AWS Quantum Technologies Blog
Tag: quantum computing
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.
Speeding up hybrid quantum algorithms with parametric circuits on Amazon Braket
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.
Constructing an “end-to-end” quantum algorithm: a comprehensive technical resource for algorithms designers
Today we’re introducing Quantum algorithms: A survey of applications and end-to-end complexities. This is a comprehensive resource, designed for quantum computing researchers and customers who are looking to explore how quantum algorithms will apply to their use cases.
Designing hybrid algorithms for neutral-atom quantum hardware using Bayesian optimization
BMW sponsors PhD students to research novel approaches to computational challenges. Today we’ll show you how they bridge the gap between academia and industry, to solve some of the hardest problems in industry using Bayesian protocols for quantum optimization problems.
Optimization with OpenQAOA on Amazon Braket
In this blog post, we introduce OpenQAOA, Entropica Labs’ open-source SDK for the QAOA, and OpenQAOA-Braket, a plugin specifically designed to expand OpenQAOA’s capabilities by leveraging Amazon Braket.
How to use pulse-level control on OQC’s superconducting quantum computer
Amazon Braket Pulse lets you control the low-level analog instructions for quantum computers, to optimize performance or develop new analog protocols, like error suppression and mitigation. Today we show you how and describe some best practices.
Introducing the Wolfram Quantum Framework for Amazon Braket
In this post, we’ll explore the Wolfram Quantum Framework and show you how to connect it with Amazon Braket to run quantum algorithms.
Graph coloring with physics-inspired graph neural networks
In this post we show how physics-inspired graph neural networks can be used to solve the notoriously hard graph-coloring problem, at scale. This can help in an huge number of familiar resource-allocation problems from sports to rental cars.
Introducing a cost control solution for Amazon Braket
Everyone needs effective cost management. In this post, we’ll introduce you to an Amazon Braket cost-control solution, which we’ve open-sourced on GitHub under an MIT license.
Amazon Braket launches IonQ Aria with built-in error mitigation
In this post, you’ll learn about IonQ’s latest trapped-ion quantum computer, IonQ Aria, which is now available on Amazon Braket, and is also the first QPU in AWS to feature error mitigation.