arXiv subjects

Yiting Mao

Publications and source records attributed to Yiting Mao.

3 recordsLinked to original sources

arXiv2026

Encoding complex-balanced thermalization in quantum circuits

Non-Markovian dynamics in open quantum systems often invalidates the complex-balanced thermalization framework, hindering predictive control of quantum simulation platforms designed to prepare out-of-equilibrium states at prescribed temperatures. We resolve this bottleneck by engineering reservoir qubits as modular microscopic units coupled to a target quantum system and constructing a quantum-circuit platform that enforces strictly Markovian complex-balanced thermalization. The platform exploits the non-orthogonality of reservoir qubit eigenstates to drive inhomogeneous heating through a modified Kubo-Martin-Schwinger relation, and uses tunable microscopic time-reversibility breaking to generate amplification-dissipation dynamics. We demonstrate two applications: temporally correlated dichromatic emission and Liouvillian exceptional-point-protected quantum synchronization at finite temperatures, displaying predictive control over out-of-equilibrium state preparation.

quant-ph↗

arXiv2025

Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning

In the field of robot target recognition research driven by artificial intelligence (AI), factors such as the disordered distribution of targets, the complexity of the environment, the massive scale of data, and noise interference have significantly restricted the improvement of target recognition accuracy. Against the backdrop of the continuous iteration and upgrading of current AI technologies, to meet the demand for accurate recognition of disordered targets by intelligent robots in complex and changeable scenarios, this study innovatively proposes an AI - based intelligent robot disordered target recognition method using reinforcement learning. This method processes the collected target images with the bilateral filtering algorithm, decomposing them into low - illumination images and reflection images. Subsequently, it adopts differentiated AI strategies, compressing the illumination images and enhancing the reflection images respectively, and then fuses the two parts of images to generate a new image. On this basis, this study deeply integrates deep learning, a core AI technology, with the reinforcement learning algorithm. The enhanced target images are input into a deep reinforcement learning model for training, ultimately enabling the AI - based intelligent robot to efficiently recognize disordered targets. Experimental results show that the proposed method can not only significantly improve the quality of target images but also enable the AI - based intelligent robot to complete the recognition task of disordered targets with higher efficiency and accuracy, demonstrating extremely high application value and broad development prospects in the field of AI robots.

cs.RO↗

arXiv2024

Diagnosing thermalization dynamics of non-Hermitian quantum systems via GKSL master equations

The application of the eigenstate thermalization hypothesis to non-Hermitian quantum systems has become one of the most important topics in dissipative quantum chaos, recently giving rise to intense debates. The process of thermalization is intricate, involving many time-evolution trajectories in the reduced Hilbert space of the system. By considering two different expansion forms of the density matrices adopted in the biorthogonal and right-state time evolutions, we have derived two versions of the Gorini-Kossakowski-Sudarshan-Lindblad master equations describing the non-Hermitian systems coupled to a bosonic heat bath in thermal equilibrium. By solving the equations, we have identified a sufficient condition for thermalization under both time evolutions, resulting in Boltzmann biorthogonal and right-eigenstate statistics, respectively. This finding implies that the recently proposed biorthogonal random matrix theory needs an appropriate revision. Moreover, we have exemplified the precise dynamics of thermalization and thermodynamic properties with test models.

quant-ph↗