Doctoral Thesis Oral Defense
March 16, 2026 9:30AM—11:00AM
Location:
In Person and Virtual - ET
-
Reddy Conference Room, Gates Hillman 4405 and Zoom
Speaker:
MINGKUAN XU,
Ph.D. Candidate
Computer Science Department
Carnegie Mellon University
https://mingkuan.taichi.graphics/
Optimizing and simulating quantum circuits at scale are critical bottlenecks in quantum computing. This thesis delivers a suite of tools to improve them.
For quantum circuit optimization, we first automate the discovery and verification of transformation rules by introducing Equivalence Circuit Class (ECC) sets and an efficient generation algorithm for arbitrary gate sets. We then utilize the generated rules in the superoptimizer Quartz, which optimizes circuits using a cost-based backtracking search. Compared to previous rule-based methods, Quartz successfully escapes local minima through exhaustive search. To further improve efficiency and avoid the exponential runtime penalties of pure search, we introduce QALM. This hybrid optimizer combines exhaustive search with rule-based rewriting. By interleaving bounded search-based exploration with greedy rule-based exploitation, QALM escapes local minima dynamically. It outperforms existing search-based optimizers in optimization quality and matches reinforcement learning methods without the training overhead.
While the prior two approaches aim for global optimization, this problem is intrinsically QMA-hard, creating a bottleneck for large programs. To circumvent this issue and scale up, we introduce OAC, a cut-and-meld circuit optimization algorithm. OAC cuts a circuit into subcircuits, applies an existing oracle optimizer independently, and seamlessly melds the results. This approach operates with a linear number of oracle calls while attaining local optimality. Empirical evaluation shows that OAC improves the efficiency of state-of-the-art optimizers by over an order of magnitude while enhancing overall quality.
Beyond physical execution, the scalable simulation of quantum circuits on classical hardware presents another major challenge. We present Atlas, a distributed GPU-based simulator that hierarchically partitions circuits to exploit data parallelism while minimizing communication. By using integer linear programming to allocate structurally related gates to nearby GPUs and dynamic programming for kernel scheduling, Atlas runs over 2x faster than prior state-of-the-art GPU simulators.
Together, these frameworks provide a robust toolchain, improving both the execution of quantum circuits on physical devices and their scalable classical simulation.
Thesis Committee
Zhihao Jia (Co-Chair)
Umut A. Acar (Co-Chair)
Ryan O'Donnell
Yongshan Ding (Yale University)
In Person and Zoom Participation. See announcement.
For More Information:
matthewstewart@cmu.edu