Simulating dynamical quantum Hall effect with superconducting qubits

We propose an experimental scheme to simulate the dynamical quantum Hall effect and the related interaction-induced topological transition with a superconducting-qubit array. We show that a one-dimensional Heisenberg model with tunable parameters can be realized in an array of superconducting qubits. The quantized plateaus, which is a feature of the dynamical quantum Hall effect, will emerge in the Berry curvature of the superconducting qubits as a function of the coupling strength between nearest neighbor qubits. We numerically calculate the Berry curvatures of two-, four- and six-qubit arrays, and find that the interaction-induced topological transition can be easily observed with the simplest two-qubit array. Furthermore, we analyze some practical conditions in typical experiments for observing such dynamical quantum Hall effect.Comment: 9 pages, 6 figures, version accepted by PR

The prediction of using LHAASO's cosmic-ray electron measurements to constrain decaying heavy dark matter

LHAASO is an instrument designed for detecting cosmic rays (CRs) and gamma rays at TeV to PeV energies. The decays of heavy dark matter particles in the Galactic halo may produce high-energy electrons that can be detected by LHAASO. The main background for the LHAASO's CR electron measurements is the hadron residuals due to mis-identification of the particle species. In this paper, we estimate the LHAASO's electron background using the known all-particle CR spectrum and the hadron rejection efficiency of LHAASO. With the estimated background, we predict the capability of LHAASO to constrain DM decay lifetime at 95% confidence level for various channels. We find that, if neglecting systematic uncertainties, the CR electron measurement by LHAASO can improve the current best results by up to on order of magnitude for DM masses between 100 - 1000TeV. However, indirect measurements of CR electrons by ground-based experiments suffer from uncertainties included in the calculation, the projected constraints will be largely weakened. So for using the CR electron observation of LHAASO to constrain the DM parameters, the key point is whether the systematic error can be effectively reduced.Comment: 11 pages, 8 figures, accepted for publication in PR

Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models

Recent advancements in natural language and Large Language Models (LLMs) have enabled AI agents to simulate human-like interactions within virtual worlds. However, these interactions still face limitations in complexity and flexibility, particularly in scenarios involving multiple characters and novel objects. Pre-defining all interactable objects in the agent's world model presents challenges, and conveying implicit intentions to multiple characters through complex interactions remains difficult. To address these issues, we propose integrating virtual Game Masters (GMs) into the agent's world model, drawing inspiration from Tabletop Role-Playing Games (TRPGs). GMs play a crucial role in overseeing information, estimating players' intentions, providing environment descriptions, and offering feedback, compensating for current world model deficiencies. To facilitate future explorations for complex interactions, we introduce a benchmark named Tachikuma, comprising a Multiple character and novel Object based interaction Estimation (MOE) task and a supporting dataset. MOE challenges models to understand characters' intentions and accurately determine their actions within intricate contexts involving multi-character and novel object interactions. Besides, the dataset captures log data from real-time communications during gameplay, providing diverse, grounded, and complex interactions for further explorations. Finally, we present a simple prompting baseline and evaluate its performance, demonstrating its effectiveness in enhancing interaction understanding. We hope that our dataset and task will inspire further research in complex interactions with natural language, fostering the development of more advanced AI agents.Comment: Preliminary version of an ongoing wor