Related papers: The QSE-reduced \alpha Network
Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. Conventional methods which probe these states through direct measurements and classically computed correlations…
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial…
Details of the explosion mechanism of core-collapse supernovae (CCSNe) are not yet fully understood. There is an increasing number of numerical examples by ab-initio core-collapse simulations leading to an explosion. Most, if not all, of…
In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated…
When the density of a nuclear system is decreased, homogeneous states undergo the so-called Mott transition towards clusterised states, e.g. alpha clustering, both in nuclei and in nuclear matter. Here we investigate such a quantum phase…
The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there…
The alpha-rich freezeout from equilibrium occurs during the core-collapse explosion of a massive star when the supernova shock wave passes through the Si-rich shell of the star. The nuclei are heated to high temperature and broken down into…
The process of alpha disintegration has been studied theoretically in the framework of a microscopic superasymmetric fission model (MSAFM). The nuclear interaction potential required for the alpha decay process has been calculated by…
Quantum computing has proven to be capable of accelerating many algorithms by performing tasks that classical computers cannot. Currently, Noisy Intermediate Scale Quantum (NISQ) machines struggle from scalability and noise issues to render…
The analysis of noisy quantum states prepared on current quantum computers is getting beyond the capabilities of classical computing. Quantum neural networks based on parametrized quantum circuits, measurements and feed-forward can process…
[Background] Despite the importance of light-ion fusion in nucleosynthesis, a limited amount of data exists regarding the de-excitation following fusion for such systems. [Purpose] To explore the characteristics of alpha emission associated…
Understanding the substructure of atomic nuclei, particularly the clustering of nucleons inside them, is essential for comprehending nuclear dynamics. Various cluster configurations can emerge depending on excitation energy, the number and…
We present a neural-network emulator for the thermal and chemical evolution in Population III star formation. The emulator accurately reproduces the thermochemical evolution over a wide density range spanning 21 orders of magnitude…
Reactions between light charged nuclei at sub-Coulomb energies are crucial in astrophysical environments, but accurate cross-section measurements are hindered by electron screening. Traditional methods, including polynomial extrapolation…
The small sub-barrier tunneling probability of nuclear processes can be dramatically enhanced by collision with incident charged particles. Semiclassical methods of theory of complex trajectories have been applied to nuclear tunneling, and…
The semiconductor industry faces a computational crisis in extreme ultraviolet (EUV) lithography optimization, where traditional methods consume billions of CPU hours while failing to achieve sub-nanometer precision. We present a…
In contrast to the classical optimization process required by the quantum approximate optimization algorithm, FALQON, a feedback-based algorithm for quantum optimization [A. B. Magann {\it et al.,} {\color{blue}Phys. Rev. Lett. {\bf129},…
Big-Bang Nucleosynthesis (BBN) predictions of primordial light-element abundances offer a powerful probe of early-Universe physics. However, high-accuracy numerical BBN calculations have become a major computational bottleneck for…
We introduce Qlustering, a quantum-inspired algorithm for unsupervised learning that leverages network-based quantum transport to perform data clustering. In contrast to traditional distance-based methods, Qlustering treats the steady-state…
We study the impact of the nuclear symmetry energy and its density dependence on the {\alpha}-decay process. Within the frame work of the performed cluster model and the energy density formalism, we use different parameterizations of the…