Super Data Science: ML & AI Podcast with Jon Krohn
851: Quantum ML: Real-World Applications Today, with Dr. Florian Neukart
Jan 7, 2025
Florian Neukart, Chief Product Officer at Terra Quantum AG and an assistant professor at Leiden University, dives into fascinating discussions on quantum computing. He addresses the practicality of quantum-safe security amid rising quantum capabilities. The conversation explores hybrid quantum-classical systems and their application in industries like healthcare and automotive. Neukart emphasizes the importance of ethical quantum technology, evolving hardware, and the accessibility of quantum machine learning for aspiring data scientists.
01:11:51
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Quick takeaways
- Quantum computing integrates classical systems, enhancing real-world problem-solving, especially in optimization tasks like logistics and scheduling.
- The rise of hybrid quantum-classical systems permits easier transitions into quantum applications without overhauling traditional algorithms.
Deep dives
Advancements in Quantum Machine Learning
A new generation of hybrid quantum classical systems has made quantum computing more applicable to real-world problems. This approach integrates classical computational power with quantum capabilities, allowing for efficient problem-solving without the need to completely restructure traditional algorithms. For instance, rather than processing entire neural networks on a quantum chip, only specific parts are translated into quantum circuits, enhancing the practical utility of quantum machine learning today. This hybrid approach signals that quantum technologies can already assist in various domains, including machine learning and optimization tasks.
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