Observing Schrödinger's Cat with Artificial Intelligence: Emergent Classicality from Information Bottleneck
arxiv(2023)
摘要
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.
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