Doctoral Thesis Proposal - Zikun Li
November 9, 2026 3:00PM—4:30PM
Location:
8102
-
Gates and Hillman Centers
Speaker:
ZIKUN LI,
Ph.D. Student, Computer Science Department, Carnegie Mellon University
https://zikun-li.github.io/
Emerging applications such as quantum computing and large language model (LLM) serving depend on compilers and runtime systems to execute efficiently, yet the inputs to their optimization decisions are rarely uniform. Quantum devices support different gate sets and exhibit unequal communication costs, concurrent LLM requests carry different latency service-level objectives (SLOs), and model operators differ in their resource demands and in how well they match available GPUs.
This thesis argues that representing such heterogeneity explicitly in optimization decisions enables substantially more efficient execution, provided that the resulting search and coordination costs are kept tractable. Three completed projects support this claim. Quarl uses reinforcement learning to optimize quantum circuits across gate sets, reducing CNOT count by 21.3% on average on the IBM gate set, compared with 7.7% for the best existing optimizers. QBX compiles 2-local Hamiltonian simulation for quantum chiplets by exploiting Hamiltonian structure and nonuniform chiplet communication, reducing circuit depth on average by 2.57× over Qiskit and 10.35× over tket.
AdaServe customizes speculative decoding to individual request latency objectives, reducing SLO violations by up to 4.3× and improving goodput by up to 1.9×. The proposed work will jointly choose operator boundaries and deployment configurations for LLM serving on heterogeneous GPUs, and will characterize when operator disaggregation reduces serving cost and when its overheads outweigh the benefits.
Thesis Committee:
Zhihao Jia (Chair)
Tianqi Chen
Phillip B. Gibbons
Juncheng Yang (Harvard University)
Contact
Matt Stewart