This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A new study has demonstrated that it is possible to make a network of atoms and photons that could improve how artificial intelligence stores and recalls memories. This network, called a quantum-optical spin glass, works as an associative memory, a form of AI that enables the recall of full memories from partial information—much like how humans can recognize a person's face in a blurred photograph.
The advance, published in Science, shows that this new type of spin glass has a greater capacity to hold and recall memories than a traditional AI network of the same size. The atom-and-photon network also exhibited short-term plasticity, a phenomenon that resembles how synaptic connections between neurons in the brain change when learning new information. "We can now make neural networks at the atomic level, and they adjust themselves in a way that is somewhat similar to how we believe our brains learn," said Benjamin Lev, the study's senior author and the Stanford Fortitude Professor and professor of physics and applied physics in the School of Humanities and Sciences.
The new spin glass was made with atoms at extremely cold temperatures trapped in a vacuum chamber, so more research needs to be done to see if it can be scaled for practical applications—but with further development, it has the potential for an even larger memory capacity. The advance helps illuminate how certain spin glasses function at an atomic level, which physicists have been working to understand for decades. A spin glass is a type of frustrated magnet.
The "spin" refers to the two states in which atoms can orient, just as a bar magnet can orient with its north pole pointing up or down. Within a common refrigerator magnet, all the spins align in the same direction, all up or all down, attaching it to the refrigerator door. By contrast, the spins in a spin glass are frustrated: they get stuck pointing in random directions.
This is somewhat akin to everyday glass, which looks and behaves like a solid but has atoms in disordered locations like a fluid. In 1982, physicist John Hopfield used the properties of frustrated spins in a mathematical model to show that a network of spins can store and recall information in the form of memory patterns. Low-energy arrangements of spins called valleys contain full memories.
If the network receives incomplete or distorted information, it can "associate" the corrupted information with the most similar stored memory, thereby recalling the full memory. Hopfield shared the 2024 Nobel Prize in Physics for this work, which helped stimulate the neural-network research that underpins how AI computing systems such as ChatGPT process and understand language. However, the Hopfield network only works up to a certain point: When it contains too many memories, recall fails because the network transitions into a spin glass state, with frustrated spins creating an energy landscape that is too cluttered to accurately recall the correct memories.
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