Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business…

Pune, May 18, 2020 (GLOBE NEWSWIRE) -- The global patent analytics market size is predicted to USD 1,668.4 million by 2027, exhibiting a CAGR of 12.4% during the forecast period. The increasing advancement and integration of machine learning, artificial intelligence, and the neural network by enterprises will have a positive impact on the market during the forecast period. Moreover, the growing needs of companies to protect intellectual assets will bolster healthy growth of the market in the forthcoming years, states Fortune Business Insights in a report, titled Patent Analytics Market Size, Share and Industry Analysis, By Component (Solutions and Services), By Services (Patent Landscapes/White Space Analysis, Patent Strategy and Management, Patent Valuation, Patent Support, Patent Analytics, and Others), By Enterprise Size (Large Enterprises, Small & Medium Enterprises), By Industry (IT and Telecommunications, Healthcare, Banking, Financial Services and Insurance (BFSI), Automotive, Media and Entertainment, Food and Beverages and, Others), and Regional Forecast, 2020-2027 the market size stood at USD 657.9 million in 2019. The rapid adoption of the Intellectual Property (IP) system to retain an innovation-based advantage in business will aid the expansion of the market.

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An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

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Market Driver:

Integration of Artificial Intelligence to Improve Market Prospects

The implementation of artificial intelligence technology for analyzing patent data will support the expansion of the market. AI-based semantic search uses an artificial neural network to enhance patent discovery by improving accuracy and efficiency. For instance, in February 2018, PatSeer announced the unveiling of ReleSense, an AI-driven NLP engine. The engine utilizes 12 million+ semantic rules to gain from publically available patents, scientific journals, clinical trials, and associated data sources. ReleSense with its wide range of AI-driven capabilities offers search from classification, via APIs and predictive-analytics for apt IP solutions. The growing application of AI for domain-specific analytics will augur well for the market in the forthcoming years. Furthermore, the growing government initiatives to promote patent filing activities will boost the patent analytics market share during the forecast period. For instance, the Government of India introduced a new scheme named Innovative/ Creative India, to aware people of the patents and IP laws and support patent analytics. In addition, the growing preferment for language model and neural network intelligence for accurate, deep, and complete data insights will encourage the market.

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Regional Analysis:

Implementation of Advanced Technologies to Promote Growth in North America

The market in North America stood at USD 209.2 million and is expected to grow rapidly during the forecast period owing to the presence of major companies in the US such as IBM Corporation, Amazon.Com, Inc. The implementation of advanced technologies including IoT, big data, and artificial intelligence by major companies will aid growth in the region.

Considering this the U.S. is expected to showcase a higher growth in the patent filing. As per the World Intellectual Property, in 2018, the U.S. filed 230,085 patent applications across several domains. Asia Pacific is predicted to witness tremendous growth during the forecast period. The growth is attributed to China, which accounts for a major share in the global patent filings. According to WIPO, intellectual property (IP) office in China had accounted for 46.6% global share in patent registration, in 2018. The growing government initiatives concerning patents and IP laws in India will significantly enable speedy growth in Asia Pacific.

Key Development:

March 2018: Ipan GmbH announced its collaboration with Patentsight, Corsearch, and Uppdragshuset for the introduction of an open IP platform named IP-x-change platform. The platform enables prior art search, automatic data verification tools, smart docketing tools integrated in real-time to optimize IP management solution.

List of Key Companies Operating in the Patent Analytics Market are:

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Detailed Table of Content

TOC Continued..!!!

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Have a Look at Related Research Insights:

Intellectual Property Software Market Size, Share and Global Trend By Deployment (On-premises & Cloud-based solutions), By Services (Development & Implementation Services, Consulting Services, Maintenance & Support Services), By Applications (Patent Management, Trademark Management and others), By Industry Vertical (Healthcare, Electronics and others) and Geography Forecast till 2025

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Fortune Business Insightsoffers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights, we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

Contact Us:Fortune Business Insights Pvt. Ltd.308, Supreme Headquarters,Survey No. 36, Baner,Pune-Bangalore Highway,Pune- 411045, Maharashtra,India.Phone:US: +1-424-253-0390UK: +44-2071-939123APAC: +91-744-740-1245Email:sales@fortunebusinessinsights.comFortune Business InsightsLinkedIn|Twitter|Blogs

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Patent Analytics Market to Reach USD 1,668.4 Million by 2027; Integration of Machine Learning and Artificial Intelligence to Spur Business...

The impact of the coronavirus on the Machine Learning in Healthcare Cybersecurity Market Report 2020 – News Distinct

Global Machine Learning in Healthcare Cybersecurity Market Analysis 2020 with Top Companies, Production, Consumption, Price and Growth Rate

The Machine Learning in Healthcare Cybersecurity Market 2020 report includes the market strategy, market orientation, expert opinion and knowledgeable information. The Machine Learning in Healthcare Cybersecurity Industry Report is an in-depth study analyzing the current state of the Machine Learning in Healthcare Cybersecurity Market. It provides a brief overview of the market focusing on definitions, classifications, product specifications, manufacturing processes, cost structures, market segmentation, end-use applications and industry chain analysis. The study on Machine Learning in Healthcare Cybersecurity Market provides analysis of market covering the industry trends, recent developments in the market and competitive landscape.

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It takes into account the CAGR, value, volume, revenue, production, consumption, sales, manufacturing cost, prices, and other key factors related to the global Machine Learning in Healthcare Cybersecurity market. All findings and data on the global Machine Learning in Healthcare Cybersecurity market provided in the report are calculated, gathered, and verified using advanced and reliable primary and secondary research sources. The regional analysis offered in the report will help you to identify key opportunities of the global Machine Learning in Healthcare Cybersecurity market available in different regions and countries.

The Global Machine Learning in Healthcare Cybersecurity 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Machine Learning in Healthcare Cybersecurity analysis is provided for the international markets including development trends, competitive landscape analysis, and key regions development status.

Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report also states import/export consumption, supply and demand Figures, cost, price, revenue and gross margins.

In addition to this, regional analysis is conducted to identify the leading region and calculate its share in the global Machine Learning in Healthcare Cybersecurity. Various factors positively impacting the growth of the Machine Learning in Healthcare Cybersecurity in the leading region are also discussed in the report. The global Machine Learning in Healthcare Cybersecurity is also segmented on the basis of types, end users, geography and other segments.

