AWS Quantum Technologies Blog
Tag: quantum algorithms
Introducing the Amazon Braket Learning Plan and Digital Badge
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 […]
A detailed, end-to-end assessment of a quantum algorithm for portfolio optimization, released by Goldman Sachs and AWS
In this post we’ll walk you through some key takeaways from a paper published today by scientists from Goldman Sachs and AWS describing a quantum algorithm for portfolio optimization.
Towards practical molecular electronic structure simulations on NISQ devices with Amazon Braket and Kvantify’s FAST-VQE algorithm
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.
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.
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.
Running quantum chemistry calculations using AWS ParallelCluster
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.
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.