Research

The convergence of quantum computing, AI, and ML can revolutionize science, technology, the economy, and society as a whole. I explore a wide array of opportunities within quantum intelligence, including investigating AI/ML to enhance the reliability of noisy quantum hardware, leveraging quantum-ML hybrid systems to tackle practical problems, and exploring potentials for quantum advantage.

Quantum computers can tackle problems beyond the capabilities of classical supercomputers; however, current and near-term quantum devices are noisy and prone to errors, limiting their reliability, applicability, and scalability. Addressing these limitations requires device-level enhancements that may span generations of quantum hardware. Therefore, software techniques to improve the reliability of quantum hardware are an important area of research. In my research, I focus on full-stack optimization for both digital and analog quantum computers, addressing every layer—from the application, through programming languages and compilers, to error suppression, mitigation, and correction. I am also interested in fault-tolerant quantum computing and am exploring opportunities in real-time decoding and non-conventional error-correcting techniques for various types of quantum computers.

The vast potential of quantum technology incentivizes adversaries to target quantum programs, aiming to extract sensitive data and intellectual property. This threatens both the privacy and security of quantum applications.
My objective is to devise techniques that safeguard user privacy and ensure the integrity of quantum programs when executed on untrusted quantum systems.

Optimization is a cornerstone in solving many complex problems in science and industry. Quantum computers and optimization accelerators, such as quantum annealers, promise to significantly advance this field by addressing challenges beyond the scope of classical methods. My research aims to develop innovative optimization techniques—encompassing classical, quantum, and hybrid approaches—that achieve higher quality solutions in less time and with greater energy efficiency.

Identifying quantum-friendly problems is crucial for demonstrating quantum speedup. I aim to explore leveraging near-term quantum systems and quantum machine learning models to tackle real-world challenges in areas such as AI/ML, Natural Language Processing (NLP), computer vision, security, drug discovery, climate studies, and more.