificial neural networks, and connections with quantum entanglement. Our work paves the route towards powerful applications of machine learning in the study of quantum phases, phase transitions, and beyond.

15:20  16:00

coffee break

16:00  16:30

discussion

16:30  16:55

Masatoshi Imada
(University of Tokyo)
Machine Learning on HighTc Experimental Data and Boltzmann Machine on Quantum ManyBody Problems
We discuss how to combine Boltzmann machine learning techniques with conventional variational Monte Carlo methods developed for decades with various physical intuitions, to reach stateoftheart accurate ground states of quantum spin and fermion models [1,2]. We next discuss how a machine learning technique combined with existing angleresolved photoemission spectra offers an emergent understanding on a grand challenge at the heart of the superconducting mechanism of the cuprate superconductors, in otherwise inaccessible ways.
[1] Y. Nomura, A. S. Darmawan, Y. Yamaji and M. Imada: Phys. Rev. B 96, 205152 (2017)
[2] Y. Nomura, G. Carleo and M. Imada: arXiv:1802.09558

16:55  17:20

Petru Tighineanu
(Max Planck Institute for the Science of Light)
Reinforcement learning with neural networks for quantum memory
In our recent work (https://arxiv.org/abs/1802.05267) we employ reinforcement learning to train a deep artificial neural network that discovers, tabula rasa (i.e., with no human knowledge or guidance), complete quantumerrorcorrection strategies in a collection of quantum bits subject to decoherence. The network discovers optimal encoding and decoding protocols of the logical qubit as well as intricate correction strategies that are adapted to the measurement outcomes. A key novelty of our work is the development of an immediate reward scheme based on a physically meaningful quantity describing the capacity to protect the quantum information stored in a quantum memory. Our work opens the prospect for developing fully automated quantumerrorcorrection strategies in complex quantum systems.
In the first part of the talk I will explain how the neural network develops on its own the building blocks of a quantumerrorcorrection strategy: the encoding of the logical qubit, the error detection and correction, and the decoding of the logical qubit at the end of the simulation time. In the second part I will explain how our approach can be used to develop strategies that are fully adapted to the subtleties of the quantum device such as the hardware specifications and the noise. I will also present a detailed analysis of the internal workings of the neural network.

17:20  18:00

discussion

18:00  19:00

dinner

19:00  19:30

discussion

19:30

poster session (focus on even poster numbers)
