Speaker: 王子竹教授，电子科技大学

Title: Variational Optimization for Quantum Problems using Deep Generative Networks

Language: Chinese

Time & Venue: 2025.04.28 14:30-15:30 思源楼813

Abstract: Optimization is one of the keystones of modern science and engineering. Its applications in quantum technology and machine learning helped nurture variational quantum algorithms and generative AI respectively. We propose a method to perform variational optimization based on classical generative models: the Variational Generative Optimization Network (VGON). We use three quantum tasks to show its quick convergence in the training phase and its ability to generate accurate and diverse nearly optimal solutions. For the first task of finding the best state in an entanglement-detection protocol, VGON greatly reduces the optimization time compared to stochastic gradient descent while generating nearly optimal quantum states. For the second task of finding the ground state of a 1D quantum spin model with variational quantum circuits, VGON alleviates the barren plateau problem in variational quantum circuits. For the final task of generating degenerate ground states of many-body quantum Hamiltonians, VGON can identify the degenerate ground state spaces after a single stage of training and generate a variety of states therein.