Colloquium

I give an overview of trapped ion quantum information experiments and discuss prospects for implementing multi-valued quantum logic using trapped ions. Qudits (the multi-state generalization of qubits) are attractive for quantum computing because they enable a much larger Hilbert space for the same number of trapped ions, which may allow us to improve the information capacity of a quantum processor. I describe possible advantages and disadvantages of using qudits in place of qubits, and lay out some of the protocols that my lab will test for implementing measurements, single-qudit operations, and two-qudit operations in a trapped ion system.

In this talk I will discuss generalising the mapping from quantum error correcting codes to stat. mech. models (originally due to Dennis, Kitaev, Landahl and Preskill) to the case of correlated Pauli noise. This mapping connects the error correcting threshold to a phase transition, allowing us to use methods for studying stat. mech. models to estimate code thresholds. Using this, I will present numerical estimates of toric code thresholds under correlated noise. Time permitting, I will also discuss how this mapping can be used to give a tensor network-based algorithm for approximate optimal decoding of 2d topological codes, generalising the decoder of Bravyi, Suchara and Vargo.

The basic geometry of the Solar System - the shapes, spacings, and

orientations of the planetary orbits - has long been a subject of

fascination as well as inspiration for planet-formation theories. For

exoplanetary systems, those same properties have only recently come

into focus. I will review our current knowledge of the occurrence of

planets around other stars, their orbital distances and

eccentricities, the orbital spacings and mutual inclinations in

multiplanet systems, the orientation of the host star's rotation axis,

and the properties of planets in binary-star systems. I will also

discuss opportunities to improve our understanding, with data from the

recently launched Transiting Exoplanet Survey Satellite.

I will discuss the status and future of numerical lattice Quantum Chromodynamics (QCD) calculations for nuclear physics. With advances in supercomputing, we are beginning to quantitatively understand nuclear structure and interactions directly from the fundamental quark and gluon degrees of freedom of the Standard Model. Recent studies provide insight into the neutrino-nucleus interactions relevant to long-baseline neutrino experiments, double beta decay, and nuclear sigma terms needed for theory predictions of dark matter cross-sections at underground detectors. I will also address new work constraining `exotic glue’ in nuclei, which will be measurable for the first time at a future electron-ion collider, and explain how machine learning tools are providing new possibilities in this field.

Despite much theoretical effort, there is no complete theory of the “strange” metal phase of the high temperature 

superconductors, and its linear-in-temperature resistivity. This phase is believed to be a strongly-interacting metallic 

phase of matter without fermionic quasiparticles, and is virtually impossible to model accurately using traditional

perturbative field-theoretic techniques. Recently, progress has been made using large-N techniques based on the 

solvable Sachdev-Ye-Kitaev (SYK) model, which do not involve expanding about any weakly-coupled limit. I will 

describe constructions of solvable models of strange metals based on SYK-like large-N limits, which can reproduce  

some of the experimentally observed features of strange metals and adjoining phases. These models, and further 

extensions, could possibly pave the way to developing a controlled theoretical understanding of the essential building 

blocks of the electronic state in correlated-electron superconductors near optimal doping.

A key prediction of the Lambda CDM framework of structure formation is that a host halo containing a Milky Way sized disk galaxy should contain hundreds of thousands of sub-dwarf galaxy mass dark matter subhalos. Devoid of stars, these substructures remain undetected. Detecting them will not only corroborate the existence of dark matter but also give crucial information on the particle nature of dark matter and how they cluster at small scale. Cold stellar streams originate when globular clusters are tidally disrupted in the Milky Way potential. Gravitational encounters with dark matter subhalos will perturb these streams' density. I will present a novel method of probing the fundamental nature of dark matter by statistically analyzing the power spectrum of the stream density. I will discuss the uncertainties that the known baryonic structures such as the Galactic bar, the spiral arms, the Galactic population of giant molecular clouds and globular clusters can impart. Understanding their effects will be crucial for deciding whether a particular stream can be used to probe dark matter substructures.  

While the term “wide-field telescope” might sound like an oxymoron, a strong argument can be made that wide-field instruments lie behind much of the success of Canadian astronomy. Furthermore, despite the large size of the optical-IR community in Canada, this success has been made possible by considering multiple wavelength windows, from gamma to radio, and access to a suite of facilities. Over the past two decades, through a pair of 10-year Long Range Plans, Canadian astronomy has carefully structured its facility access, to ensure, as best as can be reasonably hoped, that the community maintains access to world-leading facilities. I’ll summarize the progression of the community vision on wide-field astronomy, highlighting successes and some missteps. As we head towards the creation of the 3rd Canadian Long Range Plan for astronomy, I’ll also present some personal views on what scientific leadership means and the challenges that it presents for a smaller country (at least in the G7 sense!) like Canada.