Our new sample is updated which correspond in new report showing impact of COVID-19 on Industry

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The report can answer the following questions:

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Table of Content

1 Industry Overview of Machine Learning in Healthcare Cybersecurity

2 Manufacturing Cost Structure Analysis

3 Development and Manufacturing Plants Analysis of Machine Learning in Healthcare Cybersecurity

4 Key Figures of Major Manufacturers

5 Machine Learning in Healthcare Cybersecurity Regional Market Analysis

6 Machine Learning in Healthcare Cybersecurity Segment Market Analysis (by Type)

7 Machine Learning in Healthcare Cybersecurity Segment Market Analysis (by Application)

8 Machine Learning in Healthcare Cybersecurity Major Manufacturers Analysis

9 Development Trend of Analysis of Machine Learning in Healthcare Cybersecurity Market

10 Marketing Channel

11 Market Dynamics

12 Conclusion

13 Appendix

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The impact of the coronavirus on the Machine Learning in Healthcare Cybersecurity Market Report 2020 - News Distinct

Global Machine Learning as a Service (MLaaS) Market Expected to reach highest CAGR by 2025: Microsoft, International Business Machine, Amazon Web…

The study on Global Machine Learning as a Service (MLaaS) Market , offers deep insights about the Machine Learning as a Service (MLaaS) market covering all the crucial aspects of the market. Moreover, the report provides historical information with future forecast over the forecast period. Some of the important aspects analyzed in the report includes market share, production, key regions, revenue rate as well as key players. This Machine Learning as a Service (MLaaS) report also provides the readers with detailed figures at which the Machine Learning as a Service (MLaaS) market was valued in the historical year and its expected growth in upcoming years. Besides, analysis also forecasts the CAGR at which the Machine Learning as a Service (MLaaS) is expected to mount and major factors driving markets growth. This Machine Learning as a Service (MLaaS) market was accounted for USD xxx million in the historical year and is estimated to reach at USD xxx million by the end of the year 2025..

This study covers following key players:MicrosoftInternational Business MachineAmazon Web ServicesGoogleBigmlFicoHewlett-Packard Enterprise DevelopmentAt&T

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To analyze the global Machine Learning as a Service (MLaaS) market the analysis methods used are SWOT analysis and PESTEL analysis. To identify what makes the business stand out and to take the chance to gain advantage from these findings, SWOT analysis is used by marketers. Whereas PESTEL analysis is the study concerning Economic, Technological, legal political, social, environmental matters. For the analysis of market on the terms of research strategies, these techniques are helpful.It consists of the detailed study of current market trends along with the past statistics. The past years are considered as reference to get the predicted data for the forecasted period. Various important factors such as market trends, revenue growth patterns market shares and demand and supply are included in almost all the market research report for every industry. It is very important for the vendors to provide customers with new and improved product/ services in order to gain their loyalty. The up-to-date, complete product knowledge, end users, industry growth will drive the profitability and revenue. Machine Learning as a Service (MLaaS) report studies the current state of the market to analyze the future opportunities and risks.

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Market segment by Type, the product can be split into Special ServiceManagement Services

Market segment by Application, split into BankingFinancial ServicesInsuranceAutomobileHealth CareDefenseRetailMedia & EntertainmentCommunicationOther

For the study of the Machine Learning as a Service (MLaaS) market it is very important the past statistics. The report uses past data in the prediction of future data. The keyword market has its impact all over the globe. On global level Machine Learning as a Service (MLaaS) industry is segmented on the basis of product type, applications, and regions. It also focusses on market dynamics, Machine Learning as a Service (MLaaS) growth drivers, developing market segments and the market growth curve is offered based on past, present and future market data. The industry plans, news, and policies are presented at a global and regional level.

Some Major TOC Points:1 Report Overview2 Global Growth Trends3 Market Share by Key Players4 Breakdown Data by Type and ApplicationContinued

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Global Machine Learning as a Service (MLaaS) Market Expected to reach highest CAGR by 2025: Microsoft, International Business Machine, Amazon Web...

Cloud Storage Market to Reach USD 297.54 Billion by 2027; Higher Adoption of Machine Learning to Boost Growth, Says Fortune Business Insights -…

Key Companies Covered in Cloud Storage Market Research Report Are Amazon Web Services, Inc., Dell Technologies Inc., Dropbox, Fujitsu Ltd, Inc., Google, Inc., Hewlett Packard Enterprise Development LP, IBM Corporation, Microsoft Corporation, Oracle, pCloud AG, Rackspace, Inc., VMware, Inc.

PUNE, India, May 18, 2020 /PRNewswire/ -- The global cloud storage market is set to gain traction from the rising adoption of autonomous systems and machine learning. Besides, the introduction to unique video systems, internet of things (IoT), and remote sensing technologies are driving the market growth. This information is provided by Fortune Business Insights in a recent study, titled, "Cloud Storage Market Size, Share & Industry Analysis, By Component (Storage Model, and Services), By Deployment (Private, Public, and Hybrid), By Enterprise Size (SMEs, and Large Enterprises), By Vertical (BFSI, IT and Telecommunication, Government and Public Sector, Manufacturing, Healthcare and Life Sciences, Retail and Consumer Goods, Media and Entertainment, and Others), and Regional Forecast, 2020-2027." The study further mentions that the cloud storage market size was USD 49.13 billion in 2019 and is projected to reach USD 297.54 billion by 2027, exhibiting a CAGR of 25.3% during the forecast period.

Highlights of the Report

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An Overview of the Impact of COVID-19 on this Market:

The emergence of COVID-19 has brought the world to a standstill. We understand that this health crisis has brought an unprecedented impact on businesses across industries. However, this too shall pass. Rising support from governments and several companies can help in the fight against this highly contagious disease. There are some industries that are struggling and some are thriving. Overall, almost every sector is anticipated to be impacted by the pandemic.

We are taking continuous efforts to help your business sustain and grow during COVID-19 pandemics. Based on our experience and expertise, we will offer you an impact analysis of coronavirus outbreak across industries to help you prepare for the future.

Click here to get the short-term and long-term impact of COVID-19 on this Market.Please visit:https://www.fortunebusinessinsights.com/cloud-storage-market-102773

Drivers & Restraints-

Covid-19 Pandemic to Boost Growth Backed by Rising Usage of Cloud Storage Solutions

Cloud storage solutions are gaining more popularity at present as workforces are inclining towards a distributed work environment. These solutions aid workforces in collaborating and staying connected. The outbreak of Covid-19 pandemic is enabling several organizations to support remote working, as well as manage the vast amount of data smoothly. Microsoft, for instance, has surged the benefits of Windows and extended Azure cloud credits for non-profit and critical care organizations, such as food & nutrition, public safety, and health support. In addition to that, the utilization of analytics-driven platforms is helping companies in the generating a large amount of data. They are therefore, preferring hybrid cloud storage solutions more than the conventional ones. However, the occurrence of data breaches may hamper the cloud storage market growth in the coming years.

