Paper accepted by AAAI 2024!

Paper on quantum neural network is accepted by AAAI 2024 🎉

Our paper Exponential Hardness of Optimization from the Locality in Quantum Neural Networks by Hao-Kai Zhang, Chengkai Zhu, Geng Liu, and Xin Wang was accepted by AAAI 2024!

For AAAI 2024: Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI) (formerly the American Association for Artificial Intelligence) is the premier scientific society dedicated to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines.

For the paper Exponential Hardness of Optimization from the Locality in Quantum Neural Networks: Quantum neural networks (QNNs) have become a leading paradigm for establishing near-term quantum applications in recent years. The trainability issue of QNNs has garnered extensive attention, spurring demand for a comprehensive analysis of QNNs in order to identify viable solutions. In this work, we propose a perspective that characterizes the trainability of QNNs based on their locality. We prove that the entire variation range of the loss function via adjusting any local quantum gate vanishes exponentially in the number of qubits with a high probability for a broad class of QNNs. This result reveals extra harsh constraints independent of gradients and unifies the restrictions on gradient-based and gradient-free optimizations naturally. We showcase the validity of our results with numerical simulations of representative models and examples. Our findings, as a fundamental property of random quantum circuits, deepen the understanding of the role of locality in QNNs and serve as a guideline for assessing the effectiveness of diverse training strategies for quantum neural networks.