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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