Tag: Technical How-to

This post was contributed by Mao Lin, Bruno Camino, John Buckeridge and Scott M. Woodley Many advanced materials — from battery electrodes to semiconductor alloys — owe their useful properties to atomic-scale disorder. But predicting how atoms arrange themselves at a given temperature is hard: the number of possible configurations explodes combinatorially and sampling them […]

This post was contributed by Dimitar Trenev, Sebastian Stern, Tyler Takeshita, Cedric Lin, Peter Komar, Pooja Rao, Jerome Gonthier, and Elica Kyoseva. As quantum computing matures toward fault tolerance, a pressing question faces the high-performance computing (HPC) community: why is tightly integrating quantum processors to classical supercomputing infrastructure important? Today, algorithm researchers from Amazon Web […]

Introduction In biochemical processes development and analysis, binding energy, the energy released when a small molecule docks into a protein’s active site, determines how strongly a compound, such as a ligand, interacts with its protein target. Early-stage computational prediction of this quantity helps research teams prioritize candidates before committing to resource-intensive laboratory testing. Conventional methods […]

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 […]

Amazon Braket provides access to quantum computing resources and tools to develop quantum algorithms, test them on quantum circuit simulators, and run them on different quantum hardware technologies. Amazon Braket notebooks provide customers with a fully managed development environment that comes pre-installed with a range of and quantum development frameworks, including the Amazon Braket SDK […]

Quantum computing promises to revolutionize industries. Our new Quantum Embark program provides expert guidance to help you harness its potential.

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

ML models driving high-stakes decisions need interpretability. See how the Amazon QSL and Fidelity FCAT developed interpretable models based on Boolean logic.

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.

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 […]