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Physics has been driven by the discovery of novel phases of matter, largely in materials.  Recently, the advent of both quantum error correction and machine learning, viewed as physical phenomena, has compelled us to revisit the notion of a phase itself.  I will show how decodable/undecodable regimes of codes constitute distinct phases in a precise sense, and they furnish machine unlearnable/learnable phases of data.  I will demonstrate how conditional mutual information (CMI) serves as an essential quantity in characterizing the corresponding phases and transitions and thus provides a new diagnostic for both error correction thresholds and machine learnability of quantum and classical systems, including real-world data.  

In this broad-audience talk, I will discuss the basics of gravitational wave theory and the major achievements in the field following the direct observation of the first gravitational wave signal in 2015. Then, I will discuss how gravitational wave data are used to test the validity of general relativity and motivate my current research on the subject.

Since the 1980s, simulation of quantum many-body systems has been a leading candidate for practical quantum advantage. Yet computing thermal equilibrium properties, a central pillar of this vision, still lacks an end-to-end algorithmic solution. Today, I will present a general-purpose algorithmic and analytic framework for preparing quantum thermal states as a quantum analog of Markov chain Monte Carlo (MCMC) methods, and as a workable model of nature’s system-bath interaction.