Segment-

BFSI Segment to Grow Steadily Fueled by Need for Improving Consumer Experience

Based on vertical, the banking, financial services and insurance (BFSI) segment generated 22.4% cloud storage market share in 2019. The industry deals with large volumes of customer data on regular bases. It delivers efficient services to the customers. To serve them better, they require cloud storage technology as it poses as a transformative digital solution. This solution provides a high level of scalability, agility, and data security to the industry. Cloud storage systems not only improve consumer experience and revenues, but also enhance the operational efficiency. These factors are set to drive the growth of the BFSI segment in the near future.

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Regional Analysis-

North America to Remain Dominant Owing to Rising Adoption of Various Digital Services

Regionally, the market is divided into Latin America, Europe, Asia Pacific, the Middle East and Africa, and North America. Amongst these, North America procured USD 19.85 billion revenue in 2019 and is set to dominate the market. This growth is attributable to the rising adoption of several digital services, such as electronic signatures and e-commerce in the U.S. Also, the increasing rate of cybercrime would contribute to the growth. However, the outbreak of Covid-19 pandemic is expected to obstruct growth by affecting the technological investments of industry giants. Asia Pacific, on the other hand, is projected to exhibit an astonishing growth during the forecast period backed by the increasing usage of smartphones.

Competitive Landscape-

Key Companies Focus on Expanding Product Offerings to Surge Revenue

Microsoft, IBM, and Amazon are some of the top companies operating in the global market. They are striving to widen their product offerings by keeping up with the latest trends. They will also be able to surge their revenue this way. Below are two of the latest industry developments:

Fortune Business Insights presents a list of all the companies operating in the global Cloud Storage Market. They are as follows:

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Detailed Table of Content

TOC Continued...!!!

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Have a Look at Related Research Insights:

Cloud Analytics MarketSize, Share & Industry Analysis, By Deployment Type (Public Cloud, Private Cloud, and Hybrid Cloud), By Organization Size (Small And Medium-Sized Enterprises (SMEs) and Large Enterprises), By End-User (BFSI, IT and Telecommunications, Retail and Consumer Goods, Healthcare and Life Sciences, Manufacturing, Education, and Others) and Regional Forecast, 2019-2026

Cloud Computing MarketSize, Share & Industry Analysis, By Type (Public Cloud, Private Cloud, Hybrid Cloud), By Service (Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS)), By Industry (Banking, Financial Services, and Insurance (BFSI), IT and Telecommunications, Government, Consumer Goods and Retail, Healthcare, Manufacturing, Others (Energy and Utilities, Education), and Regional Forecast, 2020-2027

Cloud Gaming MarketSize, Share & Industry Analysis, By Device (Smartphone, Laptop/Tablets, Personal Computer (PC), Smart TV and Consoles), By Streaming Type (File Streaming and Video Streaming), By End-Users (Business to Business (B2B) and Business to Consumers (B2C)), and Regional Forecast, 2020-2027

Cloud security MarketSize, Share & Industry Analysis, By Component (Solutions, Services), By Security Type (Application Security, Database Security, Endpoint Security, Network Security, Web and Email Security), By Deployment (Private, Public, Hybrid), By End-User (Large scale enterprise , Small & medium enterprise), By Industry Verticals (Healthcare, BFSI, IT & Telecom, Government Agencies)Others and Regional Forecast, 2019-2026

Retail Cloud MarketSize, Share & Industry Analysis, By Model Type (Infrastructure as a Service, Platform as a Service and Software as a Service), By Deployment (Public, Private and Hybrid Cloud), By Solution (Supply Chain Management, Workforce Management, Customer Management, Reporting & Analytics, Data Security, Omni-Channel), By Enterprise Size (Small & Medium and Large Enterprise) and Regional Forecast, 2019-2026

Location Analytics MarketSize, Share & Industry Analysis, By Component (Solution, Services), By Location Type (Indoor, Outdoor), By Deployment Type (Cloud, On-Premises), By End-User (Retail, Government, Energy and Utilities, Healthcare, Travel and Transportation, Telecommunications, and Others) and Regional Forecast, 2019-2026

Security Analytics MarketSize, Share & Industry Analysis, By Component (Solutions, and Services), By Application (Network Security Analytics, Web Security Analytics, Endpoint Security Analytics, and Application Security Analytics), By Vertical (BFSI, Government and Defense, IT and Telecommunication, Manufacturing, Healthcare, Energy and Utilities, and Others), and Regional Forecast, 2020-2027

Retail Analytics MarketSize, Share and Industry Analysis By Type (Software, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By Function (Customer Management, Supply Chain, Merchandising, In-Store Operations, and Strategy & Planning) and Regional Forecast 2019-2026

About Us:

Fortune Business Insightsoffers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights, we aim at highlighting the most lucrative growth opportunities for our clients. We therefore offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

Contact Us:Fortune Business Insights Pvt. Ltd.308, Supreme Headquarters,Survey No. 36, Baner,Pune-Bangalore Highway,Pune- 411045, Maharashtra,India.Phone:US: +1-424-253-0390UK: +44-2071-939123APAC: +91-744-740-1245Email:[emailprotected]Fortune Business InsightsLinkedIn|Twitter|Blogs

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Cloud Storage Market to Reach USD 297.54 Billion by 2027; Higher Adoption of Machine Learning to Boost Growth, Says Fortune Business Insights -...

Associations with No Place to Meet Are Turning to JUNO, A Live and On-Demand Digital Platform – AiThority

JUNO is pleased to announce the only all-in-one, live, andon-demand learning platformutilizing four human motivators to engage users and maximize the value of their experience. Gone are the days of multiple platforms, contracts, and vendors to secure ongoing engagement and learning with members. JUNO was built for a post-COVID-19reality. Greater strain and tighter budgets require a flexible solution to handle the New Normal and beyond.

Recommended AI News: Hawaii Signs Participating Addendum with DroneUp Providing Public Sector Agencies Access to Drone Services

JUNO facilitates full user engagement by offering these tools and features that meet the emerging user in their most desired expectations.

Connection: From hybrid to completely digital events, virtual meetings are the wave of the future. In fact, Microsoft teams alone have seen 2.7 billion meeting minutes in one day, a 200 percent increase. JUNO onboards users around interests, strengths, and desired improvement areas and allows machine learning triggers to recommend peer connections, mainstage, and breakout learning opportunities.

Gamification: 60% of all start-ups gamify their user experience because gamificationworks! By triggering real and powerful human emotions, users generate higher levels ofhappiness, intrigue, and excitement resulting in desires to engage further and stay involved longer.From profile building to polls, quizzes, and continued learning, JUNO ensures that every user action has value.

Recommended AI News: 3 Steps To Channel Customer Feedback Into Product Innovation

Business growth: So how will JUNO help your business grow? JUNO supports users and partners by facilitating business connections through live exhibit experiences, digital think-tank sessions, suggested collaboration partnerships, and skills-based visibility tools.

