Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multi-level communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://github.com/yushuowiki/ALTER.

Quantum reinforcement learning (QRL) is one promising algorithm proposed for near-term quantum devices. Early QRL proposals are effective at solving problems in discrete action space, but often suffer from the curse of dimensionality in the continuous domain due to discretization. To address this problem, we propose a quantum Deep Deterministic Policy Gradient algorithm that is efficient at solving both classical and quantum sequential decision problems in the continuous domain. As an application, our method can solve the quantum state-generation problem in a single shot: it only requires a one-shot optimization to generate a model that outputs the desired control sequence for arbitrary target state. In comparison, the standard quantum control method requires optimizing for each target state. Moreover, our method can also be used to physically reconstruct an unknown quantum state.