Yuanhang Zhang

Boosting AI with physics, and understanding physics with AI.

Welcome!

I am a PhD candidate in physics at the University of California, San Diego, working at the intersection of physics, machine learning and unconventional computing.

While computing is a mathematical concept, realizing it requires physics. Sure, modern computers are cool with CMOS technology, but physics has so much more than standard CMOS electronics.

As an example, quantum entanglement correlates all qubits within a quantum computer, enabling us to process complex problems with incredible speed. For non-quantum systems with long-range order, we can also harness correlations among distant units to achieve unprecedented parallelism. This line of thought leads to the idea of MemComputing. (For more information, see this book and this review paper!)

Machine learning adds another layer of intrigue to this landscape. With physics and novel computing paradigms, we can better understand and accelerate AI, while the power of AI allows us to develop even faster algorithms.

With the recent surge of large language models, the possibilities are endless. What can we learn from a foundation model for physics? And will physical principles help us along the journey towards artificial general intelligence? The answers might be closer than we think.

Note: If you’re checking out my published work, my name is spelled “Yuan-Hang Zhang” there.