Category: Amazon Braket

Three new capabilities launched today for Aquila on Amazon Braket let you customize lattice geometry and detuning. Learn how increased flexibility empowers your research.

Check out our latest blog to learn how we implemented a cost-effective development cycle for training a hybrid quantum-classical algorithm using Amazon Braket and hyperparameter optimization.

The story of how Qedma used Amazon Braket Direct for dedicated access to IonQ hardware to execute milestone VQE circuits. This post details leveraging reservations and collaborating directly with experts. An exciting look at accelerating innovation in quantum computing.

Today we look at how Citi Innovation Labs is exploring quantum computing for portfolio optimization in partnership with Classiq and AWS. Their research examines how adjustments to the QAOA algorithm’s penalty factor impact performance.

Discover how Airbus and BMW Group are harnessing quantum computing to tackle industry challenges. Join the Airbus-BMW Group Quantum Mobility Quest and help shape the future of transportation.

Available today, quantum computing developers, educators, and enthusiasts can learn the foundations of quantum computing on Amazon Web Services (AWS) with the Amazon Braket Digital Learning Plan and earn their own Digital badge – at no additional cost. You earn the badge after completing a series of learning courses and scoring at least 80% on an […]

Quantum computing’s potential for computational chemistry is immense, but there are practical limitations. We show how Kvantify’s FAST-VQE algorithm can deliver great accuracy, performance, superior cost-effectiveness, driving us closer to transformative applications in drug discovery.

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.

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’re announcing improvements to the task-processing speed and our support for parametric compilation on QPUs from Rigetti Computing in Amazon Braket. This enables up to 10x faster runtime performance for algorithms that use Amazon Braket Hybrid Jobs.