From the course: Cloud Quantum Computing Essentials
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Cloud quantum neural networks: Hello worlds
From the course: Cloud Quantum Computing Essentials
Cloud quantum neural networks: Hello worlds
- [Instructor] In addition to potential complexity around data preparation for machine learning using quantum programming, another consideration is noise or errors. In general, the Google implementation of TensorFlow Quantum examples are excellent and very, very useful. So starting with hello, world or hello, many worlds, these tutorials introduce topics around the pragmatic reality of trying to use QPUs to speed up ML operations. So I recommend you go through all of them if you're going to be trying to build anything for actual customers. And let's just start by looking at hello, many worlds. So this is showing a classical neural network and it can learn to correct qubit calibration errors. It's using Cirq, of course, the library we've seen throughout this course. And it's going to create, edit and invoke noisy, intermediate, scale, quantum or NISP circuits and it's going to demonstrate how Cirq interfaces with…
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