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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.

The mixing time of Markovian dissipative evolutions of open quantum many-body systems can be bounded using optimal constants of certain quantum functional inequalities, such as the logarithmic Sobolev constant. For classical spin systems, the positivity of such constants follows from a mixing condition for the Gibbs measure, via quasi-factorization results for the entropy.

Inspired by the classical case, we present a strategy to derive the positivity of the logarithmic Sobolev constant associated to the dynamics of certain quantum systems from some clustering conditions on the Gibbs state of a local, commuting Hamiltonian. In particular we address this problem for the heat-bath dynamics in 1D and the Davies dynamics, showing that the first one is positive under the assumptions of a mixing condition on the Gibbs state and a strong quasi-factorization of the relative entropy, and the second one under some strong clustering of correlations.

Global symmetries and gauge symmetries have played a crucial role in physics.  The idea of duality demonstrates that gauge symmetries can be emergent and might not be fundamental.  During the past decades it became clear that the circle of ideas about emergent gauge symmetries and duality is central in different branches of physics including Condensed Matter Physics, Quantum Field Theory, and Quantum Gravity.   We will review these developments, which highlight the unity of physics.

One of the best known open-source tools for studying numerical relativity is the Einstein Toolkit.  With it, you can simulate collisions of black holes and neutron stars, and study their horizons and gravitational waves. In this talk we learn more about what the Einstein Toolkit can do, how it works. An introductory, hands-on tutorial will follow.

Quantum complexity is a notion characterizing the universality of the entanglement arising from a quantum evolution. A universal evolution will result in a complex entanglement. At the same time, this also corresponds to small fluctuations and to unlearnability from the point of view of machine learning. All these aspects are connected to the different features of k-designs, which are under-samplings of the Hilbert space. 

We study the transition in complexity due to the doping of a quantum circuit by universal gates and show that the transition to complex entanglement can be obtained by just a single gate. 
These results are relevant for the notions of scrambling, quantum chaos, OTOCs and operator spreading.

We conjecture that the transition to 4−design, W-D and unlearnability are one and the same.

I will describe some connections between the Eigenstate Thermalization Hypothesis (ETH), the entanglement structure of generic excited eigenstates of chaotic quantum systems ("EE", arXiv:1906.04295), and the "bound on chaos" limiting the growth rate of the out-of-time-order four-point correlator in such systems ("OTOC", arXiv:1906.10808).