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The research was conducted at the Danish National Research Foundation’s “Center of Excellence for Hybrid Quantum Networks (Hy-Q)” and is a collaboration between Ruhr University Bochum in Germany and the University of Copenhagen’s Niels Bohr Institute.

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Hot carrier solar cells, a concept introduced several decades ago, have long been seen as a potential breakthrough in solar energy technology. These cells could surpass the Shockley–Queisser efficiency limit, which is a theoretical maximum efficiency for single-junction solar cells. Despite their promise, practical implementation has faced significant challenges, particularly in managing the rapid extraction of hot electrons across material interfaces.

Researchers at Ludwig-Maximilians-Universität, Max-Planck-Institut für Quantenoptik, Munich Center for Quantum Science and Technology (MCQST) and the University of Massachusetts recently carried out a study investigating the equilibrium fluctuations in large quantum systems. Their paper, published in Nature Physics, outlines the results of large-scale quantum simulations performed using a quantum gas microscope, an experimental tool used to image and manipulate individual atoms in ultracold atomic gases.

The large language models that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”

But, if a researcher needs to do a specialized task that a machine could do more efficiently and they don’t have access to a large institution that offers access to generative AI tools, what other options are available? Say, a parent wants to prep their child for a difficult test and needs to show many examples of how to solve complicated math problems.

Building their own LLM is an onerous prospect for costs mentioned above, and making direct use of the big models like GPT-4 and Llama 3.1 might not immediately be suited for the complex in logic and math their task requires.