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Solving classical and quantum physics many-body systems are amongst the hardest problems in the natural sciences, but also of fundamental importance for applications such as material and drug design. In this talk, I will give a an overview of fundamental physics problems at multiple time- and lengthscales and describe deep learning methods to address them: 1) solving the quantum-chemical electronic Schrödinger equation with deep variational Monte Carlo, 2) learning to coarse-grain many-body systems, and 3) sampling equilibrium states of classical many-body systems with generative learning.

Cloud computing allows users to rent computers from vendors including Amazon, Microsoft, and Google, paying for only the resources they use.  In this tutorial, I will give an intro to some of the services offered by the Google Cloud Platform, including bare and containerized Virtual Machines.

Since the discovery of the first exoplanets in the early 1990s, we have detected more than 4,000 worlds beyond our solar system. Many of these are similar in size to our Earth, leading to an obvious question: could any be habitable?

For now, we typically only know the size and orbit of these planets, but nothing about their surface conditions. Although we cannot know for sure if these worlds could support life, we can use models to speculate on what we might find there.

In her Nov. 6 talk at Perimeter Institute, astrophysicist and author Elizabeth Tasker will take audiences for a speculative stroll through a few of the alien worlds we’ve discovered in the galaxy, and ponder whether someone else may already call them home.

Elizabeth Tasker is an astrophysicist at the Japan Aerospace Exploration Agency (JAXA). Her research explores the formation of stars and planets using computer simulations. She is particularly interested in how diverse planets might be and what different conditions might exist beyond our Solar System. Elizabeth is also a keen science communicator and writer for the NASA NExSS “Many Worlds” online column. Her popular science book, The Planet Factory, was published in Canada last April.

Connections between 2D gapped quantum phases and the anyon fusion theory have been proven in various ways under different settings. In this work, we introduce a new framework connecting them by only assuming a conjectured form of entanglement area law for 2D gapped systems. We show that one can systematically define topological charges and fusion rules from the area law alone, in a well-defined way. We then derive the fusion rules of charges satisfy all the axioms required in the algebraic theory of anyons. Moreover, even though we make no assumption about the exact value of the constant sub-leading term of the entanglement entropy, this term is shown to be equal to the logarithm of the total quantum dimension of the anyon theory we defined. 

Many complex systems have a generative, or linguistic, aspect: life is written in the language of DNA; protein structure is written in a language of amino acids, and human endeavour is often written in text. Are there universal aspects of the relationship between sequence and structure? I am trying to answer this question using models of random languages. Recently I proposed a model of random context-free languages [1] and showed using simulations that the model has a transition from an unintelligent phase to an ordered phase. In the former, produced sequences look like noise, while in the latter they have a nontrivial Shannon entropy; thus the transition corresponds to the emergence of information-carrying in the language. 

In this talk I will explain the basics of natural language syntax, without assuming any prior knowledge of linguistics. I will present the results from the model above, and explain how the model is related to complex matrix models with disorder [2].

[1] https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.128301

[2] https://arxiv.org/abs/1902.07516

Docker provides "containerization" for software -- a way to package up a piece of software and all its dependencies, so that it can run the same way on different computers.  It is a promising technology for scientists, both for reproducibility, ease of collaboration, and for running software on different computers (like your laptop and different clusters).  I will go through the why and how of using Docker.  For supercomputers (such as our Symmetry machine), Docker isn't allowed due to security issues, but the Singularity program allows users to run Docker containers, so I'll show you how to run your Docker container on Symmetry using Singularity.

 Holographic quantum error correcting codes (HQECC) have been proposed as toy models for the AdS/CFT correspondence, and exhibit many of the features of the duality. HQECC give a mapping of states and observables. However, they do not map local bulk Hamiltonians to local Hamiltonians on the boundary. In this work, we combine HQECC with Hamiltonian simulation theory to construct a bulk-boundary mapping between local Hamiltonians, whilst retaining all the features of the HQECC duality. This allows us to construct a duality between models, encompassing the relationship between bulk and boundary energy scales and time dynamics.

It also allows us to construct a map in the reverse direction: from local boundary Hamiltonians to the corresponding local Hamiltonian in the bulk. Under this boundary-to- bulk mapping, the bulk geometry emerges as an approximate, low-energy, effective theory living in the code-space of an (approximate) HQECC on the boundary. At higher energy scales, this emergent bulk geometry is modified in a way that matches the toy models of black holes proposed previously for HQECC. Moreover, the duality on the level of dynamics shows how these toy-model black holes can form dynamically.

We will talk about recent progress in NMR technologies simulating topological phases. We will describe how states are prepared, how they are evolved in time and various tricks that we can play with it, including measurements of topological properties such as modular matrices, and thus potentially applied for identifying phases of matter in future simulations.

Machine learning has led to recent advancements in image processing, language translation, finance, robotics, musical and visual arts, and medical diagnosis. In this session, we will explore how machine learning can be applied within fields of physics. We will introduce fundamental concepts in machine learning such a neural networks and supervised vs. unsupervised learning, and then proceed to learn to use tools from Python's TensorFlow library.

Bring your laptop. You can attend remotely via Zoom <https://zoom.us/j/154009181>.