We train a generative language model on the randomized local measurement data collected from Schrödinger's cat quantum state. We demonstrate that the classical reality emerges in the language model due to the information bottleneck: although our training data contains the full quantum information about Schrödinger's cat, a weak language model can only learn to capture the classical reality of the cat from the data. We identify the quantum-classical boundary in terms of both the size of the quantum system and the information processing power of the classical intelligent agent, which indicates that a stronger agent can realize more quantum nature in the environmental noise surrounding the quantum system. Our approach 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.

I am an Associate Professor of Physics at the University of California, San Diego. His research lies at the intersection of quantum many-body physics, quantum information science, and machine learning. He is known for pioneering work on symmetric mass generation, entanglement dynamics in quantum circuits, and AI-assisted quantum state learning. Recent efforts focus on developing machine learning algorithms for quantum error correction, quantum tomography, and representation learning of quantum systems. He is a recipient of the NSF CAREER Award and UC Hellman Fellowship, and he serves on the editorial board of Machine Learning: Science and Technology.