AWS Quantum Computing Blog

Tag: quantum machine learning

Winners announced in the BMW Group Quantum Computing Challenge

The four winning teams of the BMW Quantum Computing Challenge were announced this morning at the annual Q2B conference in Santa Clara, California. The challenge, focused on discovering potential quantum computing solutions for real-world use cases, was a collaboration between the BMW Group and the Amazon Quantum Solutions Lab Professional Services team. “We at the […]

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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 […]

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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 […]

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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 […]

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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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