A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures

TL;DR

This paper introduces a quantum-inspired probabilistic deep learning model that effectively predicts and designs functional meta-structures by capturing all plausible configurations based on desired performance metrics.

Contribution

It proposes a novel probability-density-based neural network inspired by quantum mechanics for inverse design of meta-structures, enhancing design diversity and accuracy.

Findings

Accurately predicts multiple plausible meta-structures for given spectral targets.

Enriches design options by capturing all likely candidates in the probability distribution.

Demonstrates effectiveness through designing structures with specific transmission spectra.

Abstract

In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at the core of the microcosmos. Meanwhile, machine learning inverse design of materials raised intensive attention, resulting in various intelligent systems for matter engineering. Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures. Our probability-density-based neural network (PDN) can accurately capture all plausible meta-structures to meet the desired performances. Local maxima in probability density distribution correspond to the most likely candidates. We verify this approach by designing multiple meta-structures for each targeted transmission spectrum to enrich design choices.