arXiv subjects

Jianping Xu

Publications and source records attributed to Jianping Xu.

4 recordsLinked to original sources

arXiv2026

Surface code logical operations on a superconducting quantum processor

Fault-tolerant quantum computation requires logical operations that manipulate encoded information while preserving quantum error-correction protection. In planar surface-code architectures, code deformation and lattice surgery provide a local, measurement-based route to such operations. Here we experimentally realize key elements of patch-based surface-code logical processing on a 107-qubit superconducting quantum processor. We first implement a reusable primitive layer comprising merge and split, patch expansion and shrinkage, and deformations mediated by domain walls and twist defects. We then compose these primitives to realize logical state routing, the logical controlled-NOT gate, and the single-qubit Hadamard and phase gates, which together form a Clifford-generating set. All operations are implemented on distance-three rotated surface-code patches with multi-round syndrome extraction and neural-network decoding, without post-selection. Our results advance superconducting surface-code experiments from protected logical memory to active, patch-based fault-tolerant logical operations.

quant-ph↗

arXiv2023

Accelerating Chemical Exchange Saturation Transfer Imaging Using a Model-based Deep Neural Network With Synthetic Training Data

Purpose: To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil chemical exchange saturation transfer (CEST) data. Theory and Methods: Inspired by the variational network, the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed neural network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on three healthy volunteers and five brain tumor patients using retrospectively undersampled data with various acceleration factors, and compared with other state-of-the-art reconstruction methods. Results: The proposed CEST-VN method generated high-quality CEST source images and APT-weighted (APTw) maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original variational network. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the joint CEST-specific loss function and data-sharing block used. Conclusions: The proposed CEST-VN method can offer high-quality CEST source images and APTw maps from highly undersampled multi-coil data by integrating the deep-learning prior and multi-coil sensitivity encoding model.

physics.med-ph↗

arXiv2021

Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents

Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations. To automate this process and relieve engineers' burden, a novel multi-stage control approach is presented in this paper to train centralized and decentralized reinforcement learning agents that can automatically adjust grid controllers for regulating transmission line flows at normal condition and under contingencies. The power grid flow control problem is formulated as Markov Decision Process (MDP). At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits. If line overloading issues remain unresolved, stage two is used to train decentralized SAC agent via load throw-over at local substations. The effectiveness of the proposed approach is verified on a series of actual planning cases used for operating the power grid of SGCC Zhejiang Electric Power Company.

cs.LG↗

arXiv2021

Mean Trajectories of Multiple Tracking Points on A Brownian Rigid Body: Convergence, Alignment and Twist

We consider mean trajectories of multiple tracking points on a rigid body that conducts Brownian motion in the absence and presence of an external force field. Based on a na\"ıve representation of rigid body - polygon and polyhedron where hydrodynamic interactions are neglected, we study the Langevin dynamics of these Brownian polygons and polyhedra. Constant force, harmonic force and an exponentially decaying force are investigated as examples. In two dimensional space, depending on the magnitude and form of the external force and the isotropy and anisotropy of the body, mean trajectories of these tracking points can exhibit three regimes of interactions: convergence, where the mean trajectories converge to either a point or a single trajectory; alignment, where the mean trajectories juxtapose in parallel; twist, where the mean trajectories twist and intertwine, forming a plait structure. Moreover, we have shown that in general a rigid body can sample from these regimes and transit between them. And its Brownian behavior could be modified during such transition. Notably, from a polygon in two dimensional space to a polyhedron in three dimensional space, the alignment and twist regimes disappear and there is only the convergence regime survived, due to the two more rotational degrees of freedom in three dimensional space.

cond-mat.stat-mech↗