3 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
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…