Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials

ABSTRACT

Optical metamaterials exhibit superior optical performance, yet their high‐dimensional design problems impede efficient global optimization. In this study, we introduce Quantum‑driven Learning and Exploration for Advanced Photonic metamaterials (Q‐LEAP) that integrates material screening, property calculation and quantum‐accelerated structural design. By incorporating a factorization machine (FM) algorithm and leveraging the quantum tunnelling effect of quantum annealing, we efficiently explore a vast solution space of containing 2 ¹⁰⁸ candidates, with a single optimization step completed in 2.56 ms, attaining an objective value that reaches 85.83% of its theoretical limit. To jointly evaluate runtime cost, sampling efficiency, and optimization effectiveness, we define normalized efficiency index on a natural‑log basis. Q‑LEAP reaches 21.77 × 10 ⁻² , approximately 40 times that of the best conventional method (0.55 × 10 ⁻² ). Furthermore, we combine first‑principles calculations with a physics‑informed residual machine learning (PRML) method to predict frequency‑dependent dielectric functions. While density functional perturbation theory spectra are truncated at the phonon cutoff frequency, PRML overcomes this by using the Lorentz oscillator model as physical guidance, ensuring the high‑frequency extrapolation obeys the underlying oscillator dynamics. The Q‐LEAP constitutes an efficient design strategy for de novo optical metamaterials, with quantum‑driven optimization delivering a substantial speedup. This speedup can be transferred to high‑dimensional problems in other domains.