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.
Category: Quantum Machine Learning
Quantum Machine Learning is all about how to exploit Quantum Computing to build smarter algorithms for AI and Machine Learning.
CrownBio and JSR Life Sciences Partner with Cambridge Quantum Computing to Leverage Quantum Machine Learning for Novel Cancer Treatment Biomarker DiscoveryBased 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…
Quantum computing enters cancer arena with Cambridge-California pactCrown 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…
QuSoft Seminar: David Sutter (IBM Zurich)In this talk, we address this question by trying to understand how powerful and trainable quantum machine learning models are, relative to popular classical neural networks. Everyone is welcome to attend the online QuSoft seminar this week with David Sutter on ‘The power of quantum…
Women in Quantum IV – Global Virtual Summit To Champion Women in Quantum Computing March 9-11The main sponsors, Zapata Computing and Zurich Instruments, are joined by Strangeworks, IQM, QC Ware, Quantum Brilliance, D-Wave, SeeQC, Xanadu and Honeywell. Truly an international summit, speakers and audience will span the globe, coming from…
The quantum advantage: a novel demonstrationAt the LIP6 Laboratory (CNRS/Sorbonne University) and the Institut de Recherche en Informatique Fondamentale (CNRS/University of Paris) In 2019, Google achieved the first demonstration of this type when it sampled from random quantum circuits on their Sycamore chip, made of 53 superconducting qubits. Very recently,…
Developers Double Down on the Machine Learning Basics Moving Into the New YearIn particular, three hardware-focused ideas are essential to ML hardware development: designing toward the edge, low-power architecture, and compatibility with ML frameworks. As for 2021, there could be great potential for ML applications in quantum computing, robotics, and…
How to enable quantum computing innovation through accessDuring 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…
Cambridge Quantum Computing (CQC) has recently announced its results after collaborating with Aker BP, one of the largest European independent companies.
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.
