Zizhu Wang - Solving quantum problems with variational optimization with deep generative networks

Fri, 20 Feb 2026 00:00:00 +0000

Solving quantum problems with variational optimization with deep generative networks

This seminar, given by Zizhu Wang, will happend on 20 February 2026, at 13:0. It will take place in Room 24-25/405.

Find a map of the campus here.

Abstract

The Variational Generative Optimization Network (VGON) provides a model-agnostic variational optimization framework that utilizes deep generative models to solve difficult quantum optimization problems. Unlike methods that directly tune parameters, VGON learns a mapping of probability transport from a latent space to the solution space. This enables the parallel and efficient generation of a large number of high-quality, highly diverse approximate optimal solutions on classical hardware, such as GPUs. We apply VGON to four quantum tasks: (1) Identifying optimal high-dimensional quantum states for entanglement detection; (2) Finding the ground states of one-dimensional many-body spin models via variational quantum circuits, while avoiding the barren plateau problem; (3) Generating an orthogonal basis for the degenerate ground-state space of many-body Hamiltonians—specifically, diverse degenerate ground states—in a single training run; and (4) Generating robust pulse sequences for superconducting quantum gates.

Zizhu Wang | LIP6 - QI Team

Zizhu Wang - Solving quantum problems with variational optimization with deep generative networks

Solving quantum problems with variational optimization with deep generative networks

Abstract