What does it mean to ask feminist questions of the worlds we study?
Join Aryn Martin from York University in a talk about addressing this question and whether having more women in science is the same as having more feminism in science.
Machine learning is an interesting family of problems for which near-term quantum devices can provide considerable advantages. In particular, exponential quantum speedup is recently demonstrated in learning a Boolean function that calculates the parity of a randomly chosen input bit string and a hidden bit string in the presence of noise, the problem known as learning parity with noise (LPN).
The quantum query complexity of a function f measures how many bits of the input a quantum computer must look at in order to compute f.
Observations reveal the cosmos to be astonishingly simple, and yet deeply puzzling, on the largest accessible scales. Why is it so nearly symmetrical? Why is there a cosmological constant (or dark energy) and what fixes its value? How did everything we see emerge from a singular “point” in the past?
Wavelength selective thermal emitters are highly desired for the development of the compact/energy-efficient spectroscopic sensing systems capable of detecting various gases such as COx, CH4, and NOx, which are strongly needed in environmental science, medical care, and other industrial applications. In addition, for the latter applications, dynamic control of thermal emission intensity is important for such emitters because synchronous detection can increase the signal-to-noise ratio significantly.
Quantum random-access memories (qRAM) are required by numerous quantum algorithms. In many cases, qRAM queries are the limiting factor in the implementation of these algorithms. In the limit of a large number of queries, there will be a massive resource overhead, as in this regime it is not possible to bypass the need for active error correction. In this talk, I will present our work towards quantifying this overhead. We will explore a variety of different qRAM circuits designed to query classical bits in superposition.
A random walk on a graph, P, with marked vertex set M, finds a marked vertex using a O(HT(P,M)) steps of the walk, where HT(P,M) is the hitting time. Previous quantum algorithms could detect the presence of a marked vertex in O(sqrt{HT(P,M)}) steps, or find a marked vertex in O(sqrt{HT(P,M)}) steps if M contained at most one vertex, but the case of finding in the presence of multiple marked vertices was left as an open problem.
Rangefinding has many applications in navigation, civil engineer, construction, military, surveillance and security. Most commonly rangefinders estimate the distance to an object by measuring the time of flight of light for the journey to and returning from the target. Conventional techniques use lasers for illumination in state of the art rangefinding systems. However, the particular state of light lasers produce makes them easy to detect.
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.
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.