Type search term(s) and press enter

Popular Searches

Research Program

I am been deeply captivated by the idea that quantum information offers new ways to store and process data. Since my undergraduate years, I’ve dedicated myself to understanding and contributing to the technological future promised by quantum technology.

Over two decades ago, the field was much smaller than today’s multi-billion dollar quantum tech industry. I’ve always believed this technology would be transformative for humanity, yet significant near-term challenges remain for the quantum industry to reach its full potential. Despite the growth and focus of industrial research and the investment from public and private sponsors, certain obstacles are best addressed by a university research group.

My approach is highly theoretical yet practically driven, which has given me a unique position in this emerging field. My work, often referred to as quantum applications or solutions science, integrates quantum application requirements with hardware constraints in innovative ways, reminiscent of systems science or systems engineering. The core of my research seeks to bridge the gap between theory and applications, focusing on the software aspect of quantum computing platforms.

With rapid experimental advancements, we’re now only several years away from discovering if noisy quantum processors can outperform classical computation in practical tasks. The goal of this research program is to understand the limitations of these new quantum applications, combining theoretical physics, computer science, and numerics, offering a diverse range of topics for young scientists. If pre-fault tolerant quantum processors don’t surpass classical processors in practical tasks, our aim is to minimize the requirements for effective quantum information processing. We also adopt a long-term vision, with projects focusing on error mitigation and maximizing algorithmic output with minimal quantum hardware.

My current research agenda seeks to:

  1. Quantify the limitations of quantum optimization and machine learning.
  2. Determine best practices for processing quantum information in the presence of noise and for integrating error mitigation/correction.
  3. Develop theoretical foundations and propose experimental demonstrations of quantum information processing tasks.