Physics-Based Data Augmentation for Quantum State Classification
bravo_abad · x · 2026-07-31
Data augmentation is usually based on heuristics (e.g., flipping an image doesn't change the label). This paper transforms the idea into physics, using quantum mechanics to guarantee that transformations cannot change the underlying label.
The authors tackle a challenging classification problem: determining which subsystems are entangled given several modes of light. This normally requires full quantum state tomography, which is impractical for continuous-variable systems in infinite-dimensional spaces.
The Approach: Instead, researchers feed binned joint probability distributions from homodyne detection—a cheap measurement available in any quantum optics lab—into a convolutional neural network. By deriving data augmentation from physics, they bypass the training data bottleneck, avoiding the heavy computation required to simulate density matrices and certify entanglement structures for every labeled example.
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