Quantum Information in Relativity: Measurements and Causality
Divide-and-conquer method for approximating output probabilities of constant-depth, geometrically-local quantum circuits
Nolan Coble, University of Maryland, College Park
Quantum Computational Particle Physics
Meet with experts who have taken their academic experience and found opportunities to bring quantum to market. As part of this panel, they will discuss their personal pathway into commercialization including challenges and lessons learned.
Optimal Theory Control Techniques for Nitrogen Vacancy Ensembles
Nitrogen Vacancy (NV) Centers in diamond are a very versatile tool. A single Nitrogen Vacancy center is most notably known for sensing magnetic fields, but recently has presented itself as a functional node for a quantum internet, to name just two of its wide ranges of applications.
A direct product theorem for quantum communication complexity with applications to device-independent QKD
Srijita Kundu, University of Waterloo
Observation and manipulation of a phase separated state in a charge density wave material
Join us for Quantum Today, where we sit down with researchers from the University of Waterloo’s Institute for Quantum Computing (IQC) to talk about their work, its impact and where their research may lead.
In partnership with the Kitchener Public Library, join John Donohue for a conversation with author and researcher Chad Orzel. They'll be talking about Orzel's latest book, A Brief History of Timekeeping.
About the book:
Sharp and engaging, A Brief History of Timekeeping is a story not just about the science of sundials, sandglasses, and mechanical clocks, but also the politics of calendars and time zones, the philosophy of measurement, and the nature of space and time itself.
We examine the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime. We will show that, for local noise that is sufficiently weak and unital, the output distribution p_noisy of typical circuits can be approximated by F*p_ideal + (1−F)*p_unif, where F is the probability that no local errors occur, p_ideal is the distribution that would arise if there were no errors, and p_unif is the uniform distribution.