Graduate Student in Artificial Intelligence
PennSchool of Engineering and Applied ScienceMy research interests lie in quantum algorithms and quantum machine learning, particularly their applications to finance. I am interested in understanding how quantum methods can provide new approaches to modeling, optimization, and risk in complex financial systems. Here, I write about recent work in quantum computing and share notes and ideas from my own research.
Persistent homology can read structural change in a financial time series that a price chart alone does not show. We embed market data into point clouds, estimate Betti numbers with classical solvers and with quantum phase estimation, and show that spikes in β₀ line up with crash regimes — then train a simple LSTM on those topological features.
The quantum Fourier transform is the same classical Fourier transform, applied to amplitudes. On a periodic amplitude comb it piles constructive interference at multiples of N/r and cancels everywhere else — which is why period finding, and therefore Shor, can read a hidden period from a single measurement.
