785: Math, Quantum ML and Language Embeddings — with Dr. Luis Serrano (@SerranoAcademy)

Current Challenges

Quantum computing faces significant challenges in real-world machine learning applications. Jon Krohn and Luis Serrano discuss the limitations posed by the current number of qubits, which restrict practical applications 1. Luis highlights the difference between quantum and classical computing, noting that quantum computers can generate true randomness, unlike classical ones that rely on pseudo-random numbers 2.

A classical computer cannot generate a random bit. It can pretend to flip a coin and give you pseudo-random numbers, but they're not random at all.

--- Luis Serrano

Despite these challenges, there are promising results in using quantum circuits to find patterns beyond the reach of classical algorithms 2.

   

Future Potential

The future potential of quantum computing in machine learning is vast, though currently limited by technological constraints. Luis Serrano shares insights from his experience at Zapata Computing, emphasizing the infancy of quantum machine learning 3. He discusses ongoing research into quantum neural networks for supervised learning, which have shown promising results 4.

Our research was based on two things, basically. Question one was, is there anything we can do that is meaningful with what we have right now?

--- Luis Serrano

As quantum technology advances, it holds the potential to revolutionize machine learning by enabling computations that are currently impossible with classical systems 4.