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Quantum Computing for Finance: A Book Review

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Manage episode 484060192 series 3655012
Content provided by kathrynj2. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by kathrynj2 or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This episode reviews the book, Quantum Computing for Finance by Oswaldo Zapata, outlining the foundational concepts of classical and quantum computing, including topics like Boolean logic, qubits, quantum gates, and error correction. It explores how quantum computing can potentially enhance classical financial methods such as portfolio optimization, Monte Carlo simulations, and machine learning algorithms used in areas like credit risk assessment and fraud detection. The discussion includes how the book surveys the current quantum computing landscape, detailing different hardware technologies, key companies and startups, and offering advice on how financial institutions can prepare for the integration of quantum technology, emphasizing the importance of talent development and hybrid approaches.

References

Zapata, O. Quantum Computing for Finance

The book is available at:

https://www.scribd.com/document/860542791/Quantum-Computing-for-Finance-Oswaldo-Zapata-PhD

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

  continue reading

12 episodes

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iconShare
 
Manage episode 484060192 series 3655012
Content provided by kathrynj2. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by kathrynj2 or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This episode reviews the book, Quantum Computing for Finance by Oswaldo Zapata, outlining the foundational concepts of classical and quantum computing, including topics like Boolean logic, qubits, quantum gates, and error correction. It explores how quantum computing can potentially enhance classical financial methods such as portfolio optimization, Monte Carlo simulations, and machine learning algorithms used in areas like credit risk assessment and fraud detection. The discussion includes how the book surveys the current quantum computing landscape, detailing different hardware technologies, key companies and startups, and offering advice on how financial institutions can prepare for the integration of quantum technology, emphasizing the importance of talent development and hybrid approaches.

References

Zapata, O. Quantum Computing for Finance

The book is available at:

https://www.scribd.com/document/860542791/Quantum-Computing-for-Finance-Oswaldo-Zapata-PhD

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

  continue reading

12 episodes

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