Quantum Software Stack Development

Quantum software stack development is fundamental to enabling scalable and efficient quantum computing. We focus on building a layered architecture that bridges high-level quantum algorithms and low-level hardware instructions. By integrating advanced compiler technologies, simulation acceleration technologies, and error-aware optimizations, we aim to build a robust, modular software stack that maximizes the capabilities of quantum hardware while minimizing the impact of noise. This work lays the foundation for dependable and high-performance quantum systems.

Quantum Error Mitigation and Correction

Our research enhances quantum reliability by bridging the gap between the NISQ and FTQC eras. We develop compiler-based optimizations and specialized architectures to mitigate noise in current NISQ machines, ensuring higher computational accuracy. Simultaneously, we advance fault-tolerant systems by developing high-efficiency decoders and exploring magic-state distillation and cultivation. By optimizing these Quantum Error Correction (QEC) technologies, our work establishes the essential framework for scalable and stable large-scale quantum computing.

Quantum Circuit Simulation Acceleration

To mitigate the exponential overhead of vector-matrix multiplication in large-scale quantum simulations, we investigate acceleration techniques that leverage near- and in-memory computing based on emerging memory technologies. We also explore circuit-knitting methods to decompose large circuits into smaller, manageable subcircuits, effectively bypassing the memory limitations of classical hardware. By combining these hardware-driven optimizations with hybrid classical-quantum algorithms, we aim to significantly extend the boundaries of large-scale quantum circuit simulation.

Quantum Artificial Intelligence (Quantum AI)

Quantum AI is an exciting and innovative research field that integrates quantum computing and artificial intelligence to advance the capabilities of next-generation AI systems. Quantum AI has the potential to significantly improve the efficiency and effectiveness of AI algorithms by leveraging quantum computational advantages. We conduct research using compiler technology to optimize performance and minimize errors in Quantum AI applications across fields such as drug discovery, finance, and optimization.