Ongoing learning: Live events must move past the transactional into the transformational. JUNO Creates EQ and IQ learning pathways to engage users on all levels. From certification and badging to goal setting and performance commitments, JUNO offers a diverse set of actions for users to personally develop.

In a time in which what got you here wont get you there, JUNO delivers the get you there solution, Former PCMA CEO, Deborah Sexton.

Recommended AI News: NVIDIA Accelerates Apache Spark, Worlds Leading Data Analytics Platform

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Reality Of Metrics: Is Machine Learning Success Overhyped? – Analytics India Magazine

In one of the most revealing research papers written recent times, the researchers from Cornell Tech and Facebook AI quash the hype around the success of machine learning. They opine and even demonstrate that the trend appears to be overstated. In other words, the so-called cutting edge research or benchmark work perform similarly to one another even if they are a decade apart. In other words, the authors believe that metric learning algorithms have not made spectacular progress.

In this work, the authors try to demonstrate the significance of assessing algorithms more diligently and how few practices can help reflect ML success in reality.

Over the past decade, deep convolutional networks have made tremendous progress. Their application in computer vision is almost everywhere; from classification to segmentation to object detection and even generative models. But is the metric evaluation carried out to track this progress has been leakproof? Are the techniques employed werent affected by the improvement in deep learning methods?

The goal of metric learning is to map data to an embedding space, where similar data are close together, and the rest are far apart. So, the authors begin with the notion that the deep networks have had a similar effect on metric learning. And, the combination of the two is known as deep metric learning.

The authors then examined flaws in the current research papers, including the problem of unfair comparisons and the weaknesses of commonly used accuracy metrics. They then propose a training and evaluation protocol that addresses these flaws and then run experiments on a variety of loss functions.

For instance, one benchmark paper in 2017, wrote the authors, used ResNet50, and then claimed huge performance gains. But the competing methods used GoogleNet, which has significantly lower initial accuracies. Therefore, the authors conclude that much of the performance gain likely came from the choice of network architecture, and not their proposed method. Practices such as these can put ML on headlines, but when we look at how much of these state-of-the-art models are really deployed, the reality is not that impressive.

The authors underline the importance of keeping the parameters constant if one has to prove that a certain new algorithm outperforms its contemporaries.

To carry out the evaluations, the authors introduce settings that cover the following:

As shown in the above plot, the trends, in reality, arent that far from the previous related works and this indicates that those who claim a dramatic improvement might not have been fair in their evaluation.

If a paper attempts to explain the performance gains of its proposed method, and it turns out that those performance gains are non-existent, then their explanation must be invalid as well.

The results show that when hyperparameters are properly tuned via cross-validation, most methods perform similarly to one another. This work, believe the authors, will lead to more investigation into the relationship between hyperparameters and datasets, and the factors related to particular dataset/architecture combinations.

According to the authors, this work exposes the following:

The authors conclude that if proper machine learning practices are followed, then the results of metric learning papers will better reflect reality, and can lead to better works in most impactful domains like self-supervised learning.

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Reality Of Metrics: Is Machine Learning Success Overhyped? - Analytics India Magazine

Democratizing Data-Driven Processes Through AutoML for Better Business Prospects – Analytics Insight

Data Science and Machine Learning are among the most deployed and useful technologies of the current marketplace. And as the utility increases, the new wave of advancements hit the industry with more innovations in its tides. Similarly, to add an extra edge to what Data Science and ML could achieve, we now have AutoML (Automated Machine Learning) platforms. It is among the top trends of contemporary data-market with most of the big techs investing in its successful incorporation. Companies including Google, Amazon, Microsoft have already embraced AutoML in their business processes to accelerate the effectiveness of their operations and products. Considered as a quiet revolution in AI, the technology has transformed the entire data science landscape while offering a great deal to modern-day businesses.

Automated machine learning (AutoML) is the process to automate an end-to-end process of leveraging machine learning algorithms to real-world problems. One of the most peculiar features of the technology is that even people with no data science or ML expertise can work with this platform to carry out desired outcomes.

According to Gartners survey, it takes around 4 years to make an AI project go live which doesnt cope-up with the rising demand and transforming market dynamics. And, according to statistics, huge investments in data and AI projects are only successful 15% of the time. However, with the rise in current trends and the AutoML platform, small AI projects can be produced in a short period of time.

Moreover, the soaring demands for machine learning systems dont imply the successful deployment of ML models across a wide range of applications. Its success requires a proficient team of seasoned data scientists and a team that decides which model is the best for a particular business problem. But the shortage of data science talents has doesnt quite fulfilled the scenario. Here enters the AutoML platform which tends to automate the maximum number of steps in an ML pipeline while reducing the human effort without compromising on the quality of performance.

Have you heard of Mercari? Mercari is a popular online shopping app in Japan. The company uses Googles AutoML tool in order to better process the image classification. Using a UI for uploading photos, Mercaris app can identify and suggest brand names from over 12 major brands through customized AutoML pipeline technology.

Leveraging Googles AutoML platform enabled the company to customize ML models in successfully identifying over 50,000 images with an accuracy of 91.3%.

Moreover, the implementation of automated machine learning across physical retail stores is redefining their future with rich business benefits including better sales forecasting and significant others. Analyzing the available current customer data and purchasing season, the AutoML platform can help retail industry businesses with better sales prospects. This can subsequently reduce the unused inventory costs and waste in unnecessary promotions.

While leveraging the AutoML to enhance business effectiveness and productivity, brands can also improve customer personalization through customization.

For any business across any industry, AutoML is bound to make cost reductions and increase productivity for data scientists while the democratization of machine learning reduces demand for them. The technology also helps accelerate revenues and customer satisfaction. AutoML models with enhanced accuracy possess the capability to improve other, less tangible business results too.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Q&A on the Book Hands-On Genetic Algorithms with Python – InfoQ.com

Key Takeaways

Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.

In addition to our interview, InfoQ was able to obtain a sample chapter which can be downloaded here.

InfoQ: How do genetic algorithms work?

Eyal Wirsansky: Genetic algorithms are a family of search algorithms inspired by the principles of evolution in nature. They imitate the process of natural selection and reproduction, by starting with a set of random solutions, evaluating each one of them, then selecting the better ones to create the next generation of solutions. As generations go by, the solutions we have get better at solving the problem. This way, genetic algorithms can produce high-quality solutions for various problems involving search, optimization, and learning. At the same time, their analogy to natural evolution allows genetic algorithms to overcome some of the hurdles encountered by traditional search and optimization algorithms, especially for problems with a large number of parameters and complex mathematical representations.

InfoQ: What type of problems do genetic algorithms solve?

