AWS Quantum Computing Blog

Category: Open Source

Closeup coding on screen, Woman hands programming on screen laptop

Introducing the Qiskit provider for Amazon Braket

We are excited to share a solution to one of our most frequent customer requests: a Qiskit provider for Amazon Braket. Users can now take their existing algorithms written in Qiskit, a widely used open-source quantum programming SDK and, with a few lines of code, run them directly on Amazon Braket. The qiskit-braket-provider currently supports […]

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Amazon Braket launches OpenQASM support

Last year, we announced that AWS had joined the OpenQASM Technical Steering Committee to help shape a unified approach to express quantum programs across a variety of different hardware technologies. Today, we are excited to announce that customers can now run OpenQASM programs on all gate-based devices on Amazon Braket. Quantum computing is a nascent […]

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Mitiq Overview

Exploring quantum error mitigation with Mitiq and Amazon Braket

By Ryan LaRose, a researcher with Unitary Fund and Michigan State University; Nathan Shammah, CTO of Unitary Fund; Peter Karalekas, Software Engineer at the AWS Center for Quantum Computing; and Eric Kessler, Sr. Manager of Applied Science for Amazon Braket. In this blog post, we demonstrate how to use Mitiq, an open-source library for quantum […]

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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 […]

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Working with PennyLane for variational quantum algorithms and quantum machine learning

The field of quantum computing today resembles the state of machine learning a few decades ago – in many ways. Near-term quantum algorithms for optimization, computational chemistry, and other applications are based on the very same principles that are used to train a neural network. In machine learning, there was no theoretical proof that a […]

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