Po-Wei Huang

Abstract Accurately computing the free energies of biological processes is a cornerstone of computer-aided drug design, but it is a daunting task. The need to sample vast conformational spaces and account for entropic contributions makes the estimation of binding free energies very expensive.

Abstract We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compositional structure of KAN and is based on block-encodings, constructed recursively from a single layer using quantum singular value transformation. We demonstrate the algorithmic utility of QKAN in two applications.

Lithium-ion batteries (LIBs) have emerged as a pivotal contender in the realm of electronic devices, particularly in the domains of new energy vehicles, portable electronic products, and energy storage power stations [1]. This prominence can be attributed to the exceptional attributes of LIBs, including their lightweight construction, high energy density, and commendable reusability. Nevertheless, as the energy density of LIBs increases, so too does the risk of thermal runaway.