Physics

It is increasingly clear that scientific careers are strongly affected by social and cultural factors and are not determined only by merit. On one hand, socio-economic conditions limit the access of large portions of the population to higher education. This aspect is particularly acute in Latin America and the Caribbean. On the other hand, various forms of discrimination and conscious and unconscious bias also affect career choice and progression, especially in STEM areas. This results in disciplines in which various groups are highly underrepresented. Underrepresentation impacts career progression and, through its self-reinforcing character, makes it very difficult to modify the status quo. Diversity has many dimensions including race/ethnicity, gender, disability and sexual orientation, among others. Active policies are necessary to change the current situation of inequality. The STEM community must deeply reflect and debate how to diversify its composition and then take appropriate actions to implement those changes.  In this talk we will present the main outcomes of the “Increasing diversity and inclusion in science” workshop where activities are planned to learn about the current situation at various levels, from the point of view of STEM students and researchers at different career stages to those of policy makers and social scientists. Although the focus of the workshop will be on gender in science, intersectionalities with ethnicity will also be addressed. You are all invited to attend what we expect will be a very participative colloquium that hopefully will yield useful ideas for future actions and policies.

Quantum state tomography (QST) is the art of reconstructing an unknown quantum state through measurements. It is a key primitive for developing quantum technologies. Neural network quantum state tomography (NNQST), which aims to reconstruct the quantum state via a neural network ansatz, is often implemented via a basis-dependent cross-entropy loss function. State-of-the-art implementations of NNQST are often restricted to characterizing a particular subclass of states, to avoid an exponential growth in the number of required measurement settings. In this talk, I will discuss an alternative neural-network-based QST protocol that uses shadow-estimated infidelity as the loss function, named “neural-shadow quantum state tomography” (NSQST). After introducing NNQST and the classical shadow formalism, I will present numerical results on the advantage of NSQST over NNQST at learning the relative phases, NSQST’s noise robustness, and NSQST’s advantage over direct shadow estimation. I will also briefly discuss the future prospects of the protocol with different variational ansatz and randomized measurements, as well as its experimental feasibility.

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Zoom link https://pitp.zoom.us/j/94167105773?pwd=TXR3TUtwNjV4VFB4SEpvTkhqd29SUT09

Long-range entangled quantum matter encompasses a wealth of fascinating phenomena including fractionalization and criticality.  I will show how quantum dynamics involving measurements can both enable new kinds of long-range entangled states and facilitate their realization on quantum simulators.  In the first part, I will illustrate how competing measurements along with unitary time evolution can give rise to distinct universality classes of non-equilibrium criticality.  In the second part, I will show how measurements and unitary evolution conditioned on the measurement outcomes (“adaptive quantum circuits”) enable efficient preparation of long-range entangled matter.  Finally, I will demonstrate how environmental measurement (decoherence) can remarkably enrich quantum critical pure states, giving rise to renormalization group flows between quantum channels with important implications on the entanglement structure of the resulting critical mixed states.  

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Zoom link https://pitp.zoom.us/j/97087115629?pwd=NHdlWG9oM0xHUDIzU05sUWRKdWlFdz09