Эксперимент: Isometric tensor-network token mixer (#604)

{ "worked": true, "confidence": 9, "verdict": "Built a trainable complex butterfly/isometric token mixer with local U(2) gates, exact adjoint inverse, norm checks, controlled non-unitary perturbation sweeps, and a dense fitting baseline. The mechanism manifested: unitary errors stayed at 6.5e-16–7.6e-15 for N=4–64, inverse reconstruction error was 3.9e-15, and measured drift matched the predicted (1+epsilon)^(log2(N)*repetitions) law to about 2e-15 relative error across all sweep points. In the toy fit the structured mixer reached MSE 2.63e-9 versus 6.72e-14 for dense mixing with 128 real structured coordinates versus 256 complex dense entries; this confirms stability and compression, but not superior optimization or task accuracy.", "metrics": { "baseline": "Dense complex N=16 fit: final MSE 6.72e-14 after 300 steps; 256 complex parameters, equivalent to 512 real scalars.", "idea": "Structured U(2) butterfly N=16 fit: final MSE 2.63e-9 after 300 steps; 128 real coordinates; norm ratio approximately 1; unitarity Frobenius error <=7.61e-15 over N=4..64; inverse error 3.94e-15." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "No CIFAR/ViT or long-sequence task accuracy was tested, and no reliable wall-clock/FLOP speedup was established. The toy target was itself generated by the same structured family, the implementation uses a butterfly O(N log N) topology rather than the idea's broader QFT O(N log^2 N) construction, and gate parameterization/training at larger N was not evaluated." }