Opening the Box: Can Machine Learning Resolve Schrödinger’s Cat?

Machine learning cannot resolve the philosophical measurement problem of Schrödinger's cat, but it provides powerful tools for the operational challenges of quantum mechanics. Neural networks are used for quantum state tomography, acting as generative models to reconstruct a system's full quantum state from a limited number of measurements. Time-series models can forecast decoherence—the collapse of a superposition—by learning from environmental noise, while reinforcement learning agents can discover strategies to actively protect fragile quantum states. These ML techniques are crucial for building and controlling quantum computers by helping to see, predict, and protect quantum superpositions. While ML cannot distinguish between interpretations that make identical predictions, it could be used in anomaly detection to search for evidence of alternative physical theories.