Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Atos, Bayer and RWTH Aachen University Use Atos Quantum Learning Machine to Study Human Disease Patterns

Quantum computing to accelerate research in the Health sector

Atos, a global leader in digital transformation, Bayer, an international life science company, and RWTH Aachen University announce that they are working together to evaluate the use of Quantum Computing in research and analysis of human disease patterns. Computing and life science experts from these three institutions will use the Atos Quantum Learning Machine, the world’s highest-performing quantum simulator, to research the evolution of multi-morbidity human diseases from large data repositories.

“Quantum Computing is one of the up and coming technologies that will have a game-changing impact on the life science industry, healthcare providers and of course treatment options for patients. While we consider it being early days for QC we want to make sure to learn how and in which areas it can best be used.”, says Dirk Schapeler, VP G4A Digital Innovation from Bayer.

The project is based on anonymized real-world data of intensive care patients, to analyze and identify correlations between comorbidities and relevant patterns of disease evolution. This concept complements the approach of clinical trial studies that usually focuses on a limited number of patients and well-structured data to analyze disease criteria.

Read More: Interview with Jeffrey Kofman, CEO and Founder at Trint

“We need to better understand the health state of patients with more than one disease. The Atos Quantum Learning machine will help us analyze the evolution of a disease and the interaction with comorbidities.”, says Dr. Ulf Hengstmann, G4A Digital Health Innovation Manager from Bayer.”We already know that patients with specific diseases like heart failure can have several typical comorbidities. Now we need to understand why this is happening and how it affects therapy”.

The Atos Quantum Learning Machine is the first industrialized and turnkey universal gate-based quantum system capable of simulating up to 41 Qubits (Quantum bits). It combines an ultra-compact system with a universal and standardized quantum programming language, AQASM (Atos Quantum Assembly Language). It also embeds a powerful software stack to simulate quantum programs on any quantum hardware including modelling of quantum noise, a unique feature on the market. Thanks to an in-memory based infrastructure, the computing simulation capacity can scale at any stage to support application scalability combined with higher workloads.

Related Posts
1 of 1,459

Read More: The Top 5 “Recipes” That Give AI Projects a Higher Likelihood of Success

“Quantum computing is the next game-changer of the digital age”, says Ursula Morgenstern, CEO of Atos Germany. “To unfold its full potential, customers need to develop and explore concrete use cases like Bayer is doing in this project”.

The challenge for the coming years is to find applications which are able to run on early qubits quantum processors to demonstrate that quantum can either tackle problems that traditional computing cannot solve or prove that it is exponentially faster.

Read More:  Fluor Uses IBM Watson to Deliver Predictive Analytics Capability for Megaprojects

In parallel to the quantum computing approach, the Joint Research Center for Computational Biomedicine at RWTH Aachen University is running the analysis on a High Performance Computer (HPC) to evaluate the accuracy and performance of the quantum experiment results.

“Structural learning of mechanisms from massive data is a research focus of the Joint Research Center for Computational Biomedicine. The combination of quantum computing and machine learning is to a certain extent still a terra incognita where we see high potential in medical data analysis”, says Professor Andreas Schuppert, Head of the JRC for Computational Biomedicine. “We are therefore creating comparative data analysis using HPC to evaluate its advantages in structural learning”.

Read More: The AI Gold Rush: How to Make Money off AI and Machine Learning!

Leave A Reply

Your email address will not be published.