Colloquium Calendar

Recent strides in machine learning have shown that computation can be performed by practically any controllable physical system that responds to physical stimuli encoding data [1]. This perspective opens new frontiers for computational approaches using Physical Neural Networks (PNNs) [2, 3, 4] and provides a framework to deepen our understanding of their biological counterparts—neural circuits in living organisms.

One of NASA’s primary science goals is to directly image and characterize the atmospheres of Earth-like planets orbiting nearby stars. The observations are extremely challenging because the planets appear adjacent to their parent stars, near telescope diffraction limits, and their reflected light is 10 billion times fainter than the star. An elegant solution to this problem, first proposed by Lyman Spitzer in 1962, is to employ a starshade, a flower-shaped diffraction screen positioned far in front of a telescope so that it shadows the starlight without blocking the planet light.

A pre-eminent problem of modern physics is to reconcile the disparity between the laws of quantum physics – which apply at the small scale – and the laws of gravity – that apply at the large scale. This talk will motivate the nature of the fundamental problem and highlight some of the ideas we are currently pursuing to address it. In particular, a decisive confrontation of gravity and quantum theory calls for mechanical objects, massive enough to measurably gravitate with each other, prepared in quantum states of their motion.