Search Talks

I will discuss information theoretic properties of planar black hole microstates in 2 + 1 dimensional asymptotically anti-de Sitter spacetime, modeled by black holes with an end-of-the-world brane behind the horizon. The von Neumann entropy of sufficiently large subregions in the dual CFT exhibits a time-dependent phase, which from a doubly-holographic perspective corresponds to the appearance of quantum extremal islands in the brane description. Considering the case where dilaton gravity is added to the brane, we show that tuning the associated couplings affects the propagation of information in the dual CFT state. By requiring that information theoretic bounds on the growth of entanglement entropy are satisfied in the dual CFT, we can place bounds on the allowed values of the couplings on the brane.

Zoom link:  https://pitp.zoom.us/j/96173295706?pwd=REdGajBXbTlPYVFhL0s1c3lINXY5Zz09

Gravitational wave observations are revealing new features in the mass distribution of merging binary black holes (BBHs). The BBHs we observe today are relics of massive stars that lived in the early Universe, and we aim to use their properties to help reveal the lives and deaths of their stellar ancestors.   

In this talk, I will discuss which of the observed features are robust, and if/how we can use them to constrain the uncertain progenitor physics. I will focus on the lowest mass BHs, just above the edge of NS formation because we find they I) contain crucial information about the most common formation pathway, II) are least affected by uncertainties in the cosmic star formation, and III) shine new light on the much-disputed mass-gap between neutron stars and black holes.

Zoom link:  https://pitp.zoom.us/j/91476126992?pwd=QXdENmErYklaYTdLcDZNTVBXamlXdz09

QuEra is a quantum computing start up located in Boston, spinning off from the groups in the physics and engineering departments of Harvard and MIT. I have spent this fall working at QuEra and will introduce you to the company and its neutral-atom quantum computing technology.

"Motivated by applications in machine learning, we present a quantum algorithm for Gibbs sampling from continuous real-valued functions defined on high dimensional tori. We show that these families of functions satisfy a Poincare inequality. We then use the techniques for solving linear systems and partial differential equations to design an algorithm that performs zeroeth order queries to a quantum oracle computing the energy function to return samples from its Gibbs distribution. We further analyze the query and gate complexity of our algorithm and prove that the algorithm has a polylogarithmic dependence on approximation error (in total variation distance) and a polynomial dependence on the number of variables, although it suffers from an exponentially poor dependence on temperature."

The Floquet code implements a periodic sequence of two-qubit measurements to realize the topological order. After each measurement round, the instantaneous stabilizer group can be mapped to a honeycomb toric code, thus explaining the topological feature. However, the code also possesses a time-crystal order distinct from a stationary toric code – an e-m exchange after every cycle. In this work, we construct a continuous path interpolating between the Floquet and toric codes, focusing on the transition between the time-crystal and non-time crystal phases. We show that this transition is characterized by a diverging length scale. We also add single qubit noise to the model and obtain a two-dimensional parametric phase diagram of the Floquet code.

Zoom link:  https://pitp.zoom.us/j/95933569447?pwd=UkcrVHdkWERTNVIrcXdCREQ3Y1JUZz09

I will present recent progress in building a rigorous theory to understand how scientists, machines, and future quantum computers could learn models of our quantum universe. The talk will begin with an experimentally feasible procedure for converting a quantum many-body system into a succinct classical description of the system, its classical shadow. Classical shadows can be applied to efficiently predict many properties of interest, including expectation values of local observables and few-body correlation functions. I will then build on the classical shadow formalism to answer two fundamental questions at the intersection of machine learning and quantum physics: Can classical machines learn to solve challenging problems in quantum physics? And can quantum machines learn exponentially faster than classical machines?

Zoom link:  https://pitp.zoom.us/j/97994359596?pwd=UlBwc2hoSkNzWlZvM1o1RWErU1U2QT09