AI
Artificial Intelligence
Efficient and robust AI, model compression, LLM and multi-agent systems, and practical intelligent computing.
Academic Homepage
Shuai Xu, Ph.D.
Associate Professor
Department of Computer and Data Sciences
Case Western Reserve University
Artificial Intelligence · Quantum Computing · Theoretical Computer Science
I develop efficient, reliable, and theoretically grounded computational methods, with particular interests in artificial intelligence, quantum algorithms and systems, and the mathematical foundations of computation.
Exploring the frontiers of computation, from intelligent algorithms to quantum systems.
Research
Research spanning artificial intelligence, quantum computing, and theoretical computer science.
Q
Quantum Computing
Quantum circuit optimization and cutting, quantum algorithms, hardware-aware compilation, and scalable quantum systems.
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Theoretical Computer Science
Algorithms, coding-based computation, optimization, and mathematical foundations of computing.
Selected Publications
Representative work across my three primary research directions. Figures are drawn from the papers themselves.
Artificial Intelligence · CVPR 2025
Flexible Group Count Enables Hassle-Free Structured Pruning
Efficient structured pruning through flexible group counts and deployment-friendly grouped convolutions.
Paper ↗
Artificial Intelligence · 2026
Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems
A controlled, MAS-demanding benchmark for understanding whether reported efficiency gains are genuinely structural.
arXiv ↗
Quantum Computing · HPDC 2026
Scalable Quantum Circuit Simulation via Circuit Cutting and FPGA Acceleration
Algorithm-hardware co-design for scalable cut-based quantum simulation with FPGA acceleration.
Paper ↗
Quantum Computing · 2025
HOPPS: Hardware-Aware Optimal Phase Polynomial Synthesis
SAT-based hardware-aware synthesis with iterative blockwise optimization for reducing CNOT count and depth.
arXiv ↗
Quantum Computing · 2023
Efficient Quantum Circuit Cutting by Neglecting Basis Elements
Golden cutting points reduce quantum circuit cutting cost while preserving reconstruction accuracy.
arXiv ↗
Theoretical Computer Science · TCS 2019
A New Coding-based Algorithm for Finding Closest Pair of Vectors
Error-correcting codes provide randomized and deterministic algorithms for the Closest Pair and Light Bulb problems.
Paper ↗Recent Highlights
2026
Research on scalable quantum circuit simulation, modular quantum systems, and LLM-based multi-agent efficiency.
2025–26
Received the KF-SCIS Outstanding Alumni Award.
2025
Received the J. Bruce Jackson, M.D., Award for Excellence in Undergraduate Mentoring.
Research Community
View photo →My research is highly collaborative, bringing together students and colleagues across artificial intelligence, quantum computing, algorithms, and interdisciplinary computing.
