Can Quantum Computing help improve our ability to train coding language models (LLM) of large neural networks?
What is “training”?
In the jargon of artificial intelligence (ai) studies, “training” means optimizing a statistical analysis. modeloften implemented as a neural networkto make predictions based on some input data and a measure of how good these predictions are (“cost” or “loss” function). there are three main paradigms in which said procedure can occur: supervised, unsupervised (often <a target="_blank" class="af pt" href="https://aws.amazon.com/what-is/autoregressive-models/” rel=”noopener ugc nofollow” target=”_blank”>autoregressive), and reinforcement learning. In supervised In learning, each data point is labeled so that the model predictions can be directly compared to the true values (e.g this is the image of a cat or a dog). In unsupervised training, there are no explicit labels, but the comparison is performed with features extracted from the data itself (e.g predict the next word in a sentence). Finally, reinforcement Learning is based on optimizing the long-term returns of a sequence of decisions (predictions) based on the interaction between the statistical model and the environment (should the car brake or accelerate at a yellow traffic light?).
In all these cases, the optimization of the model parameters It is a long process that requires…
