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The 2nd talk of a monthly webinar series jointly hosted by Perimeter, IVADO, and Institut Courtois. Speaker: David Kremer Garcia, AI Engineer & Lead Data Scientist, IBM Quantum, Yorktown Heights, NY, USA. In this session, I will talk about how we are using AI to improve quantum circuit transpiling and optimization. I will show some of our recent work, where we apply AI methods such as Reinforcement Learning to different transpiling tasks and achieve a remarkable balance between speed and quality of the results. I will also talk about how we integrate these methods with other heuristics to provide "ai-enhanced transpiling" through our Qiskit Transpiler Service.

The Indigenous peoples from the Pacific Ocean are the People of the Sea. Over generations, our ancestors pioneered ways to intentionally travel across thousands of miles of open ocean using only the information nature provides. No external instruments were needed for guidance; rather the navigator became the most sophisticated instrument needed by tuning into the stars, winds, waves, birds and marine life.    In this presentation, I will share these stories along with principles that govern living and operating on traditional voyaging vessels. These values established for the canoes translated directly into the balance is attained between Pacific Indigenous peoples and their island nations. As we envision the future of our planet and look to weave together Indigenous wisdom with western science, we must learn to listen to what is offered by the people of the largest continent on the planet, the Pacific Ocean.

As part of a monthly webinar series jointly hosted by Perimeter, IVADO, and Institut Courtois, Ilya Shpitser will present an introduction to causal inference and its applications to problems in physics and computer science. This seminar will be fully on zoom and members of all three institutes are welcome. 

Abstract: In this talk I will give some history of ideas of causal inference, describe the causal inference workflow, including formalizing the cause-effect question in terms of a parameter, defining (or learning) the causal model, checking if the data has information about the desired parameter via identification theory, and efficiently estimating the parameter if it is identified.  I will briefly touch on connections of causal inference to other areas, discuss what machine learning and causal inference can teach each other, and describe some open problems.   Zoom TBC

Predicting the outcome of scattering processes of elementary particles in colliders is the central achievement of relativistic quantum field theory applied to the fundamental (non-gravitational) interactions of nature. While the gravitational interactions are too minuscule to be observed in the microcosm, they dominate the interactions at large scales. As such the inspiral and merger of black holes and neutron stars in our universe are now routinely observed by gravitational wave detectors. The need for high precision theory predictions of the emitted gravitational waveforms has opened a new window for the application of perturbative quantum field theory techniques to the domain of gravity. In this talk I will show how observables in the classical scattering of black holes and neutron stars can be efficiently computed in a perturbative expansion using a world-line quantum field theory; thereby combining state-of-the-art Feynman integration technology with perturbative quantum gravity. Here, the black holes or neutron stars are modelled as point particles in an effective field theory sense. Fascinatingly, the intrinsic spin of the black holes may be captured by a supersymmetric extension of the world-line theory, enabling the computation of the far field wave-form including  spin and tidal effects to highest precision. I will review our most recent results at the fifth order in the post-Minkowskian expansion amounting to the computations of hundreds of thousands of four loop Feynman integrals.

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Zoom link