We present Grammar Fingerprinting, a model-free method for identifying quantum processor hardware topology directly from raw measurement noise. Using SAX encoding and a character-level LSTM, the method extracts learned transition matrices from readout sequences and clusters them to classify hardware topology blindly. Applied to 28 Google Sycamore quantum supremacy configurations, it achieves 84.5% mean accuracy with six independent controls confirming the signal. A sharp data-length threshold at 8,000 measurements reveals long-range temporal structure in multi-qubit noise correlations. Full code, results, and validation included.
Dániel Csaplár (Sun,) studied this question.