Wirsansky: Genetic algorithms can be used for solving almost any type of problem, but they particularly shine where traditional algorithms cannot be used, or fail to produce usable results within a practical amount of time. For example, problems with very complex or non-existing mathematical representation, problems where the number of variables involved is large, and problems with noisy or inconsistent input data. In addition, genetic algorithms are better equipped to handle deceptive problems, where traditional algorithms may get trapped in a suboptimal solution.

Genetic algorithms can even deal with cases where there is no way to evaluate an individual solution by itself, as long as there is a way to compare two solutions and determine which of them is better. An example can be a machine learning-based agent that drives a car in a simulated race. A genetic algorithm can optimize and tune the agent by having different versions of it compete against each other to determine which version is better.

InfoQ: What are the best use cases for genetic algorithms?

Wirsansky: The most common use case is where we need to assemble a solution using a combination of many different available parts; we want to select the best combination, but the number of possible combinations is too large to try them all. Genetic algorithms can usually find a good combination within a reasonable amount of time. Examples can be scheduling personnel, planning of delivery routes, designing bridge structures, and also constructing the best machine learning model from many available building blocks, or finding the best architecture for a deep learning model.

Another interesting use case is where the evaluation is based on peoples opinion or response. For example, you can use the genetic algorithm approach to determine the design parameters for a web sitesuch as color palette, font size, and location of components on the pagethat will achieve the best response from customers, such as conversion or retention. This idea can also be used for genetic art artificially created paintings or music that prove pleasant to the human eye (or ear).

Genetic algorithms can also be used for ongoing optimizationcases where the best solution may change over time. The algorithm can run continuously within the changing environment and respond dynamically to these changes by updating the best solution based on the current generation.

InfoQ: How can genetic algorithms select the best subset of features for supervised learning?

Wirsansky: In many cases, reducing the number of featuresused as inputs for a model in supervised learningcan increase the models accuracy, as some of the features may be irrelevant or redundant. This will also result in a simpler, better generalizing model. But we need to figure out which are the features that we want to keep. As this comes down to finding the best combination of features out of a potentially immense number of possible combinations, genetic algorithms provide a very practical approach. Each potential solution is represented by a list of booleans, one for each feature.

The value of the boolean (0 or 1) represents the absence or presence of the corresponding feature. These lists of boolean values are used as genetic material, that can be exchanged between solutions when we mate them, or even mutated by flipping values randomly. Using these mating and mutation operations, we create new generations out of preceding ones, while giving an advantage to solutions that yielded better performing models. After a while, we can have some good solutions, each representing a subset of the features. This is demonstrated in Chapter 7 of the book (our sample chapter) with the UCI Zoo dataset using python code, where the best performance was achieved by selecting six particular features out of the original sixteen.

InfoQ: What are the benefits that we can get from using genetic algorithms with machine learning for hyperparameter tuning?

Wirsansky: Every machine learning model utilizes a set of hyperparametersvalues that are set before the training takes place and affect the way the learning is done. The combined effect of hyperparameters on the performance of the model can be significant. Unfortunately, finding the best combination of the hyperparameter valuesalso known as hyperparameter tuningcan be as difficult as finding a needle in a haystack.

Two common approaches are grid search and random search, each with its own disadvantages. Genetic algorithms can be used in two ways to improve upon these methods. One way is by optimizing the grid search, so instead of trying out every combination on the grid, we can search only a subset of combinations but still get a good combination. The other way is to conduct a full search over the hyperparameter space, as genetic algorithms are capable of handling a large number of parameters as well as different parameter types continuous, discrete and categorical. These two approaches are demonstrated in Chapter 8 of the book with the UCI Wine dataset using python code.

InfoQ: How can genetic algorithms be used in Reinforcement Learning?

Wirsansky: Reinforcement Learning (RL) is a very exciting and promising branch of machine learning, with the potential to handle complex, everyday-life-like tasks. Unlike supervised learning, RL does not present an immediate 'right/wrong' feedback, but instead provides an environment where a longer-term, cumulative reward is sought after. This kind of setting can be viewed as an optimization problem, another area where genetic algorithms excel.

As a result, genetic algorithms can be utilized for reinforcement learning in several different ways. One example can be determining the weights and biases of a neural network that interacts with its environment by mapping input values to output values. Chapter 10 of the book includes two examples of applying genetic algorithms to RL tasks, using the OpenAI Gym environments mountain-car and cart-pole.

InfoQ: What is bio-inspired computing?

Wirsansky: Genetic algorithms are just one branch within a larger family of algorithms called Evolutionary Computation, all inspired by Darwinian evolution. One particularly interesting member of this family is Genetic Programming, that evolves computer programs aiming to solve a specific problem. More broadly, as evolutionary computation techniques are based on various biological systems or behaviors, they can be considered part of the algorithm family known as Bio-inspired Computing.

Among the many fascinating members of this family are Ant Colony Optimizationimitating the way certain species of ants locate food and mark the paths to it, giving advantage to closer and richer locations of food; Artificial Immune Systems, capable of identifying and learning new threats, as well as applying the acquired knowledge and respond faster the next time a similar threat is detected; and Particle Swarm Optimization, based on the behavior of flocks of birds or schools of fish, where individuals within the group work together towards a common goal without central supervision.

Another related, broad field of computation is Artificial Life, involving systems and processes imitating natural life in different ways, such as computer simulations and robotic systems. Chapter 12 of the book includes two relevant Python-written examples, one solving a problem using genetic programming, and the otherusing particle swarm optimization.

Eyal Wirsansky is a senior software engineer, a technology community leader, and an artificial intelligence researcher and consultant. Eyal started his software engineering career as a pioneer in the field of voice over IP, and he now has over 20 years' experience of creating a variety of high-performing enterprise solutions. While in graduate school, he focused his research on genetic algorithms and neural networks. One outcome of his research is a novel supervised machine learning algorithm that combines the two. Eyal leads the Jacksonville (FL) Java user group, hosts the Artificial Intelligence for Enterprise virtual user group, and writes the developer-oriented artificial intelligence blog, ai4java.

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Q&A on the Book Hands-On Genetic Algorithms with Python - InfoQ.com

Bitglass Integrates CrowdStrike’s Machine-Learning Technology to Provide Zero-Day Advanced Threat Protection in the Cloud – Business Wire

CAMPBELL, Calif.--(BUSINESS WIRE)--Bitglass, the Next-Gen Cloud Security Company, announced today that it has partnered with CrowdStrike, a leader in cloud-delivered endpoint protection, to provide an agentless advanced threat protection (ATP) solution that identifies and remediates both known and zero-day threats on any cloud application or service, as well as any device that accesses corporate IT resources (including personal devices).

