Physics

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>.

We give a quantum speedup for solving the canonical semidefinite programming relaxation for binary quadratic optimization. The class of relaxations for combinatorial optimization has so far eluded quantum speedups. Our methods combine ideas from quantum Gibbs sampling and matrix exponent updates. A de-quantization of the algorithm also leads to a faster classical solver. For generic instances, our quantum solver gives a nearly quadratic speedup over state-of-the-art algorithms.
This is joint work with Fernando Brandao (Caltech) and Daniel Stilck Franca (QMATH, Copenhagen).

There is a profound lack of diversity in science labs and classrooms, which has a negative impact on productivity. Scientific research demonstrates that diverse groups are more creative and better able to solve problems. Though the perception is that things are improving, NSERC’s recently released report shows that attrition rates in Canadian STEM fields are higher for women than for men at all career stages and that the percentage of women has not changed substantially in the last 15 years. Racialized and Indigenous people are also underrepresented at Canadian universities. This seminar will explore the scientific research surrounding EDI in hiring, writing reference letters, reviewing grants, and managing trainees. Also included are tips and tricks for incorporating EDI into grant applications.

Making use of modern multi-core computers (and multi-machine supercomputers) in Python can be achieved in a few different ways.  I will present the built-in Python “multiprocessing” module approach, which tends to work well and relatively painlessly for “naturally parallel” (aka “embarrassingly parallel”) problems. I’ll show what is required to use this approach, including how to submit jobs to the Slurm batch system on Symmetry.