Future students

Alvaro Alhambra, Perimeter Institute

Heat-Bath Algorithmic Cooling is a technique for producing pure quantum systems by utilizing a surrounding heat-bath. Here we connect the study of these cooling techniques to the resource theory of athermality, enabling us to derive provably optimal cooling protocols under a variety of experimental restrictions on the available control.

Rescheduled from Tuesday, February 12

QUANTUM + Film: A screening of 10 Quantum Shorts

A festival for quantum-inspired films

The Quantum Shorts festival called for short films inspired by quantum physics and the universe answered. Filmmakers all over the world responded with their movies.

Fabrication and Growth of 111 SiNWs for Mechanical Spin-Detection

Pardis Sahafi, Institute for Quantum Computing

In our group, vertical Si nanowires grown on a 111 surface are used for force detection in nanoscale NMR and ESR. These measurements require a very long (20 µm) and minimally tapered vertical Si nanowires, to be used as nano-mechanical oscillators with a high quality factor (Q ~ 10⁴).

Alexander Belovs, University of Latvia

This talk reflects on recent research in progress with Andras Gilyen. Over the years, there have been a number of papers dealing with quantum algorithms testing some properties of classical probability distributions. Our goal is to understand what is the right way for quantum algorithms to access the distribution. There is a number of possible models, and we analyse their mutual strength.

IQC and the Department of Physics at the University of Waterloo welcome Shahpoor Moradi, University of Calgary

Quantum computation has been developed as a computationally efficient paradigm to solve problems that are intractable with conventional classical computers. Quantum computers have the potential to support the simulation and modeling of many complex physical systems, not just quantum ones, significantly more rapidly than conventional supercomputers.

Frederic Magniez, Université Paris Diderot

We describe a new quantum paradigm, that we call Quantum Chebyshev’s inequality, to approximate with relative error the mean of any random variable with a number of quantum samples that is linear in the ratio of the square root of the variance to the mean. Classically the dependency is quadratic. To illustrate this method, we apply it to the approximation of frequency moments in the multi-pass streaming model, and to the approximation of the number of edges and triangles in the quantum graph query access model.