Three dimensional fracton phases are new type of phases featuring exotic excitations called fractons. They are gapped point-like excitations constrained to move in sub-dimensional space. In this talk, I will present the gapped fracton topological order discovered in exact solvable models and gapless fracton phase described by U(1) symmetric tensor gauge theories. Their relation with ordinary topological ordered phase would be discussed in detail. Particularly, the fracton topological order exhibited in an exact solvable model called X-cube model can be constructed by coupling toric code layers. And it can also be obtained from a particular rank-2 symmetric tensor gauge theory called scalar charge theory by Higgs mechanism and partial confinement transition.

Searching for a proper set of order parameters which distinguishes different phases of matter sits in the heart of condensed matter physics. In this talk, I discuss topological invariants as (non-local) order parameters for symmetry protected topological (SPT) phases of fermions in the presence of time-reversal symmetry. It turns out that topological invariants provide a natural definition for the partial transpose of density matrices. The partial transpose can then be used to define an entanglement measure (analog of entanglement negativity) for mixed states of fermions. I will show that this quantity captures the mixed state entanglement in fermionic SPTs as well as in a system of free fermions with a Fermi surface.

Recently there have been several proposals of low-energy precision experiments that can search for new particles, new forces, and the Dark Matter of the Universe in a way that is complementary to collider searches. In this talk, I will present some examples involving atomic clocks, nuclear magnetic resonance, and astrophysical black holes accessible to LIGO.

The general purpose computer can run any program we can express in
symbolic logic; that makes it the go-to tool for accomplishing any task
that can be reduced to a computable function, and that's why software is
eating the world and cars and colliders and airplanes and pacemakers and
toasters are all just turning into computers in fancy cases.

That also means that every policy problem we can imagine will eventually
involve a computer -- and thus involve a lawmaker insisting that it must
be possible to make a computer that can run every program except for
some program that creates a problem in the world.

We don't know how to make that computer, but we can approximate it by
creating a computer with some spyware-like program running on it that
checks to see whether you're running a "naughty" program and shuts it down.

That's a spider we swallow to catch a fly. Here's the bird we swallow to
catch the spider: laws like Canada's C-11 make it a crime to
investigate, circumvent, or point out defects in systems that have these
spyware-like processes.

Companies have noticed that this offers some real upsides for them: they
can use these spyware-like processes to prevent users their customers
from using their own consumables (e.g. printer ink), parts, service
depots, apps -- anything that the company can command a high margin on,
provided that it's illegal for a customer to choose someone else's products.

What a deadly combination: companies are rapidly expanding the
constellation of devices that are locked in this way, and once a device
is locked in this way, it can't be investigated, its defects can't be
disclosed, and it can't be modified to improve it, mitigate its flaws,
and protects its owner from cybersecurity risk.

Peer review exists out of recognition that there is no way to know if
you're right until you let your enemies try to prove you wrong; in
creating a monopolistic right to control the use of products after they
were sold, Parliament also created the right for companies to enjoin the
most fundamental task of scientific knowledge creation: finding mistakes
and pointing them out.

This is urgent: our world is made of computers, those computers are
designed to treat their owners as their enemies, and security
researchers can't audit those computers without risking civil and
criminal reprisals. That's a catastrophe in the making.

The surprising success of learning with deep neural networks poses two fundamental challenges: understanding why these networks work so well and what this success tells us about the nature of intelligence and our biological brain. Our recent Information Theory of Deep Learning shows that large deep networks achieve the optimal tradeoff between training size and accuracy, and that this optimality is achieved through the noise in the learning process.

In this talk, I will focus on the statistical physics aspects of our theory and the interaction between the stochastic dynamics of the training algorithm (Stochastic Gradient Descent) and the phase structure of the Information Bottleneck problem. Specifically, I will describe the connections between the phase transition and the final location and representation of the hidden layers, and the role of these phase transitions in determining the weights of the network.

Based partly on joint works with Ravid Shwartz-Ziv, Noga Zaslavsky, and Shlomi Agmon.