Cloud applications and bring your own device (BYOD) policies offer organizations enhanced flexibility and efficiency, but they can also serve as proliferation points for malware if not properly secured. This Original Equipment Manufacturer (OEM) offering from CrowdStrike uses machine learning (ML) and deep file inspection to identify malware and other threats. Together with Bitglass Next-Gen Cloud Access Security Broker (CASB), threats are automatically remediated based on preset policies.

Bitglass CASB leverages agentless inline proxies to monitor and mediate traffic between cloud applications and devices in order to enforce granular security policies on data in transit. By incorporating CrowdStrikes detection capabilities directly into Bitglass agentless proxy, the integration can identify and block malware in real time as infected files are uploaded to cloud applications or downloaded onto devices (even personal devices) --without the need for software installations. Additionally, integration with application programming interfaces (APIs) allows for the detection and quarantining of malware already at rest in the cloud.

Once malware makes its way into a cloud app, it can quickly spread into connected apps as well as into users devices, said Anurag Kahol, chief technology officer and co-founder of Bitglass. Consequently, organizations need a multi-faceted solution that can automatically block malware both at rest and in transit. If they wait for IT teams to review and respond to threat notifications, its often too late. Were proud to leverage CrowdStrikes industry-leading technology to deliver a robust cloud ATP solution that stops threats and empowers enterprises to embrace the cloud applications and BYOD policies that spur innovation and productivity.

As a cloud-delivered endpoint protection leader at the forefront of securing organizations from sophisticated tactics, CrowdStrike understands that a successful security strategy lies in the ability to quickly detect, respond and remediate threat activity, said Dr. Sven Krasser, CrowdStrikes chief scientist. By incorporating our machine learning file-scan engine, which is trained leveraging the 3 trillion endpoint-related events processed weekly by the Falcon Platform, with Bitglass unique, agentless architecture, customers gain comprehensive, real-time protection and control over corporate data across all endpoints with reduced risk of exposure.

The solution is fully deployed in the cloud and is completely agentless--requiring no hardware appliances or software installations and ensuring rapid deployment. Additionally, Bitglass Polyscale Architecture scales and adapts to an enterprise's exact needs on the fly. There is no need for backhauling or bottleneck architectures.

For more information, download the joint solution brief here:https://pages.bitglass.com/CD-FY20Q2-CrowdstrikeBitglassSolutionsBrief_LP.html?&utm_source=pr

About Bitglass

Bitglass, the Next-Gen Cloud Security company, is based in Silicon Valley with offices worldwide. The company's cloud security solutions deliver zero-day, agentless, data and threat protection for any app, any device, anywhere. Bitglass is backed by Tier 1 investors and was founded in 2013 by a team of industry veterans with a proven track record of innovation and execution.

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Bitglass Integrates CrowdStrike's Machine-Learning Technology to Provide Zero-Day Advanced Threat Protection in the Cloud - Business Wire

Northern Trust rolls out machine learning tech for FX management solutions – The TRADE News

Northern Trust has deployed machine learning models within its FX currency management solutions business, designed to enable greater oversight of thoughts of daily data points.

The solution has been developed in partnership with Lumint, an outsourced FX execution services provider, and will help buy-side firms reduce risk throughout the currency management lifecycle.

The technology utilised by the Robotic Oversight System (ROSY) for Northern Trust systematically scans newly arriving, anonymised data to identify anomalies across multi-dimensional data sets. It is also built on machine learning models developed by Lumint using a cloud platform that allows for highly efficient data processing.

In a data-intensive business, ROSY acts like an additional member of the team working around the clock to find and flag anomalies. The use of machine learning to detect data outliers enables us to provide increasingly robust and intuitive solutions to enhance our oversight and risk management, which can be particularly important in volatile markets, said Andy Lemon, head of currency management, Northern Trust.

Northern Trust announced astrategic partnership with Lumint in 2018to deliver currency management services with portfolio, share class and lookthrough hedging solutions alongside transparency and analytics tools.

Northern Trusts deployment of ROSY amplifies the scalability of its already highly automated currency hedging operation; especially for the more sophisticated products such as look-through hedging offered to its global clients, added Alex Dunegan, CEO, Lumint.

The solution is the latest rollout of machine learning technology by Northern Trust, as the bank continues to leverage new technologies across its businesses. In August last year, Northern Trust developed a new pricing engine within its securities lending business by utilising machine learning and advanced statistical technology.

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Northern Trust rolls out machine learning tech for FX management solutions - The TRADE News

Machine Learning Operationalization Software Market (2020-2026) | Where Should Participant Focus To Gain Maximum ROI | Exclusive Report By DataIntelo…

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Seeqc UK Awarded 1.8M In Grants To Advance Quantum Computing Initiatives – Quantaneo, the Quantum Computing Source

Seeqc, the Digital Quantum Computing company, today announced its UK team has been selected to receive two British grants totaling 1.8 million from Innovate UKs Industrial Challenge Strategy Fund.

We are looking forward to applying our deep expertise in design, testing and manufacturing of quantum-ready superconductors, along with our resource-efficient approach to qubit control and readout to this collaborative development of quantum circuits.

Quantum Foundry

The first 800,000 grant from Innovate UK is part of a 7M project dedicated to advancing the commercialization of superconducting technology. Its goal is to bring quantum computing closer to business-applicable solutions, cost-efficiently and at scale.

Seeqc UK is joining six UK-based companies and universities in a consortium to collaborate on the initiative. This is the first concerted effort to bring all leading experts across industry and academia together to advance the development of quantum technologies in the UK.

Other grant recipients include Oxford Quantum Circuits, Oxford Instruments, Kelvin Nanotechnology, University of Glasgow and the Royal Holloway University of London.

Quantum Operating System

The second 1 million grant is part of a 7.6 million seven-organization consortium dedicated to advancing the commercialization of quantum computers in the UK by building a highly innovative quantum operating system. A quantum operating system, Deltaflow.OS, will be installed on all quantum computers in the UK in order to accelerate the commercialization and collaboration of the British quantum computing community. The universal operating system promises to greatly increase the performance and accessibility of quantum computers in the UK.

Seeqc UK is joined by other grant recipients, Riverlane, Hitachi Europe, Universal Quantum, Duality Quantum Photonics, Oxford Ionics, and Oxford Quantum Circuits, along with UK-based chip designer, ARM, and the National Physical Laboratory.

Advancing Digital Quantum Computing

Seeqc owns and operates a multi-layer superconductive electronics chip fabrication facility, which is among the most advanced in the world. The foundry serves as a testing and benchmarking facility for Seeqc and the global quantum community to deliver quantum technologies for specific use cases. This foundry and expertise will be critical to the success of the grants. Seeqcs Digital Quantum Computing solution is designed to manage and control qubits in quantum computers in a way that is cost-efficient and scalable for real-world business applications in industries such as pharmaceuticals, logistics and chemical manufacturing.

