In this post, we’ll describe how the PennyLane-Braket SDK plugin to study the ground state of the anti-ferromagnetic Ising spin-chain on a 1D lattice on the Aquila quantum processor, a neutral-atom quantum computer available on-demand via the AWS Cloud.
Explore quantum algorithms faster by running your local Python code as an Amazon Braket Hybrid Job with minimal code changes
Today we’ll show you how to use a new python decorator from the Amazon Braket SDK to help algorithm researchers seamlessly execute local Python functions as an Amazon Braket Hybrid Job with just one extra line of code.
Today, we launched a new feature in Amazon Braket Hybrid Jobs, which allows you to run hybrid workloads with simulators that are embedded with your algorithm code. For instance, one of the simulators available in this new feature is the PennyLane Lightning GPU simulator, accelerated by NVIDIA’s cuQuantum library. In this blog post, we show […]
Accelerate hybrid quantum-classical algorithms on Amazon Braket using embedded simulators from Xanadu’s PennyLane featuring NVIDIA cuQuantum
In 2021, Amazon Braket, the fully-managed quantum computing service from AWS, launched Amazon Braket Hybrid Jobs to provide customers a convenient way to run hybrid algorithms without worrying about managing the underlying infrastructure. With features such as priority access to quantum processing units (QPUs), Amazon Braket Hybrid Jobs is designed for lower latency and faster […]
Over 3200 developers from across the PennyLane community came together to deliver impactful and creative solutions to quantum computing challenges during the virtual QHack event held February 14-25. Participants joined from 105 countries worldwide, from the high school level all the way through PhDs and professionals. AWS sponsored QHack for the second consecutive year, after […]
The field of quantum computing today resembles the state of machine learning a few decades ago – in many ways. Near-term quantum algorithms for optimization, computational chemistry, and other applications are based on the very same principles that are used to train a neural network. In machine learning, there was no theoretical proof that a […]