A research team has demonstrated that overparametrization improves performance in quantum machine learning, a technique that surpasses the capabilities of classical computers. Their research offers insights for optimizing the training process in quantum neural networks, allowing for enhanced performance in practical quantum applications.
Machine learning usually involves training neural networks to process information and learn how to solve a given task. During the training phase, the algorithm updates the parameters of the neural network to find their optimal setting. Overparametrization, a concept in classical machine learning, adds more parameters to prevent the algorithm from stalling out.
The implications of overparametrization in quantum machine learning models were poorly understood until now. The research team from Los Alamos National Laboratory establishes a theoretical framework for predicting the critical number of parameters at which a quantum machine learning model becomes overparametrized. Adding parameters at this critical point prompts a leap in network performance and makes the model significantly easier to train.
By leveraging aspects of quantum mechanics like entanglement and superposition, quantum machine learning holds the promise of much greater speed and quantum advantage over classical computers.
The research team’s findings can be illustrated through a thought experiment. Imagine a hiker searching for the tallest mountain in a dark landscape. The number of parameters in the model corresponds to the directions available for the hiker to move. With too few parameters, the hiker may mistake a small hill for the tallest mountain or get stuck in a flat region. But as the number of parameters increases, the hiker can explore more directions in higher dimensions, avoiding traps and finding the true peak.
This breakthrough in quantum machine learning has significant implications for various applications, such as classifying different phases of matter in quantum materials research. It opens up possibilities for optimizing the training process and achieving enhanced performance in practical quantum applications.
The research study, titled “Theory of overparametrization in quantum neural networks,” was conducted by a team of researchers from Los Alamos National Laboratory. The study was funded by LDRD at Los Alamos National Laboratory.