Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session X68: DQI Invited Session: Machine Learning and Quantum PhysicsInvited
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Sponsoring Units: DQI Chair: Raphael Pooser, Oak Ridge National Lab Room: Four Seasons 4 |
Friday, March 6, 2020 11:15AM - 11:51AM |
X68.00001: Machine learning quantum states in the NISQ era Invited Speaker: Roger Melko We discuss the development of machine learning techniques for the purpose of reconstructing quantum states from projective measurement data. Technology adapted from a branch of unsupervised learning, called generative models, are well-suited for learning representations of quantum states from real experimental data. We discuss quantum state reconstruction with several classes of generative model, and compare in particular the performance of tractable and approximate density models. We demonstrate their practical use for state reconstruction, moving systematically through increasingly complex classes of pure and mixed quantum states. As an example of a use case for a real experimental noisy intermediate-scale quantum (NISQ) device, we review recent efforts in reconstructing a cold atom wavefunction. Finally, we discuss the outlook for scalable experimental state reconstruction using machine learning, in the NISQ era and beyond. |
Friday, March 6, 2020 11:51AM - 12:27PM |
X68.00002: About that useful little corner of Hilbert space and its neural network representations Invited Speaker: Giuseppe Carleo The vast majority of quantum states of interest for practical applications have distinctive features and intrinsic structure. These typically occupy only a very limited corner of the vast manifold of allowed quantum states, making them often amenable for compact classical representations. |
Friday, March 6, 2020 12:27PM - 1:03PM |
X68.00003: Machine Learning and Quantum Invited Speaker: Pengfei Zhang In this talk, I will first talk about “ machine learning of quantum problems”. Since quantum problems are governed by well-defined physical rules, they are good platforms to investigate explainable machine learning. Here I will give one example of learning potential-to-density mapping by the recurrent neural network, from which we can extract the Schrodinger equation. Secondly, I will talk about “ machine learning for quantum experiments”. Especially, I will describe how to use the idea of active learning to optimize quantum control, which requires minimal number of experimental data. Finally, I will talk about “ machine learning on quantum compute”. I will bring together the concept information scrambling and quantum machine learning. I will show that the tripartite information developed for quantifying information scrambling can be used to diagnose the training dynamics of a quantum neural network. |
Friday, March 6, 2020 1:03PM - 1:39PM |
X68.00004: Machine Learning with Tensor Networks Invited Speaker: Edwin Stoudenmire Tensor networks are a technique developed to compress otherwise exponentially large, many-body wavefunctions into a form such that they can be optimized and their properties computed with only polynomial effort. Key examples including the matrix product state (MPS) and projected entangled pair state (PEPS) tensor networks, which give state-of-the-art results for challenging systems of strongly correlated electrons. But in recent years, it has been appreciated that tensor networks are a broader technique for compressing very large linear functions, which can appear in many different problem domains. One very promising domain is machine learning of real-world data, where certain powerful models closely resemble wavefunction tensors and where tensor networks can be directly applied, just as in physics. |
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