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Nuclear physics is a foundation of modern science, providing critical insights into the fundamental structure of matter, nuclear reactions, and processes governing the universe's evolution. However, the field is characterized by immense computational challenges due to the inherently complex nature of nuclear systems \cite{national2001physics}. Modeling nuclear structure involves solving many-body quantum systems, where interactions between protons and neutrons are governed by intricate forces that require high-dimensional calculations. Similarly, nuclear reactions, such as fusion and fission, require accurate simulations of reaction mechanisms and fragment distributions, which are computationally intensive because of the nonlinear and stochastic nature of these processes.
In parallel, AI uses data-driven techniques to support nuclear physics research. When attempting to decipher experimental data or optimize quantum circuits, as well as when relating nuclear properties, machine learning's great abilities to find patterns in the data set, make predictions, analyze data, and optimize processes add a whole new dimension to what has been previously possible due to advancements in nuclear physics.
Modeling nuclear interactions and many-body systems with quantum computing offers the potential to transform advances in nuclear physics by resolving computer-related issues. In contrast to classical computers, quantum computers are unique in that they can represent and process exponentially bigger state spaces by utilizing features like superposition and entanglement. As a result, quantum computers are superior at simulating intricate nuclear systems.
The Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) are important quantum algorithms in this field. By variationally estimating eigenvalues through quantum circuits, VQE is ideally suited to the task of determining the ground-state energy of quantum systems. This is especially pertinent to the research of nuclear stability and structure. QPE enhances this by facilitating accurate eigenvalue computations, which makes it indispensable for modeling dynamic nuclear processes like decay or particle scattering.
Since it requires a lot of resources, it poses a very difficult problem when it comes to scalability and approximation errors. Therefore, classical models would only be able to handle an exponential rise in variables with more nucleons, which would be useless for simulating heavy nuclei or many-body interactions. Losing accuracy in some descriptions, particularly in the context of exotic nuclei or extreme conditions, is a price paid for the simplicity of such computing. High-resolution classical simulations usually require supercomputing infrastructure and use enormous amounts of memory and processing resources.
The direct ability to simulate the quantum system is how quantum computing gets beyond these restrictions. It can handle the many-body wave function without the need for any approximation, and it can describe nuclear states as accurately as has ever been achieved. This makes it easier to effectively exploit computationally intractable challenges, such as simulating extremely extreme astrophysical occurrences under circumstances that surpass the physical capabilities of nuclear reactors or modeling rare isotopes.
AI methodologies complement quantum workflows by optimizing computational performance and extracting meaningful insights from experimental data. Machine learning models efficiently identify patterns in reaction pathways, refine quantum circuit parameters, and process large datasets from nuclear experiments. This integration enhances the accuracy, speed, and efficiency of nuclear physics research, enabling a deeper understanding of both fundamental processes and practical applications.
The integration of quantum computing with AI changes nuclear physics methodologies from the inside and allows breakthrough achievements in nuclear structure analysis, reaction modeling, and astrophysical simulation. It fast-tracks theory discovery with enhancing experimental capabilities; hence, the overall synergy unlocks transformations in applications including energy production, isotope creation, and research innovation in the nuclear field.
This work discusses the interplay between quantum computing and artificial intelligence (AI) in enhancing simulations and analyses in nuclear physics. Quantum computing overcomes the shortcomings of classical approaches in modeling complex nuclear systems by directly simulating quantum many-body interactions. High-precision calculations of nuclear structures, interactions, and reactions can be obtained by algorithms like the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE), without facing the scaling issues and errors present in the classical approach. Such developments have particular importance in studying heavy nuclei and extreme astrophysical phenomena.
