About

Ji Liu is an Associate Professor at the Department of Electrical and Computer Engineering, Stony Brook University. He is also affiliated with the Department of Applied Mathematics and Statistics, the Institute for AI-Driven Discovery and Innovation, and the Institute for Engineering-Driven Medicine. His research interests include distributed control, distributed optimization, distributed machine learning, distributed quantum computing, resilience of distributed algorithms, multi-agent systems, epidemic networks, social networks, and cyber-physical systems.

Research topics

• Computer Science
• Artificial Intelligence
• Political Science
• Computer Security
• Sociology
• Machine Learning
• Demography
• Social psychology
• Psychology
• Biology
• Medicine
• Ecology
• Internet privacy
• Operations research
• Mathematics
• Public relations
• Physics
• Algorithm
• Environmental health

Selected publications

• QuantEM: The quantum error management compiler

  ArXiv.org · 2025-09-19

  preprintOpen access1st authorCorresponding

  As quantum computing advances toward fault-tolerant architectures, quantum error detection (QED) has emerged as a practical and scalable intermediate strategy in the transition from error mitigation to full error correction. By identifying and discarding faulty runs rather than correcting them, QED enables improved reliability with significantly lower overhead. Applying QED to arbitrary quantum circuits remains challenging, however, because of the need for manual insertion of detection subcircuits, ancilla allocation, and hardware-specific mapping and scheduling. We present QuantEM, a modular and extensible compiler designed to automate the integration of QED codes into arbitrary quantum programs. Our compiler consists of three key modules: (1) program analysis and transformation module to examine quantum programs in a QED-aware context and introduce checks and ancilla qubits, (2) error detection code integration module to map augmented circuits onto specific hardware backends, and (3) postprocessing and resource management for measurement results postprocessing and resource-efficient estimation techniques. The compiler accepts a high-level quantum circuit, a chosen error detection code, and a target hardware topology and then produces an optimized and executable circuit. It can also automatically select an appropriate detection code for the user based on circuit structure and resource estimates. QuantEM currently supports Pauli check sandwiching and Iceberg codes and is designed to support future QED schemes and hardware targets. By automating the complex QED compilation flow, this work reduces developer burden, enables fast code exploration, and ensures consistent and correct application of detection logic across architectures.

  PublisherOA PDFDOI

• Evaluation of Insulation Performance in Oil-Immersed Power Equipment Under Low Temperatures

  Research Square · 2025-05-26

  preprintOpen access
  PublisherOA PDFDOI

• The Effect of Random Vibration on Performance of High-Speed Permanent Magnet Generator for Hydrogen Fueled Vehicle Turbocharged Drive Recovery

  International Journal of Applied Electromagnetics and Mechanics · 2025-02-12

  article

  Electromagnetic performance of the high-speed permanent magnet generator (HSPMG) used a hydrogen fueled vehicle turbocharged drive recovery with random vibration excitation is researched thoroughly in this paper. Random vibration excitation of unevenness of road surface is acquired by random vibration model constructed by comprehensive consideration of road condition. The natural frequencies and mode of vibration needed in random vibration analysis are extracted by modal analysis of the rotor of generator. The radial deformation of the rotor of HSPMG under extreme driving conditions is obtained by random vibration analysis and the maximum value of eccentric displacement in the air gap is 0.0075 mm. But the radial deformation of the rotor of HSPMG under normal driving conditions can be ignored. The electromagnetic performance of HSPMG with uneven air gap length caused by rotor eccentric displacement, such as flux density distribution of air gap, cogging torque, no-load torque and the no-load voltage are further studied, which the performance of HSPMG is optimized.

  PublisherDOI

• EGSRAL:An Enhanced 3D Gaussian Splatting Based Renderer with Automated Labeling for Large-Scale Driving Scene

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11

  articleOpen access

  3D Gaussian Splatting (3D GS) has gained popularity due to its faster rendering speed and high-quality novel view synthesis. Some researchers have explored using 3D GS for reconstructing driving scenes. However, these methods often rely on various types of data, such as depth maps, 3D bounding boxes, and trajectories of moving objects. Additionally, the lack of annotations for synthesized images limits their direct application in downstream tasks. To address these issues, we propose EGSRAL, a 3D GS-based method that relies solely on training images without extra annotations. EGSRAL enhances 3D GS's capability to model both dynamic objects and static backgrounds and introduces a novel adaptor for auto labeling, generating corresponding annotations based on existing annotations. We also propose a grouping strategy for vanilla 3D GS to address perspective issues in rendering large-scale, complex scenes. Our method achieves state-of-the-art performance on multiple datasets without any extra annotation. For example, the PSNR metric reaches 29.04 on the nuScenes dataset. Moreover, our automated labeling can significantly improve the performance of 2D/3D detection tasks.

