This paper introduces an autoresearch framework that uses AI coding agents to optimize ground-state preparation protocols for quantum and quantum-classical many-body systems. It demonstrates how this method can effectively tune hyperparameters for VQE, DMRG, and AFQMC protocols, leading to improved energy proxies within fixed computational budgets across various spin models and molecular Hamiltonians.

This paper proposes QAROO, an AI-driven online task offloading framework for wireless-powered mobile edge computing (MEC) networks. It integrates quantum neural networks, attention mechanisms, and recurrent neural networks to co-optimize computing and energy resources, offering an efficient and stable solution for dynamic IoT environments.

This paper introduces Q-Tag, the first watermarking framework for Quantum Circuit Generative Models (QCGMs). It addresses the challenge of protecting intellectual property in automatically synthesized quantum circuits by embedding ownership signals directly into the generative process using a symmetric sampling strategy and a synchronization mechanism, ensuring both high fidelity and robust watermark detection against various attacks.