7 papers
naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement
Hankyeol Kim, Pilsung Kang
Physics-Informed Neural Networks (PINNs) are effective methods for solving inverse problems and discovering governing equations from observational data. However, their performance…
Barren Plateaus as Destructive Interference: A Diagnostic Framework and Implications for Structured Ansatzes
Pilsung Kang
Barren plateaus (BPs) are usually described by the exponential suppression of gradient variance, but the mechanism by which gradient signal disappears remains unclear. We show that…
Implementing Pearl's -Calculus on Quantum Circuits: A Simpson-Type Case Study on NISQ Hardware
Pilsung Kang
Distinguishing correlation from causation is a central challenge in machine intelligence, and Pearl's -calculus provides a rigorous symbolic framework for reasoning a…
COUNTDOWN: Contextually Sparse Activation Filtering Out Unnecessary Weights in Down Projection
Jaewon Cheon, Pilsung Kang
The growing size of large language models has created significant computational inefficiencies. To address this challenge, sparse activation methods selectively deactivates non-ess…
Quantum Entanglement as Super-Confounding: From Bell's Theorem to Robust Machine Learning
Pilsung Kang
Bell's theorem reveals a profound conflict between quantum mechanics and local realism, a conflict we reinterpret through the modern lens of causal inference. We propose and comput…
Emergent Bifurcations in Quantum Circuit Stability from Hidden Parameter Statistics
Pilsung Kang
The compression of quantum circuits is a foundational challenge for near-term quantum computing, yet the principles governing circuit stability remain poorly understood. We investi…