Quantum computers promise to address problems beyond the capabilities of classical computing; however, current and near-term quantum computers are noisy and prone to errors, limiting their reliability, applicability, and scalability. 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 convergence of quantum computing, Artificial Intelligence (AI), and Machine Learning (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 fidelity of noisy quantum hardware, leveraging quantum-ML hybrid systems to tackle practical problems, and developing advanced quantum and classical-quantum hybrid optimization techniques.
