Portrait of Zihan Chen
Let's go scarlet knights!
📍Piscataway, New Jersey

Zihan Chen 陈子涵 /ziːˈhɑːn/ ; /tʃɛn/

Office: CoRE 632, 96 Frelinghuysen Road, Piscataway, NJ 08854

I am a third-year Ph.D. student in Computer Science at Rutgers Logo Rutgers University, advised by Prof. Eddy Z. Zhang. I have obtained my M.Sc. in Computer Science from Rutgers University, and my B.E. in Telecommunication Engineering from NUIST logo Nanjing University of Information Science & Technology (Changwang School of Honors), through a joint program with UCAS logo University of Chinese Academy of Sciences.

Research Interest 💡

My research interests broadly span Computer Systems topics such as Computer Architecture, Compilers and High-Performance Computing.

Quantum Computing Logo Currently, I focus on building efficient and optimized compiler systems for end-to-end fault-tolerant quantum computing (FTQC) systems and applications.

AI Agent Logo I am also interested in developing an agentic AI workflow based on the standards of automation, verifiability, and scalability to facilitate automated research and scientific discovery.

News 📢

Publications 📚 ( / / )

Topics: Circuit Optimization / QC Programmability / (*/†: indicates equal contribution.)

Overview of the PhasePoly quantum circuit optimization framework
Leveraging Phase Polynomials for Quantum Circuit Optimization
Zihan Chen, Henry Chen, Yuwei Jin, Enhyeok Jang, Mingkuan Xu, Vannessa Chan, Won Woo Ro, Eddy Z. Zhang

[ISCA'26] [Software] [Slides] [QCE'25 Poster]

Overview of the Genesis compiler framework for hybrid CV-DV quantum computers
Genesis: A Compiler Framework for Hamiltonian Simulation on Hybrid CV-DV Quantum Computers
Zihan Chen*, Jiakang Li*, Minghao Guo*, Henry Chen, Zirui Li, Joel Bierman, Yipeng Huang, Huiyang Zhou, Yuan Liu, Eddy Z. Zhang

[ISCA'25] [Software] [Slides]

Talks and Presentations 🎤

  • [Talk] Leveraging Phase Polynomials for Quantum Circuit Optimization

    The 53rd International Symposium on Computer Architecture (ISCA 2026), Raleigh, NC. Jul 1, 2026. [Slides]

  • [Talk] Strategies and tools to compile CV-DV quantum circuits.

    CMSC 858Y: Quantum Computing Systems Seminar, University of Maryland. College Park, MD. Apr 29, 2026. [Slides]

    Hybrid Oscillator-Qubit Quantum Processors — Instruction Set Architecture, Abstract Machine Models, and Applications. ASPLOS'2026 Tutorial. Pittsburgh, PA. Mar 22, 2026. [Slides] [SIGARCH Highlight]

  • [Talk] Compiler framework on hybrid CV-DV quantum computers for Hamiltonian simulation.

    North Carolina State University CV-DV Group Meeting. Remote, May 21, 2025. [Slides]

Academic Service 🧾

Organizer of Rutgers QEC: Theory and Systems Reading Group

Weekly reading group on tutorial and papers related to Quantum Error Correction (QEC). Topics span from theoretical QEC, quantum compiler design optimized for QEC, to AI-driven approaches for QEC. There will be one presenter each week, followed by group discussion and Q&A. Hosted 37 invited talks by 23 speakers from 12 different academic institutions, spanning backgrounds in computer science, electrical engineering, applied physics, and mathematics (as of Apr 2026). Join us!

Journal Reviewer:
  • ACM Transactions on Quantum Computing
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Scientific Reports
Conference Reviewer:
  • QCE'25, '26
  • US-RSE'26
  • NetQSA'26
Artifact Evaluation Committee:
  • SOSP'25
  • MICRO'25

Work Experience 💼

  • W.J. Cody Associate, Argonne National Laboratory

    May 2026 - Aug 2026
    Working with Dr. Ji Liu on highly efficient and optimized compilation for end-to-end fault-tolerant quantum computing (FTQC) pipelines.

  • Teaching Assistant, Rutgers University

    Sep 2023 - May 2026

    • CS336: Principles of Information and Data Management (6 times)
    • ECE568: Software Engineering of Web Applications (1 time)
    • ECE518: Mobile Embedded Systems and On-Device AI (1 time)

  • Software Engineer Intern, CARINA AI

    May 2023 - May 2024
    Developed a radiology AI solution and integrated Large Language Models (LLM) into healthcare applications. These products are now deployed in leading hospitals and research institutions globally, with some commercially available and others still in the experimental phase.