Seeqcs participation in these new industry-leading British grants accelerates our work in making quantum computing useful, commercially and at scale, said Dr. Matthew Hutchings, chief product officer and co-founder at Seeqc, Inc. We are looking forward to applying our deep expertise in design, testing and manufacturing of quantum-ready superconductors, along with our resource-efficient approach to qubit control and readout to this collaborative development of quantum circuits.

We strongly support the Deltaflow.OS initiative and believe Seeqc can provide a strong contribution to both consortiums work and advance quantum technologies from the lab and into the hands of businesses via ultra-focused and problem-specific quantum computers, continued Hutchings.

Seeqcs solution combines classical and quantum computing to form an all-digital architecture through a system-on-a-chip design that utilizes 10-40 GHz superconductive classical co-processing to address the efficiency, stability and cost issues endemic to quantum computing systems.

Seeqc is receiving the nearly $2.3 million in grant funding weeks after closing its $6.8 million seed round from investors including BlueYard Capital, Cambium, NewLab and the Partnership Fund for New York City. The recent funding round is in addition to a $5 million investment from M Ventures, the strategic corporate venture capital arm of Merck KGaA, Darmstadt, Germany.

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Seeqc UK Awarded 1.8M In Grants To Advance Quantum Computing Initiatives - Quantaneo, the Quantum Computing Source

Quantum computing will (eventually) help us discover vaccines in days – VentureBeat

The coronavirus is proving that we have to move faster in identifying and mitigating epidemics before they become pandemics because, in todays global world, viruses spread much faster, further, and more frequently than ever before.

If COVID-19 has taught us anything, its that while our ability to identify and treat pandemics has improved greatly since the outbreak of the Spanish Flu in 1918, there is still a lot of room for improvement. Over the past few decades, weve taken huge strides to improve quick detection capabilities. It took a mere 12 days to map the outer spike protein of the COVID-19 virus using new techniques. In the 1980s, a similar structural analysis for HIV took four years.

But developing a cure or vaccine still takes a long time and involves such high costs that big pharma doesnt always have incentive to try.

Drug discovery entrepreneur Prof. Noor Shaker posited that Whenever a disease is identified, a new journey into the chemical space starts seeking a medicine that could become useful in contending diseases. The journey takes approximately 15 years and costs $2.6 billion, and starts with a process to filter millions of molecules to identify the promising hundreds with high potential to become medicines. Around 99% of selected leads fail later in the process due to inaccurate prediction of behavior and the limited pool from which they were sampled.

Prof. Shaker highlights one of the main problems with our current drug discovery process: The development of pharmaceuticals is highly empirical. Molecules are made and then tested, without being able to accurately predict performance beforehand. The testing process itself is long, tedious, cumbersome, and may not predict future complications that will surface only when the molecule is deployed at scale, further eroding the cost/benefit ratio of the field. And while AI/ML tools are already being developed and implemented to optimize certain processes, theres a limit to their efficiency at key tasks in the process.

Ideally, a great way to cut down the time and cost would be to transfer the discovery and testing from the expensive and time-inefficient laboratory process (in-vitro) we utilize today, to computer simulations (in-silico). Databases of molecules are already available to us today. If we had infinite computing power we could simply scan these databases and calculate whether each molecule could serve as a cure or vaccine to the COVID-19 virus. We would simply input our factors into the simulation and screen the chemical space for a solution to our problem.

In principle, this is possible. After all, chemical structures can be measured, and the laws of physics governing chemistry are well known. However, as the great British physicist Paul Dirac observed: The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.

In other words, we simply dont have the computing power to solve the equations, and if we stick to classical computers we never will.

This is a bit of a simplification, but the fundamental problem of chemistry is to figure out where electrons sit inside a molecule and calculate the total energy of such a configuration. With this data, one could calculate the properties of a molecule and predict its behavior. Accurate calculations of these properties will allow the screening of molecular databases for compounds that exhibit particular functions, such as a drug molecule that is able to attach to the coronavirus spike and attack it. Essentially, if we could use a computer to accurately calculate the properties of a molecule and predict its behavior in a given situation, it would speed up the process of identifying a cure and improve its efficiency.

Why are quantum computers much better than classical computers at simulating molecules?

Electrons spread out over the molecule in a strongly correlated fashion, and the characteristics of each electron depend greatly on those of its neighbors. These quantum correlations (or entanglement) are at the heart of the quantum theory and make simulating electrons with a classical computer very tricky.

The electrons of the COVID-19 virus, for example, must be treated in general as being part of a single entity having many degrees of freedom, and the description of this ensemble cannot be divided into the sum of its individual, distinguishable electrons. The electrons, due to their strong correlations, have lost their individuality and must be treated as a whole. So to solve the equations, you need to take into account all of the electrons simultaneously. Although classical computers can in principle simulate such molecules, every multi-electron configuration must be stored in memory separately.

Lets say you have a molecule with only 10 electrons (forget the rest of the atom for now), and each electron can be in two different positions within the molecule. Essentially, you have 2^10=1024 different configurations to keep track of rather just 10 electrons which would have been the case if the electrons were individual, distinguishable entities. Youd need 1024 classical bits to store the state of this molecule. Quantum computers, on the other hand, have quantum bits (qubits), which can be made to strongly correlate with one another in the same way electrons within molecules do. So in principle, you would need only about 10 such qubits to represent the strongly correlated electrons in this model system.

The exponentially large parameter space of electron configurations in molecules is exactly the space qubits naturally occupy. Thus, qubits are much more adapted to the simulation of quantum phenomena. This scaling difference between classical and quantum computation gets very big very quickly. For instance, simulating penicillin, a molecule with 41 atoms (and many more electrons) will require 10^86 classical bits, or more bits than the number of atoms in the universe. With a quantum computer, you would only need about 286 qubits. This is still far more qubits than we have today, but certainly a more reasonable and achievable number.The COVID-19 virus outer spike protein, for comparison, contains many thousands of atoms and is thus completely intractable for classical computation. The size of proteins makes them intractable to classical simulation with any degree of accuracy even on todays most powerful supercomputers. Chemists and pharma companies do simulate molecules with supercomputers (albeit not as large as the proteins), but they must resort to making very rough molecule models that dont capture the details a full simulation would, leading to large errors in estimation.

It might take several decades until a sufficiently large quantum computer capable of simulating molecules as large as proteins will emerge. But when such a computer is available, it will mean a complete revolution in the way the pharma and the chemical industries operate.

The holy grail end-to-end in-silico drug discovery involves evaluating and breaking down the entire chemical structures of the virus and the cure.

