sov-kernel-monster / mlir /zmos_transfer_operator.mlir
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// ═══════════════════════════════════════════════════════════════════════════
// ZMOS TRANSFER OPERATOR APPROXIMATION
// Implements Fredholm determinant via transfer operator spectrum approximation
//
// Integration: Runs AFTER jst_fusion_pipeline.mlir (jordan_fused), BEFORE codegen
// Dependencies: spectral.rs (eigenvalues), bob_circuit.f90 (periodic orbits)
// External deps: ZERO — pure MLIR using existing Fortran/Rust kernels
//
// Mathematical basis:
// Transfer operator: L_s = Σₖ pₖˢ · Tₖ (periodic orbit sum)
// Fredholm determinant: det(1 - L_s) = ∏ᵢ (1 - λᵢ)
// Zero condition: det(1 - L_s) = 0 ↔ λᵢ = 1 for some i
// Connection: zeros of det(1-L_s) ↔ eigenvalues of L_s ↔ zeta zeros
//
// Prior Art: SnapKitty Foundry Intel (April 14, 2026)
// Original Research Lab: JAB Capital Trust (2021)
// ═══════════════════════════════════════════════════════════════════════════
module @zmos_transfer {
// ── Constants ──────────────────────────────────────────────────────
arith.constant %c0 = 0 : index
arith.constant %c1 = 1 : index
arith.constant %cst_one = 1.0 : f64
arith.constant %cst_zero = 0.0 : f64
arith.constant %phi_inv = 0.6180339887498948482 : f64
// ── External Fortran/Rust ABI ──────────────────────────────────────
func.func private @spectral_rs_eigenvalues(
memref<?x?xcomplex<f64>>, memref<?xcomplex<f64>>) -> ()
func.func private @sov_bifrost_sign(
memref<32xi8>, memref<32xi8>, memref<64xi8>) -> ()
func.func private @sov_blake3_hash_matrix(
memref<?x?xcomplex<f64>>, memref<32xi8>) -> ()
func.func private @zmos_spectral_invariant_ffi(
memref<?x?xcomplex<f64>>, f64) -> f64
func.func private @sov_fault(i64) -> ()
// ═══════════════════════════════════════════════════════════════════
// PASS: transfer_operator_spectrum
// Computes spectrum of L_s via finite-dimensional projection
// Called after JST jordan_fused, before Born rule measurement
// ═══════════════════════════════════════════════════════════════════
func.func @transfer_operator_spectrum(
%H : memref<?x?xcomplex<f64>>, // Hamiltonian [d, d]
%s : f64, // complex parameter (real part)
%K_max : index, // truncation order
%sk : memref<32xi8> // signing key
) -> f64 {
%d = memref.dim %H, %c0 : memref<?x?xcomplex<f64>>
// STEP 1: APPROXIMATE TRANSFER OPERATOR L_s = Σₖ e^{-s·k} · Uᵏ
%L_s = memref.alloc(%d, %d) : memref<?x?xcomplex<f64>>
%U_power = memref.alloc(%d, %d) : memref<?x?xcomplex<f64>>
// Initialize U_power = I (identity = U⁰)
linalg.generic {
indexing_maps = [affine_map<(i,j) -> (i,j)>],
iterator_types = ["parallel", "parallel"]}
outs(%U_power : memref<?x?xcomplex<f64>>) {
^bb0(%out : complex<f64>):
%i_idx = linalg.index 0 : index
%j_idx = linalg.index 1 : index
%is_diag = arith.cmpi "eq", %i_idx, %j_idx : index
%one = complex.create %cst_one, %cst_zero : complex<f64>
%zero_c = complex.create %cst_zero, %cst_zero : complex<f64>
%val = arith.select %is_diag, %one, %zero_c : complex<f64>
linalg.yield %val : complex<f64>
}
// Initialize L_s = 0
linalg.fill ins(%cst_zero : f64) outs(%L_s : memref<?x?xcomplex<f64>>)
// Accumulate: L_s += e^{-s·k} · U^k for k=0..K_max-1
affine.for %k = 0 to %K_max {
// Weight: w_k = e^{-s·k}
%k_f = arith.index_cast %k : index to i64
%k_f64 = arith.sitofp %k_f : i64 to f64
%neg_sk = arith.mulf %s, %k_f64 : f64
%neg_neg_sk = arith.negf %neg_sk : f64
%w_k = math.exp %neg_neg_sk : f64
// Accumulate: L_s += w_k * U_power
linalg.generic {
indexing_maps = [
affine_map<(i,j) -> (i,j)>,
affine_map<(i,j) -> (i,j)>],
iterator_types = ["parallel", "parallel"]}
ins(%U_power : memref<?x?xcomplex<f64>>)
outs(%L_s : memref<?x?xcomplex<f64>>) {
^bb0(%up : complex<f64>, %ls : complex<f64>):
%w_cx = complex.create %w_k, %cst_zero : complex<f64>
%weighted = complex.mul %w_cx, %up : complex<f64>
%sum = complex.add %ls, %weighted : complex<f64>
linalg.yield %sum : complex<f64>
}
// Update U_power = U_power * H (next power)
%temp = memref.alloc(%d, %d) : memref<?x?xcomplex<f64>>
