We present a classification architecture based on the Blume-Capel (BC) spin-1 lattice model operating at the tricritical gravity point (T* = 3. 2196, m* = 0. 9317). The complete engine is 49 lines of Python with zero external dependencies and a 20-byte model per class. We introduce Freq (m*) encoding, which maps input features to frequency-modulated balanced ternary at m* harmonics, achieving 90. 6% average accuracy across 37 benchmarks against sklearn-kNN (90. 1%). In the few-shot regime (1–2 training samples per class), DarkWare outperforms kNN by +19 to +36 percentage points. We validate the architecture on 104 IBM quantum jobs across four experimental rounds on Heron r2 processors (ibmₘarrakesh), confirming the BC vacancy fraction p₀ = 0. 052 ± 0. 001 (theory: 0. 051) and observing that input class ‘c’ locks to the BC resonance magnetization m* = 0. 9317 under two independent encoding methods. We identify the design boundary: trit quantization fails when normalized inter-class feature gaps fall below 0. 33. Three classification domains (letters, vibration faults, and Iris species) produce distinguishable per-site spin signatures on transmon hardware, establishing the first BC lattice classification on superconducting qutrits.
Dor Pinchas (Mon,) studied this question.