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Abstract
Quantum computing has made remarkable progress in recent years, yet several challenges— such as limited qubit resources, low fidelity, and error-prone operations—still hinder its practical implementation. My Ph.D. research focuses on bridging the gap between high-level quantum algorithms and low-level hardware through three key projects that advance quantum compilation and error mitigation.
First, I developed CaQR, a compiler-assisted framework that enables qubit reuse through mid-circuit measurement and reset. This approach alleviates resource constraints, reduces qubit-swapping overhead, and improves fidelity on real quantum devices by up to 20%.
Second, I introduced AutoBraid, a compiler framework for surface code error correction. This work provides efficient support for fault-tolerant quantum computation, significantly reducing the complexity of logical qubit mapping and operations.
Finally, I contributed to the development of QASMTrans, an open-source quantum compiler that supports scalable quantum approximate optimization algorithms (QAOA) and other applications. This tool has demonstrated practical performance improvements in diverse quantum architectures, including trapped-ion and superconducting qubits.
My research addresses core challenges in quantum computing, offering solutions that span algorithm optimization, error correction, and hardware-agnostic compilation. These contributions enhance the viability and scalability of quantum systems, paving the way for broader adoption and impactful applications.