Events

Xiaodong Ma: Topological insulator and the quantum anomalous Hall effect

The quantum anomalous Hall effect (QAHE) is defined as a quantized Hall effect in a system without an external magnetic field. Its physical origin relies on the intrinsic topological inverted band structure and ferromagnetism.

Yoonseok Lee, University of Florida

After the discovery of topological insulators, the concept of topology permeated the various fields of condensed matter physics. Symmetry of a quantum system plays an intriguing role in close association with topology, expanding the range of topological quantum systems to superconductors/superfluids. Superfliuid 3He, which has been a prime example of symmetry breaking phase transition, is also recognized as a quantum system with various topological nature.

Bradley Hauer, University of Alberta

Cavity optomechanics, a field which studies the interplay between the photonic and phononic modes of an optical cavity, has seen rapid progress over the past decade. Micro/nano-scale optomechanical cavities have demonstrated potential for use in technologies such as quantum-limited metrology and transduction, as well as probes for exploring the fundamental nature of quantum mechanics.

David Layden - Massachusetts Institute of Technology (MIT)

Sensors based on quantum effects can measure various external quantities, such as magnetic fields, with high precision. Moreover, their sensitivity can scale more favourably with their size than is allowed classically — a property analogous to quantum speedups in computing. As with quantum computers, the performance of quantum sensors is limited by decoherence. Quantum error correction (QEC) has recently emerged as a promising approach to mitigate this decoherence, and therefore, to enhance sensitivity.

Michael Jarret, Perimeter Institute for Theoretical Physics

The runtime of Adiabatic optimization algorithms are typically characterized by the size of the spectral gap of the corresponding Hamiltonian. Gap analysis nonetheless remains a challenging problem with few general approaches.

A Case Study in Patent Development

This presentation will delve into a practical example of a patent procedure associated to a specific quantum technology: a vectorial magnetometer. We will explore the specifics of the technology and its applications, review previously existing approaches and define the inventive step, explore the phrasing of the claims, and revisit the prior patents from the freedom-to-operate point of view.

Robin Kothari, Microsoft Research (PLEASE NOTE NEW DATE AND TIME)

We use the polynomial method to prove optimal or nearly optimal lower bounds on the quantum query complexity of several problems, resolving open questions from prior work. The problems studied include k-distinctness, image size testing, k-junta testing, approximating statistical distance, approximating Shannon entropy, and surjectivity.​ Paper available at https://arxiv.org/abs/1710.09079. This is joint work with Mark Bun and Justin Thaler.

Keysight's Quantum Engineering Toolkit: A commercial, customizable integrated control and test system

Presented by guest speaker Nizar Messaoudi, Keysight Technologies Application Engineer

With traditional classical complementary metal oxide semiconductor (CMOS) computing struggling to keep up with Moore’s law, interest in quantum computing has exploded and the University of Waterloo is at the centre of this technological revolution.

PhD Seminar

Chunhao Wang, PhD candidate

David R. Cheriton School of Computer Science

We give a dissipative quantum search algorithm that is based on a novel dissipative query model. If there are $N$ items and $M$ of them are marked, this algorithm performs a fixed-point quantum search using $O(\sqrt{N/M}\log(1/\epsilon))$ queries with error bounded by $\epsilon$. In addition, we present a continuous-time version of this algorithm in terms of Lindblad evolution.

PhD Seminar

Chunhao Wang, PhD candidate

David R. Cheriton School of Computer Science

We present a quantum algorithm for simulating the dynamics of Hamiltonians that are not necessarily sparse. Our algorithm is based on the assumption that the entries of the Hamiltonian are stored in a data structure that allows for the efficient preparation of states that encode the rows of the Hamiltonian. We use a linear combination of quantum walks to achieve a poly-logarithmic dependence on the precision.