Quantum Machine Learning

Quantum Machine Learning used in Novel Drug Discovery

One of the early use cases of Quantum Computing is Quantum Machine Learning, which is not surprising given the massive interest in classical Machine Learning. Now a new collaboration between Cambridge Quantum Computing (CQC), JSR Life Sciences and CrownBio are aiming to use the latest techniques in QML (Quantum Machine Learning) to help find biomarkers which could be used in novel cancer treatments.

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Quantum Machine Learning | Quantum News

CrownBio and JSR Life Sciences Partner with Cambridge Quantum Computing to Leverage Quantum Machine Learning for Novel Cancer Treatment Biomarker Discovery

CrownBio and JSR Life Sciences Partner with Cambridge Quantum Computing to Leverage Quantum Machine Learning for Novel Cancer Treatment Biomarker Discovery

Based in Sunnyvale, California, JSR Life Sciences operates a network of manufacturing facilities, R&D labs and sales offices. CrownBio and JSR Life Sciences Partner with Cambridge Quantum Computing to Leverage…

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Quantum Machine Learning | Quantum News

Quantum computing enters cancer arena with Cambridge-California pact

Quantum computing enters cancer arena with Cambridge-California pact

Crown Bioscience, a JSR Life Sciences company, is a global drug discovery and development service specialist providing translational platforms to advance oncology, inflammation, and metabolic disease research. Together, the partners will identify a strategy to implement an early quantum computing application that will…

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Quantum Machine Learning | Quantum News

How to enable quantum computing innovation through access

How to enable quantum computing innovation through access

During the same period, significant hardware research has brought us to the so-called NISQ-era, that of noisy, intermediate-scale quantum machines. Broader access to quantum computing resources and, with that access, broader participation will be key to formulating quantum applications. Although interesting, NISQ machines…

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Machine Learning | Quantum Machine Learning

Google develops language model with more than one trillion parameters

For machine learning algorithms, parameters are the building blocks. They are an important part of the historical training data. In the language domain, sophistication generally correlates with a higher number of parameters, and this has been proven to be a reliable standard. OpenAI’s GPT-3 has 175 billion parameters, making it one of the largest language models ever trained. It can make primitive analogies, generate recipes, and even code at a basic level.

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