As I understand it, quantum computing is helpful for solving NP-hard problems, and NVIDIA has introduced QUDA to enable hybrid quantum-classical computing with applications spanning drug discovery, chemistry, weather, finance, logistics, and more. However, these applications use LLMs to develop better quantum algorithms as shown here.
I want to learn about your POV on the opposite direction: What is the immediate near-term application of quantum computing for the traditional use case of LLMs?Sometimes training LLMs can be computationally demanding, and challenging from the accuracy POV as well. Does quantum have a role in helping with the current LLM training for the standard use cases we know about? Here is an article from IONQ. Thanks in advance for your response.
As I understand it, quantum computing is helpful for solving NP-hard problems, and NVIDIA has introduced QUDA to enable hybrid quantum-classical computing with applications spanning drug discovery, chemistry, weather, finance, logistics, and more. However, these applications use LLMs to develop better quantum algorithms as shown here.
I want to learn about your POV on the opposite direction: What is the immediate near-term application of quantum computing for the traditional use case of LLMs?Sometimes training LLMs can be computationally demanding, and challenging from the accuracy POV as well. Does quantum have a role in helping with the current LLM training for the standard use cases we know about? Here is an article from IONQ. Thanks in advance for your response.