Variational Optimization for Quantum Problems using Deep Generative Networks
This addresses optimization bottlenecks in quantum information and computing, offering a model-agnostic, parallelizable approach that runs on classical hardware.
The authors tackled optimization problems in quantum science by developing a variational generative optimization network that maps random inputs to high-quality solutions. The method rapidly identified entangled states with optimal advantage in entanglement detection, attained ground state energy of an 18-spin model without barren plateaus, and output multiple orthogonal ground states of degenerate models.
Optimization drives advances in quantum science and machine learning, yet most generative models aim to mimic data rather than to discover optimal answers to challenging problems. Here we present a variational generative optimization network that learns to map simple random inputs into high quality solutions across a variety of quantum tasks. We demonstrate that the network rapidly identifies entangled states exhibiting an optimal advantage in entanglement detection when allowing classical communication, attains the ground state energy of an eighteen spin model without encountering the barren plateau phenomenon that hampers standard hybrid algorithms, and-after a single training run-outputs multiple orthogonal ground states of degenerate quantum models. Because the method is model agnostic, parallelizable and runs on current classical hardware, it can accelerate future variational optimization problems in quantum information, quantum computing and beyond.