Tag: 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.

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.

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.

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.

In this post, we show how tools from our partner Good Chemistry can help you further explore the potential of quantum computing for non-trivial molecular systems, and design experiments that you can run on Amazon Braket.

In this blog post, we show how you can track and manage hybrid quantum-classical experiments with the Hybrid Jobs feature in Amazon Braket and Amazon SageMaker Experiments, used by ML developers on AWS to organize their experiments and trials.

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.

Over 2800 developers from the PennyLane community got together during the virtual QHack event in February. Today we bring you some highlights and tell you about the winning projects.

Experimental physicists are vital to the future of quantum computing, sensing, and networking, laying the groundwork for new types of devices and enhancing the performance of existing technology. Their work, like all open science, can be accelerated through collaboration, but providing access to prototype hardware is challenging. The Cloud Queue for Quantum Devices project provides […]

Introduction The problem of robot motion planning is pervasive across many industry verticals, including (for example) automotive, manufacturing, and logistics. In the automotive industry, robotic path optimization problems can be found across the value chain in body shops, paint shops, assembly, and logistics, among others [1]. Typically, hundreds of robots operate in a single plant […]