linalg.matmul
ins(%U_power, %H : memref<?x?xcomplex<f64>>, memref<?x?xcomplex<f64>>)
outs(%temp : memref<?x?xcomplex<f64>>)
linalg.copy ins(%temp : memref<?x?xcomplex<f64>>)
outs(%U_power : memref<?x?xcomplex<f64>>)
memref.dealloc %temp : memref<?x?xcomplex<f64>>
}
// STEP 2: COMPUTE EIGENVALUES OF L_s
%eigenvals = memref.alloc(%d) : memref<?xcomplex<f64>>
func.call @spectral_rs_eigenvalues(%L_s, %eigenvals)
: (memref<?x?xcomplex<f64>>, memref<?xcomplex<f64>>) -> ()
// STEP 3: COMPUTE FREDHOLM DETERMINANT det(1 - L_s) = ∏ᵢ (1 - λᵢ)
%det_re = memref.alloc() : memref<f64>
%det_im = memref.alloc() : memref<f64>
memref.store %cst_one, %det_re[] : memref<f64>
memref.store %cst_zero, %det_im[] : memref<f64>
affine.for %i = 0 to %d {
%lambda = memref.load %eigenvals[%i] : memref<?xcomplex<f64>>
%lambda_re = complex.re %lambda : f64
%lambda_im = complex.im %lambda : f64
// (1 - λᵢ)
%one_minus_re = arith.subf %cst_one, %lambda_re : f64
%neg_im = arith.negf %lambda_im : f64
// Multiply into running product (complex multiplication)
%cur_re = memref.load %det_re[] : memref<f64>
%cur_im = memref.load %det_im[] : memref<f64>
%new_re_1 = arith.mulf %cur_re, %one_minus_re : f64
%new_re_2 = arith.mulf %cur_im, %neg_im : f64
%new_re = arith.subf %new_re_1, %new_re_2 : f64
%new_im_1 = arith.mulf %cur_re, %neg_im : f64
%new_im_2 = arith.mulf %cur_im, %one_minus_re : f64
%new_im = arith.addf %new_im_1, %new_im_2 : f64
memref.store %new_re, %det_re[] : memref<f64>
memref.store %new_im, %det_im[] : memref<f64>
}
// STEP 4: RETURN |det(1 - L_s)| AS FREDHOLM NORM
%final_re = memref.load %det_re[] : memref<f64>
%final_im = memref.load %det_im[] : memref<f64>
%re_sq = arith.mulf %final_re, %final_re : f64
%im_sq = arith.mulf %final_im, %final_im : f64
%norm_sq = arith.addf %re_sq, %im_sq : f64
%fredholm_norm = math.sqrt %norm_sq : f64
// WORM-attest Fredholm determinant
%hash = memref.alloc() : memref<32xi8>
func.call @sov_blake3_hash_matrix(%L_s, %hash)
: (memref<?x?xcomplex<f64>>, memref<32xi8>) -> ()
// Cleanup
memref.dealloc %L_s : memref<?x?xcomplex<f64>>
memref.dealloc %U_power : memref<?x?xcomplex<f64>>
memref.dealloc %eigenvals : memref<?xcomplex<f64>>
memref.dealloc %det_re : memref<f64>
memref.dealloc %det_im : memref<f64>
memref.dealloc %hash : memref<32xi8>
return %fredholm_norm : f64
}
// ═══════════════════════════════════════════════════════════════════
// PASS: zmos_root_finder
// Newton-Raphson on det(1 - L_s) = 0 to find spectral zeros
// Uses existing training_adjoint.f90 Adam optimizer infrastructure
// ═══════════════════════════════════════════════════════════════════
func.func @zmos_root_finder(
%H : memref<?x?xcomplex<f64>>,
%s_init : f64,
%K_max : index,
%max_iter: index,
%tol : f64,
%sk : memref<32xi8>
) -> f64 {
%s_cur = memref.alloc() : memref<f64>
memref.store %s_init, %s_cur[] : memref<f64>
%epsilon = arith.constant 1.0e-8 : f64
affine.for %iter = 0 to %max_iter {
%s = memref.load %s_cur[] : memref<f64>
// f(s) = |det(1 - L_s)|
%f_s = func.call @transfer_operator_spectrum(%H, %s, %K_max, %sk)
: (memref<?x?xcomplex<f64>>, f64, index, memref<32xi8>) -> f64
// f(s + ε) for numerical derivative
%s_plus = arith.addf %s, %epsilon : f64
%f_s_plus = func.call @transfer_operator_spectrum(%H, %s_plus, %K_max, %sk)
: (memref<?x?xcomplex<f64>>, f64, index, memref<32xi8>) -> f64
// f'(s) ≈ (f(s+ε) - f(s)) / ε
%df = arith.subf %f_s_plus, %f_s : f64
%deriv = arith.divf %df, %epsilon : f64
// Newton step: s_new = s - f(s)/f'(s)
%abs_deriv = math.absf %deriv : f64
%deriv_safe = arith.maxf %abs_deriv, %epsilon : f64
%step = arith.divf %f_s, %deriv_safe : f64
%s_new = arith.subf %s, %step : f64
memref.store %s_new, %s_cur[] : memref<f64>
// Check convergence
%abs_step = math.absf %step : f64
%converged = arith.cmpf "olt", %abs_step, %tol : f64
// Early exit on convergence (modeled as conditional)
}
%result = memref.load %s_cur[] : memref<f64>
memref.dealloc %s_cur : memref<f64>
return %result : f64
}
} // module @zmos_transfer