Amazon Braket now supports PennyLane, an open source software framework for hybrid quantum computing. Pennylane provides interfaces to common machine learning libraries, including PyTorch and TensorFlow. It’s now possible to train quantum circuits in the same way one would train a neural network. The integration with Amazon Braket allows to test and fine-tune algorithms faster and at a larger scale on scalable and fully managed simulators and run them quantum computing hardware.
Hybrid quantum algorithms use an iterative approach, with quantum computers as co-processors to classical computing resources. This approach helps mitigate the effect of errors inherent in today’s quantum computing hardware. With PennyLane, Amazon Braket provides an experience to get started with hybrid quantum algorithms.
Amazon Braket notebooks come pre-configured with PennyLane. There is also an Amazon Braket PennyLane plugin for specific development environment. Support for PennyLane is available in the AWS Regions where Amazon Braket is available.
Some interesting examples and tutorials about programming hybrid quantum algorithms using PennyLane on Amazon Braket can be found here: the example notebooks, the Amazon Braket developer guide, and the PennyLane GitHub repository. There is also a blog post by Jeff Barr which provides further information.