Topgen: Topology-aware bottom-up generator for variational quantum circuits

This paper proposes a bottom-up approach to generate topology-specific ansatz for Variational Quantum Algorithms (VQA), considering circuit size and real device noise in the ansatz design process. The method involves generating topology-compatible sub-circuits with desirable properties such as high expressibility and entangling capability, and combining them to form an initial ansatz. The authors further propose circuits stitching to solve the sparse connectivity issue between sub-circuits, and dynamic circuit growing to improve the accuracy. The ansatz constructed with this method is highly flexible, allowing exploration of a much larger design space than previous state-of-the-art methods. The proposed approach is benchmarked using Quantum Neural Networks (QNN) for Machine Learning (ML) tasks. PDF