Posted On: Dec 8, 2020
HAQM 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, so you can train quantum circuits in the same way you would train a neural network. The integration with HAQM Braket allows you to test and fine-tune algorithms faster and at a larger scale on scalable and fully managed simulators and run them on your choice of 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, HAQM Braket provides an easy, intuitive, and high-performing experience for you to get started with hybrid quantum algorithms. By combining PennyLane with HAQM Braket’s managed simulators for testing and fine tuning your algorithms, you can achieve a 10x or more reduction in training times when you use parallel circuit execution, compared to executing your algorithm on a single machine.
HAQM Braket notebooks come pre-configured with PennyLane so you can get started quickly. You can also install the HAQM Braket PennyLane plugin if you prefer to use your own development environment. Support for PennyLane is available in the AWS Regions where HAQM Braket is available. To get started wtih programming hybrid quantum algorithms using PennyLane on HAQM Braket, please visit the example notebooks, the HAQM Braket developer guide, and the PennyLane GitHub repository.
Modified 8/27/2021 – In an effort to ensure a great experience, expired links in this post have been updated or removed from the original post.