Journal
APPLIED PHYSICS REVIEWS
Volume 8, Issue 4, Pages -Publisher
AIP Publishing
DOI: 10.1063/5.0060481
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Funding
- U.S. Department of Energy (DOE), Office of Science through the Quantum Science Center (QSC), a National Quantum Information Science Research Center
- Purdue's Elmore ECE Emerging Frontiers Center The Crossroads of Quantum and AI
- National Science Foundation [2029553-ECCS]
- DARPA/DSO Extreme Optics and Imaging (EXTREME) Program [HR00111720032]
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In this work, a machine learning-based framework was developed to map continuous-space inverse design problems into binary optimization problems. By repeatedly resampling and retraining the factorization machine, designs with superior figures of merit were achieved.
In recent years, there is growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework's performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications.
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