  PublisherOA PDFDOI

• An Incentive-Based Decision Support System for Sustainable Delivery Scheduling

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Fast Consensus over Almost Regular Directed Graphs

  ArXiv.org · 2025-07-17

  preprintOpen accessSenior author

  This paper studies an open consensus network design problem: identifying the optimal simple directed graphs, given a fixed number of vertices and arcs, that maximize the second smallest real part of all Laplacian eigenvalues, referred to as algebraic connectivity. For sparse and dense graphs, the class of all optimal directed graphs that maximize algebraic connectivity is theoretically identified, leading to the fastest consensus. For general graphs, a computationally efficient sequence of almost regular directed graphs is proposed to achieve fast consensus, with algebraic connectivity close to the optimal value.

  PublisherOA PDFDOI

• State Dependent Optimization with Quantum Circuit Cutting

  2025-07-06

  preprintOpen access

  Quantum circuits can be reduced through optimization to better fit the constraints of quantum hardware. One such method, initial-state dependent optimization (ISDO), reduces gate count by leveraging knowledge of the input quantum states. Surprisingly, we found that ISDO is broadly applicable to the downstream circuits produced by circuit cutting. Circuit cutting also requires measuring upstream qubits and has some flexibility of selection observables to do reconstruction. Therefore, we propose a state-dependent optimization (SDO) framework that incorporates ISDO, our newly proposed measure-state dependent optimization (MSDO), and a biased observable selection strategy. Building on the strengths of the SDO framework and recognizing the scalability challenges of circuit cutting, we propose nonseparate circuit cutting-a more flexible approach that enables optimizing gates without fully separating them. We validate our methods on noisy simulations of QAOA, QFT, and BV circuits. Results show that our approach consistently mitigates noise and improves overall circuit performance, demonstrating its promise for enhancing quantum algorithm execution on near-term hardware.

  PublisherOA PDFDOI

• EC-FST: A novel pipeline for automatically analyzing mouse forced swim test

  Journal of Neuroscience Methods · 2025-09-18

  articleSenior authorCorresponding
  PublisherDOI

• HOPPS: Hardware-Aware Optimal Phase Polynomial Synthesis with Blockwise Optimization for Quantum Circuits

  2025-12-17

  article

  Blocks composed of {CNOT, R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf>} are ubiquitous in modern quantum applications, notably in circuits such as QAOA ansatzes and quantum adders. After compilation, many of them exhibit large CNOT counts or depths, which lowers fidelity. Therefore, we introduce HOPPS: a SAT-based hardware-aware optimal phase polynomial synthesis algorithm that could generate {CNOT, R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf>} blocks with CNOT count or depth optimality. Sometime {CNOT, R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</inf>} blocks are large, such as in QAOA ansatzes, HOPPS's pursuit of optimality limits its scalability. To address this issue, we introduce an iterative blockwise optimization strategy: large circuits are partitioned into smaller blocks, each block is optimally refined, and the process is repeated for several iterations. Empirical results show that HOPPS is more efficient comparing with existing near-optimal synthesis tools. Used as a peephole optimizer, HOPPS reduces the CNOT count by up to 50.0 % and the CNOT depth by up to 57.1 % under OLSQ. For large QAOA circuit, after mapping by Qiskit, circuit can be reduced CNOT count and depth by up to 44.4 % and 42.4 % by our iterative blockwise optimization.

  PublisherDOI

• Fast Consensus over Almost Regular Directed Graphs^*

  2025-07-08

  articleSenior author

  This paper studies an open consensus network design problem: identifying the optimal simple directed graphs, given a fixed number of vertices and arcs, that maximize the second smallest real part of all Laplacian eigenvalues, referred to as algebraic connectivity. For sparse and dense graphs, the class of all optimal directed graphs that maximize algebraic connectivity is theoretically identified, leading to the fastest consensus. For general graphs, a computationally efficient sequence of almost regular directed graphs is proposed to achieve fast consensus, with algebraic connectivity close to the optimal value.

  PublisherDOI

Recent grants

• III: Small: Distributed Reinforcement Learning over Complex Networks

  NSF · $600k · 2022–2026

Frequent coauthors

• Tamer Başar

  81 shared

• Brian D. O. Anderson

  Australian National University

  39 shared

• A. Stephen Morse

  32 shared

• Mengbin Ye

  23 shared

• Yixuan Lin

  Beijing University of Chinese Medicine

  20 shared

• Esther Pacitti

  Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier

  19 shared

• Philip E. Paré

  19 shared

• Xudong Chen

  17 shared

Labs

• Ji Liu GroupPI