The Python Podcast.__init__

The Python Podcast.__init__

The podcast about Python and the people who make it great

10 March 2020

Open Source Machine Learning On Quantum Computers With Xanadu AI - E253

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Quantum computers promise the ability to execute calculations at speeds several orders of magnitude faster than what we are used to. Machine learning and artificial intelligence algorithms require fast computation to churn through complex data sets. At Xanadu AI they are building libraries to bring these two worlds together. In this episode Josh Izaac shares his work on the Strawberry Fields and Penny Lane projects that provide both high and low level interfaces to quantum hardware for machine learning and deep neural networks. If you are itching to get your hands on the coolest combination of technologies, then listen now and then try it out for yourself.


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  • Your host as usual is Tobias Macey and today I’m interviewing Josh Izaac about how the work that he is doing at Xanadu AI to make it easier to build applications for quantum processors


  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what you are working on at Xanadu AI?
    • How do the specifics of your quantum hardware influence the way in which developers need to build their algorithms? (e.g. as compared to DWave)
  • What are some of the underlying principles that developers need to understand in order to take full advantage of the capabilities provided by quantum processors?
  • Can you outline the different components and libraries that you are building to simplify the work of building machine learning/AI projects for quantum processors?
    • What’s the story behind all of the Beatles references?
    • How do the different libraries fit together?
  • What are some of the workloads and use cases that you and your customers are focused on?
  • What are some of the most challenging aspects of designing a library that is accessible to developers while being able to take advantage of the underlying hardware?
  • How does the workflow for machine learning on quantum computers differ from what is being done in classical environments?
    • Given the magnitude of computational power and data processing that can be achieved in a quantum processor it seems that there is a potential for small bugs to have disproportionately large impacts. How can developers identify and mitigate potential sources of error in their algorithms?
  • For someone who is building an application or algorithm to be executed on a Xanadu processor, what does their workflow look like?
    • What are some of the common errors or misconceptions that you have seen in customer code?
  • Can you describe the design and implementation of the Penny Lane and Strawberry Fields libraries and how they have evolved since you first began working on them?
  • What are some of the most ambitious or exciting use cases for quantum systems that you have seen?
  • How are you using the computational capabilities of your platform to feed back into the research and design of successive generations of hardware?
  • What are some useful heuristics for determining whether it is worthwhile to build for a quantum processor rather than leveraging classical hardware?
  • What are some of the most interesting/unexpected/useful lessons that you have learned while working on quantum algorithms and the libraries to support them?
  • What is in store for the future of the Xanadu software ecosystem?
  • What are your predictions for the near to medium term of quantum computing?

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Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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