"Quantum Artificial Intelligence Poised for Performance Leap? New Technological Breakthroughs Propel Machine Learning into New Frontiers..."
According to reports, a groundbreaking theoretical proof from the Los Alamos National Laboratory (LANL) in the United States indicates that the use of a technique called "overparametrization" can enhance the performance of quantum machine learning, thereby challenging tasks traditionally performed by classical computers.
"Overparametrization" is a technique used in artificial intelligence and machine learning that enhances model performance and training dynamics by introducing more parameters or variables than strictly necessary. In this approach, the number of parameters in a model is intentionally set to be much greater than the available training samples or data dimensions. Despite the counterintuitive aspect of having more parameters than required, overparametrization has been found to offer several benefits in training complex machine learning models.
Reportedly, "overparametrization" can help models adapt to variations and complexities present in the data, potentially improving generalization performance when encountering new, unseen data. Essentially, overparametrization provides the model with more degrees of freedom to learn and represent underlying relationships in the data. Therefore, one of the key advantages of this technique is that it helps avoid getting stuck in suboptimal solutions during the optimization process.
However, so far, little is known about "overparametrization" in quantum machine learning models. In their latest research, the LANL team established a theoretical framework to predict the critical number of parameters for overparametrization in quantum machine learning models. At a certain critical point, increasing the parameters leads to a performance leap in the network, making the model significantly easier to train.
Martin Larocca, a researcher at LANL and the lead author of the paper, explained, "By establishing a theoretical understanding of overparametrization in quantum neural networks, our research paves the way for optimizing the training process and achieving enhanced performance in practical quantum applications."
The recent research findings have been published in the journal "Nature Computational Science."
To illustrate their discovery, the researchers described a thought experiment in which a hiker seeks the highest peak in a dark environment, representing the training process. The hiker can only move in specific directions and assess their progress by using a limited GPS system to measure altitude.
In this analogy, the number of parameters in the model corresponds to the directions the hiker can move. "One parameter allows forward and backward movement, two parameters allow lateral movement, and so on. Unlike the world of our assumed hiker, the data landscape may have more than three dimensions," they explained.
With too few parameters, the hiker cannot fully explore and might mistake a small hill for the highest peak or get stuck in flat areas where every step seems futile. However, as the number of parameters increases, the hiker can move in more directions across higher dimensions. With additional parameters, the hiker can avoid being trapped and find the true peak or solution to the problem.
Diego Garcia-Martin, a researcher at LANL and co-author of the research paper, stated, "In conclusion, we believe that our findings will be highly useful for using machine learning to understand properties of quantum data, such as classifying different phases in quantum material research, which is very challenging on classical computers."