Variational Optimization for Quantum Problems using Deep Generative Networks

TL;DR

This paper introduces a variational generative network that efficiently finds high-quality solutions for quantum problems, outperforming traditional methods and enabling rapid, parallelizable optimization on classical hardware.

Contribution

A novel variational generative optimization network that learns to produce optimal quantum solutions, avoiding barren plateaus and handling degenerate states efficiently.

Findings

Identifies entangled states with optimal advantage in entanglement detection.

Achieves ground state energy of an 18-spin model without barren plateau issues.

Outputs multiple orthogonal ground states after a single training run.

Abstract

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.