3 citations · 3 across the 3 of their papers we have counts for
6 papers
Sensorimotor World Models: Perception for Action via Inverse Dynamics
Petr Ivashkov, Randall Balestriero, Bernhard Schölkopf
Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions. At the same time, latent JEPA-st…
Learning Arbitrary Lindbladians with Quantum Error Correction
Nikita Romanov, Petr Ivashkov, Weiyuan Gong +4
We study ansatz-free Lindbladian learning, the problem of reconstructing the generator of an open quantum system without prior knowledge of its Hamiltonian or dissipator structures…
QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
Petr Ivashkov, Po-Wei Huang, Kelvin Koor +2
We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compos…
Ansatz-Free Learning of Lindbladian Dynamics In Situ
Petr Ivashkov, Nikita Romanov, Weiyuan Gong +3
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust si…
From quantum-enhanced to quantum-inspired Monte Carlo
Johannes Christmann, Petr Ivashkov, Mattia Chiurco +1
We perform a comprehensive analysis of the quantum-enhanced Monte Carlo method [Nature, 619, 282-287 (2023)], aimed at identifying the optimal working point of the algorithm. We ob…
Linearization Scheme of Shallow Water Equations for Quantum Algorithms
Till Appel, Zofia Binczyk, Francesco Conoscenti +4
Computational fluid dynamics lies at the heart of many issues in science and engineering, but solving the associated partial differential equations remains computationally demandin…