PIRSA:23060044

[VIRTUAL] Emergent Classicality from Information Bottleneck

You, Y. (2023). [VIRTUAL] Emergent Classicality from Information Bottleneck. Perimeter Institute. https://pirsa.org/23060044

You, Yi-Zhuang. [VIRTUAL] Emergent Classicality from Information Bottleneck. Perimeter Institute, Jun. 15, 2023, https://pirsa.org/23060044 

          @misc{ pirsa_PIRSA:23060044,
            doi = {10.48660/23060044},
            url = {https://pirsa.org/23060044},
            author = {You, Yi-Zhuang},
            keywords = {Condensed Matter},
            language = {en},
            title = {[VIRTUAL] Emergent Classicality from Information Bottleneck},
            publisher = {Perimeter Institute},
            year = {2023},
            month = {jun},
            note = {PIRSA:23060044 see, \url{https://pirsa.org}}
          }

Yi-Zhuang You

University of California, San Diego

Talk number
PIRSA:23060044
Collection
Talk Type
Subject
Abstract
Our universe is quantum, but our everyday experience is classical. Where is the boundary between quantum and classical worlds? How does classical reality emerge in quantum many-body systems? Does the collapse of the quantum states involve intelligence? These are fundamental questions that have puzzled physicists and philosophers for centuries. The recent development of quantum information science and artificial intelligence offers new opportunities to investigate these old problems. In this talk, we present our preliminary research on using a transformer-based language model to process randomized measurement data collected from Schrödinger’s cat quantum state. We show that the classical reality emerges in the language model due to the information bottleneck: although our training data contains the full quantum information of Schrödinger’s cat, a weak language model can only learn the classical reality of the cat from the data. Our study opens up a new avenue for using the big data generated on noisy intermediate-scale quantum (NISQ) devices to train generative models for representation learning of quantum operators, which might be a step toward our ultimate goal of creating an artificial intelligence quantum physicist.