We come from different fields, follow different questions, and meet at the same frontier where physics, mathematics, computation, and information begin to overlap.
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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.
Aaron Kim·
Read the articleThe 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.
Aaron Kim·
Read the articleNothing else in the archive yet — the featured pieces above are the full set.
PhD in Quantum Science and Engineering · Founder of QISCA
Harvard
Tensor networks · Lindbladian dynamics · Quantum many-body physics
PhD Graduate Student in Physics
Princeton
Ultracold molecules · Quantum simulation · Quantum information
PhD in Applied Mathematics
Caltech
Quantum algorithms for scientific computing · Quantum simulation · Quantum information theory
Graduate Student in Artificial Intelligence
Penn
Quantum algorithms · OptimizationQuantum machine learning
