Tag: quantum algorithms

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.

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.

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.

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.

This blog post is an introduction to HPC on AWS for quantum computing researchers who are seeking to compare their quantum or hybrid algorithms against classical calculations.

In this post, we’ll explore the Wolfram Quantum Framework and show you how to connect it with Amazon Braket to run quantum algorithms.

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.

Earlier this year, QuEra and AWS sponsored the first-ever hackathon on a neutral-atom computer. In 24 hrs the teams solved hard problems using real quantum computers. It was a rush. We asked the winning team to tell us their story.

Running Jupyter notebooks as Hybrid Jobs on Amazon Braket, you get performance and convenience of jobs, without modifying code. In this post, we show how you can scale up from exploratory notebook to repeatable and reliable experiments on different quantum hardware.