in silico
Phase 1 : In Silico Validation of QPM Inverse-Design Algorithm
- Objectif
- Validate that the QPM-inspired Fourier-space optimization algorithm can produce a synthetic potential V_syn(r) that yields a target Hamiltonian with fidelity F ≥ 0.95 for small system sizes (L=4), and identify the regime of validity for the key assumptions (non-commutativity, finite-size effects).
- Coût estimé
- €500-2000
- Durée estimée
- 4-6 weeks
- Critères de réussite
- Fidelity F for N_PM=2, Λ=4a0, δφ=0 · F ≥ 0.95 (mean over 20 realizations) · (Exact diagonalization of H_full and overlap with target state |ψ_target⟩)
- Fidelity F for N_PM=3,4,5 at Λ=4a0, δφ=0 · F ≥ 0.90 for N_PM=3, F ≥ 0.85 for N_PM=4, F ≥ 0.80 for N_PM=5 · (Same as above)
- Non-commutativity error: 1 - |⟨ψ_exact|ψ_QPM⟩| · ≤ 0.05 (i.e., overlap ≥ 0.95) · (Compute exact ground state of H_full and the state predicted by QPM (assuming commutativity))
- Fidelity under phase noise δφ=0.01 rad · F ≥ 0.93 (mean over 10 noise realizations) · (Add Gaussian noise to φ_m, diagonalize H_full, compute F)
- On continue si
- All success criteria are met (F ≥ 0.95 for N_PM=2, non-commutativity error ≤ 0.05, fidelity under noise ≥ 0.93).
- On arrête si
- Fidelity for N_PM=2 is < 0.90, or non-commutativity error > 0.10, or fidelity under noise drops below 0.85.
- On réoriente si
- If fidelity is > 0.90 but < 0.95 for N_PM=2, try alternative optimization algorithms (genetic algorithm via DEAP library, or gradient-based optimization with JAX) and re-evaluate. If non-commutativity error is > 0.05 but < 0.10, consider adding a correction term to the QPM design (e.g., iterative refinement).
- Risques
- Simulated annealing fails to converge to a global optimum for large N_PM (≥4) or small Λ (≤2).Probabilité : mediumParade : Use multi-start optimization (20 random initial patterns) and compare with genetic algorithm (DEAP) or Bayesian optimization (scikit-optimize).
- Non-commutativity error is large (>0.10) for all parameter regimes, invalidating the QPM analogy.Probabilité : lowParade : If error is systematic, develop a perturbative correction scheme (e.g., Schrieffer-Wolff transformation) to adjust V_syn(r) and re-optimize.
- Finite-size effects (L=4) cause discretization artifacts that artificially inflate fidelity.Probabilité : mediumParade : Repeat the analysis for L=6 and L=8 (computationally feasible with exact diagonalization for up to 8 sites per dimension, i.e., 64 sites). If fidelity drops significantly for larger L, adjust the algorithm to account for finite-size effects.