Abhishek Karna
PhD Student in Theoretical Physics
Princeton University
I'm a PhD student in Theoretical Physics at Princeton, working on quantum condensed matter physics. My current research applies machine learning to quantum materials. I use it to classify, predict, and screen for properties that are hard to compute from first principles alone.
Before Princeton, I spent four years at Duke doing a bit of everything: building new Quantum Monte Carlo algorithms for quantum spin systems, growing single crystals in a furnace, and modeling pollution in the Indo-Gangetic region. I graduated with a Bachelor of Science in Physics, a Bachelor of Science in Mathematics, and a Certificate in Philosophy, Politics, and Economics (PPE). I also wrote a poetry collection about fungi and documented temples across South Asia and Southeast Asia. The common thread, if there is one, is a preference for questions that resist clean disciplinary labels.
Projected Density Matrix Sampling for Lattice Hamiltonians
We present a continuous-time path-integral Monte Carlo method for computing the low-lying spectrum of generic quantum Hamiltonians within a projection subspace. The method is free of Trotter discretization errors and converges systematically to low-energy states with finite overlap with the projection subspace as β increases. Applied to the 1D Ising model near the E₈ critical point and the frustrated Shastry–Sutherland model, demonstrating quantum Monte Carlo spectroscopy for lattice Hamiltonians.