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Contenuto fornito da Sebastian Hassinger and Kevin Rowney. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Sebastian Hassinger and Kevin Rowney o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
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Modular Quantum System Architectures with Yufei Ding

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Manage episode 409007833 series 3377506
Contenuto fornito da Sebastian Hassinger and Kevin Rowney. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Sebastian Hassinger and Kevin Rowney o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode, Sebastian and Kevin interview Professor Yufei Ding, an associate professor at UC San Diego, who specializes in the intersection of theoretical physics and computer science. They discuss Dr. Ding's research on system architecture in quantum computing and the potential impact of AI on the field. Dr. Ding's work aims to replicate the critical stages of classical computing development in the context of quantum computing. The conversation explores the challenges and opportunities in combining computer science, theoretical and experimental quantum computing, and the potential applications of quantum computing in machine learning.

Takeaways

  • Yufei Ding's research focuses on system architecture in quantum computing, aiming to replicate the critical stages of classical computing development in the context of quantum computing.
  • The combination of computer science, theoretical and experimental quantum computing is a unique approach that offers new insights and possibilities.
  • AI and machine learning have the potential to greatly impact quantum computing, and finding a generically applicable quantum advantage in machine learning could have a transformative effect.
  • The development of a simulation framework for exploring different system architectures in quantum computing is crucial for advancing the field and identifying viable outcomes.

Chapters

00:00 Introduction and Background
02:12 Yufei Ding's System Architecture
03:08 AI and Quantum Computing
04:19 Conclusion

  continue reading

40 episodi

Artwork
iconCondividi
 
Manage episode 409007833 series 3377506
Contenuto fornito da Sebastian Hassinger and Kevin Rowney. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Sebastian Hassinger and Kevin Rowney o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode, Sebastian and Kevin interview Professor Yufei Ding, an associate professor at UC San Diego, who specializes in the intersection of theoretical physics and computer science. They discuss Dr. Ding's research on system architecture in quantum computing and the potential impact of AI on the field. Dr. Ding's work aims to replicate the critical stages of classical computing development in the context of quantum computing. The conversation explores the challenges and opportunities in combining computer science, theoretical and experimental quantum computing, and the potential applications of quantum computing in machine learning.

Takeaways

  • Yufei Ding's research focuses on system architecture in quantum computing, aiming to replicate the critical stages of classical computing development in the context of quantum computing.
  • The combination of computer science, theoretical and experimental quantum computing is a unique approach that offers new insights and possibilities.
  • AI and machine learning have the potential to greatly impact quantum computing, and finding a generically applicable quantum advantage in machine learning could have a transformative effect.
  • The development of a simulation framework for exploring different system architectures in quantum computing is crucial for advancing the field and identifying viable outcomes.

Chapters

00:00 Introduction and Background
02:12 Yufei Ding's System Architecture
03:08 AI and Quantum Computing
04:19 Conclusion

  continue reading

40 episodi

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