AWS Quantum Technologies Blog

Category: Quantum Technologies

Setting up your local development environment in Amazon Braket

As a fully managed quantum computing service, Amazon Braket provides a development environment based on Jupyter notebooks for you to experiment with quantum algorithms, test them on quantum circuit simulators, and run them on different quantum hardware technologies. However, Amazon Braket does not restrict you to use only the managed notebooks and the AWS management […]

Amazon Braket

AWS joins the OpenQASM 3.0 Technical Steering Committee

In the early 1990s, James Gosling introduced the Java programming language. One of the key advantages to Java was that programmers could write code once and have it run on many different backends, without needing to concern themselves with the underlying hardware. This was enabled by an intermediate representation called Java bytecode. Java programs were […]

A tensor network representation of a GHZ circuit and the corresponding circuit diagram

Simulating quantum circuits with Amazon Braket

Whether you want to research quantum algorithms, study the effects of noise in today’s quantum computers, or just prototype and debug your code, the ability to run large numbers of quantum circuits fast and cost effectively is critical to accelerate innovation. This post discusses the different types of quantum circuit simulators offered by Amazon Braket […]

Architecture diagram for the Quantum Software Research Hub, integrated with Amazon Braket

AWS supporting the Quantum Software Research Hub led by Osaka University in Japan

Since Amazon Braket, the AWS quantum computing service, was launched, customers have said they want to learn the basics of the technology, explore quantum computing, and discuss use cases with experts in their local communities. In Japan, AWS is working with Osaka University through the Quantum Software Research Hub to educate enterprise, startup, and academic […]

a diagram of 2 independent quantum processing units combined with a classical extractor to generate fully random bits

Generating quantum randomness with Amazon Braket

Introduction – the need for randomness Random numbers are a crucial resource used throughout modern computer science. For example, in computation, randomized algorithms give efficient solutions for a variety of fundamental problems for which no deterministic algorithms are available. This includes Monte Carlo methods that have widespread applications in science for the simulation of physical, […]

BMW robotic assembly line

Exploring industrial use cases in the BMW Group Quantum Computing Challenge

Today, the BMW Group launched a global open innovation challenge focused on discovering potential quantum computing solutions for real-world use cases: The BMW Group Quantum Computing Challenge. We are delighted to collaborate with BMW on this challenge, and to invite the quantum community explore new approaches to industrial applications. It’s still early days in quantum […]

Graphic of a Wigner functions of (a) a GKP state with 10 dB GKP squeezing (b) a GKP state with 12 dB GKP squeezing.

Low-overhead quantum computing with Gottesman-Kitaev-Preskill qubits

Introduction This post summarizes a research paper from the AWS Center for Quantum Computing that proposes a direction to implement fault-tolerant quantum computers with minimal hardware overhead. This research shows that by concatenating the surface code with Gottesman, Kitaev, and Preskill (GKP) qubits, it is theoretically possible to achieve a logical error rate of 10-8 […]

Quantum Machine Learning on QC Ware Forge built on Amazon Braket

By Fabio Sanches, Quantum Computing Services Lead, QC Ware In this post, I introduce you to QC Ware Forge, which is built on Amazon Braket. It provides turnkey quantum algorithms, so you can speed up research into applying quantum computing to hard data science problems. I also walk you through an example of using Forge […]

Amazon Braket

Using Quantum Machine Learning with Amazon Braket to Create a Binary Classifier

By Michael Fischer, Chief of Innovation at Aioi Insurance Services USA, Daniel Brooks, Research Data Scientist formerly of Aioi Insurance Services USA, with AWS quantum solution architects Pavel Lougovski and Tyler Takeshita. This post details an approach taken by Aioi Insurance Services USA to research an exploratory quantum machine learning application using the Amazon Braket […]

Cat codes on Bloch sphere

Designing a fault-tolerant quantum computer based on Schrödinger-cat qubits

At the AWS Center for Quantum Computing, we are doing scientific research and development on quantum computing algorithms and hardware. This post summarizes findings from our first architecture paper that describes a theoretical blueprint for a fault-tolerant quantum computer that features a novel approach to quantum error correction (QEC). Fair warning, this post dives somewhat […]