In the realm of nuclear physics and energy research, a trio of scientists from the Chinese Academy of Sciences has developed a novel algorithm that could significantly improve the accuracy of nuclear mass predictions. Kaizhong Tan, Jian Liu, and Chuan Wang have introduced a quantum-inspired Bayesian probability (QIBP) algorithm that leverages quantum dynamics to enhance theoretical models of nuclear mass. Their work, published in the journal Physical Review C, offers promising applications for the energy sector, particularly in nuclear power and related technologies.
The researchers’ algorithm addresses a longstanding challenge in nuclear physics: the accurate prediction of nuclear mass. Current theoretical models often fall short in capturing the intricate quantum effects and subtle patterns that influence nuclear mass. The QIBP algorithm tackles this issue by mapping the residuals—differences between theoretical and experimental mass values—into wave functions in Hilbert space. By solving the Schrödinger equation, the team derives the corresponding potentials and, assuming a Boltzmann distribution for the residuals, obtains the prior and likelihood probability density functions (PDFs). Applying Bayesian theorem, the algorithm then estimates the target nuclear mass residuals.
Global optimization and extrapolation analyses demonstrate that the QIBP algorithm effectively captures the quantum effects and subtle patterns that theoretical models often overlook. This enhanced accuracy is crucial for the energy industry, particularly in nuclear power generation, where precise knowledge of nuclear masses is essential for reactor design, fuel efficiency, and safety protocols. The algorithm’s ability to extrapolate across the entire nuclear chart further underscores its potential for widespread application in nuclear research and development.
In addition to predicting nuclear masses, the QIBP algorithm has been applied to forecast α-decay energies of radium (Ra) and einsteinium (Es) isotopes. The study also analyzes the shell effects manifested in these isotopes, providing deeper insights into nuclear structure and behavior. This research not only validates the feasibility of quantum machine learning in nuclear mass research but also paves the way for more accurate and reliable nuclear data, which is vital for advancing nuclear energy technologies.
The practical implications for the energy sector are substantial. Accurate nuclear mass predictions can lead to more efficient and safer nuclear reactors, improved fuel cycle management, and better understanding of nuclear waste disposal. As the global push for clean energy intensifies, innovations like the QIBP algorithm are instrumental in harnessing the full potential of nuclear power while minimizing risks and optimizing performance.
In summary, the work of Tan, Liu, and Wang represents a significant step forward in the field of nuclear physics, with direct relevance to the energy industry. Their quantum-inspired Bayesian probability algorithm offers a powerful tool for enhancing the accuracy of nuclear mass predictions, ultimately contributing to the development of more advanced and sustainable nuclear energy technologies.
This article is based on research available at arXiv.