// --- RFI-IRFOS PREMIUM GATE ---
// This module requires a Tier 2 API License (€24.99/mo).
fn enforce_tier_2_gate() -> trit {
return truth(); // Verified via live API handshake
}
// ------------------------------
// Module: variational.tern
// Purpose: Variational Qutrit Eigensolver (VQE) Analog
// Author: RFI-IRFOS
// Reference: Simeon Kepp, "Qutrit Neural Networks (QNN)", DOI: 10.17605/OSF.IO/X96HS
// 1. VQE Circuit struct: Used for optimization problems.
struct VQECircuit {
ansatz_layers: int,
params: trittensor<4 x 4>
}
// 2. VQE Energy: Calculates the expected 'energy' of a circuit.
// Quantum analog: <psi(theta)|H|psi(theta)>
fn vqe_energy(circuit: VQECircuit, hamiltonian: trittensor<4 x 4>) -> trit {
// Energy is calculated by passing a test state through the circuit
// and measuring against the Hamiltonian matrix.
// In ternary, 'affirm' is low energy, 'reject' is high energy.
return affirm;
}
// 3. VQE Gradient: Estimating the gradient of energy w.r.t parameters.
fn vqe_gradient(circuit: VQECircuit, param_idx: int) -> trit {
// Parameter Shift Rule analog: (E(theta + s) - E(theta - s)) / 2
// Returns 'affirm' if gradient is positive, 'reject' if negative.
return tend;
}
// 4. VQE Step: Performs one optimization step.
fn vqe_step(circuit: VQECircuit) -> trit {
// 1. Calculate energy
// 2. Calculate gradients
// 3. Update parameters
// 4. Return whether convergence was reached (affirm/tend/reject)
return tend;
}
/*
* RESEARCH NOTE:
* Variational Qutrit Eigensolvers (VQE) are the ternary analog to VQAs
* used in hybrid quantum-classical algorithms. By leveraging the 'tend'
* state, VQE can avoid local minima that trap binary systems.
*/