AWS announces two substantial updates to Amazon Braket

Back in August, Amazon Braket hit general availability. The service provides remote access to quantum computing simulators and real quantum hardware hosted on the cloud. Since then, it's been integrated into high-end services like Orquestra to enable better quantum software development, especially for enterprise users.

Building on this, AWS announced today that Amazon Braket will now support PennyLane, which is an open-source software framework for hybrid quantum computing and quantum machine learning applications.

Image via PennyLane

Braket's integration with PennyLane should allow customers to train quantum circuits the same way they would train a traditional neural network using common machine learning libraries due to PennyLane's native support for PyTorch and TensorFlow. AWS claims that the quantum circuit's training process will be sped up by up to ten times since the library exploits parallelism.

PennyLane is pre-installed in Braket notebooks and you can also install the Braket-PennyLane plugin in your IDE. Once you do this, you can train quantum circuits as you would train neural networks, while also making use of familiar machine learning libraries such as PyTorch and TensorFlow. When you use PennyLane on the managed simulators that are included in Amazon Braket, you can train your circuits up to 10 times faster by using parallel circuit execution.

Furthermore, AWS has expanded the choice of simulators available on Braket as well. In addition to the state vector simulator, which could simulate a 34-qubit quantum computer, the newly added tensor network simulator can simulate up to 50 qubits for certain circuits. It does so by constructing a graph of the quantum circuit to find an optimized way to process it.

Amazon says that the new tensor network simulator is especially powerful for sparse circuits, circuits with local gates, and other circuits with inherent structure. Overall, this increased power should let customers explore and optimize quantum algorithms at a larger qubit scale.

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