The continued development of quantum computers, if successful, will allow for end-to-end in-silico drug discovery and the discovery of procedures to fabricate the drug. Several decades from now, with the right technology in place, we could move the entire process into a computer simulation, allowing us to reach results with amazing speed. Computer simulations could eliminate 99.9% of false leads in a fraction of the time it now takes with in-vitro methods. With the appearance of a new epidemic, scientists could identify and develop a potential vaccine/drug in a matter of days.

The bottleneck for drug development would then move from drug discovery to the human testing phases including toxicity and other safety tests. Eventually, even these last stage tests could potentially be expedited with the help of a large scale quantum computer, but that would require an even greater level of quantum computing than described here. Tests at this level would require a quantum computer with enough power to contain a simulation of the human body (or part thereof) that will screen candidate compounds and simulate their impact on the human body.

Achieving all of these dreams will demand a continuous investment into the development of quantum computing as a technology. As Prof. Shohini Ghose said in her 2018 Ted Talk: You cannot build a light bulb by building better and better candles. A light bulb is a different technology based on a deeper scientific understanding. Todays computers are marvels of modern technology and will continue to improve as we move forward. However, we will not be able to solve this task with a more powerful classical computer. It requires new technology, more suited for the task.

(Special thanks Dr. Ilan Richter, MD MPH for assuring the accuracy of the medical details in this article.)

Ramon Szmuk is a Quantum Hardware Engineer at Quantum Machines.

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Quantum computing will (eventually) help us discover vaccines in days - VentureBeat

Quantum Computing Market Research Report 2020 By Size, Share, Trends, Analysis and Forecast to 2026 – Cole of Duty

1qb Information Technologies

Quantum Computing Market Competitive Analysis:

In addition, the projections offered in this report were derived using proven research assumptions and methods. In this way, the Quantum Computing research study offers a collection of information and analysis for every facet of the Quantum Computing market such as technology, regional markets, applications and types. The Quantum Computing market report also offers some market presentations and illustrations that include pie charts, diagrams and charts that show the percentage of different strategies implemented by service providers in the Quantum Computing market. In addition, the report was created using complete surveys, primary research interviews, observations and secondary research.

In addition, the Quantum Computing market report introduced the market through various factors such as classifications, definitions, market overview, product specifications, cost structures, manufacturing processes, raw materials and applications. This report also provides key data on SWOT analysis, return data for investments and feasibility analysis for investments. The Quantum Computing market study also highlights the extremely lucrative market opportunities that are influencing the growth of the global market. In addition, the study offers a complete analysis of market size, segmentation and market share. In addition, the Quantum Computing report contains market dynamics such as market restrictions, growth drivers, opportunities, service providers, stakeholders, investors, important market participants, profile assessment and challenges of the global market.

Quantum Computing Market Segments:

The report also underscores their strategics planning including mergers, acquisitions, ventures, partnerships, product launches, and brand developments. Additionally, the report renders the exhaustive analysis of crucial market segments, which includes Quantum Computing types, applications, and regions. The segmentation sections cover analytical and forecast details of each segment based on their profitability, global demand, current revue, and development prospects. The report further scrutinizes diverse regions including North America, Asia Pacific, Europe, Middle East, and Africa, and South America. The report eventually helps clients in driving their Quantum Computing business wisely and building superior strategies for their Quantum Computing businesses.

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Table of Content

1 Introduction of Quantum Computing Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Quantum Computing Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Quantum Computing Market, By Deployment Model

5.1 Overview

6 Quantum Computing Market, By Solution

6.1 Overview

7 Quantum Computing Market, By Vertical

7.1 Overview

8 Quantum Computing Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Quantum Computing Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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Quantum Computing Market Research Report 2020 By Size, Share, Trends, Analysis and Forecast to 2026 - Cole of Duty

Highest-performing quantum simulator IN THE WORLD delivered to Japan – TechGeek

Atos, a global leader in digital transformation, introduced the worlds first commercially available quantum simulator capable of simulating up to 40 quantum bits, or Qubits, which translates to very fucking fast.

The simulator, named Atos Quantum Learning Machine, is powered by an ultra-compact supercomputer and a universal programming language.

Quantum computing is a key priority for Japan. It launched a dedicated ten-year, 30 billion yen (.. aka US$280 million / AUD$433 million) quantum research program in 2017, followed by a 100 billion yen (.. aka US$900 million / AUD $1 billion) investment into its Moonshot R&D Program one focus of which will be to create a fault-tolerant universal quantum computer to revolutionise the economy, industry, and security sectors by 2050.

Were delighted to have sold our first QLM in Japan, thanks to our strong working partnership with Intelligent Wave Inc.. We are proud to be part of this growing momentum as the country plans to boost innovation through quantum

Combining a high-powered, ultra-compact machine with a universal programming language, the Atos Quantum Learning Machine (enables researchers and engineers to develop an experiment with quantum software. It is the worlds only quantum software development and simulation appliance for the coming quantum computer era.

It simulates the laws of physics, which are at the very heart of quantum computing, to compute the exact execution of a quantum program with double-digit precision.

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Highest-performing quantum simulator IN THE WORLD delivered to Japan - TechGeek

America Is in a New Cold War and This Time the Communists Might Win – Newsweek

It had been a bedrock belief of U.S. policy for 40 years that it was possible to bring the People's Republic of China smoothly into the family of nationsand now, one of the architects of that policy was finally acknowledging the obvious.

In a speech six months ago, former World Bank President and Deputy Secretary of State Robert Zoellick reminded listeners of his own famous 2005 call on Beijing to become a "responsible stakeholder." He ticked off a few of the ways in which China had done just that: voting for sanctions on North Korea and limiting missile exports, for instance. But he acknowledged that the project had gone off the rails.

"Xi Jinping's leadership," Zoellick said of the PRC president, "has prioritized the Communist Party and restricted openness and debate in China. China hurts itself by forging a role model for dystopian societies of intrusive technologies and reeducation camps." He added: "The rule of law and openness upon which Hong Kong's 'One Country, Two Systems' model rests may topple or be trampled. If China crushes Hong Kong, China will wound itselfeconomically and psychologicallyfor a long time."

Zoellick had that right. A global pandemic has brought relations between Beijing and Washington to its lowest point since China reopened to the world in 1978even lower even than in those extraordinary days following the 1989 Tiananmen massacre.

What had been a more confrontational, trade-centric relationship since the start of President Donald Trump's term, has now descended into bitterness in the midst of a presidential reelection campaign Trump fears is slipping away. Any chance that the pandemic might spur Washington and Beijing to set differences aside and work together on treatments and other aspects of the pandemicsuch as how exactly it startedis long gone.

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Last week, the Trump administration moved to block shipments of semiconductors to Huawei Technologies. The Commerce Department said it was amending an export rule to "strategically target Huawei's acquisition of semiconductors that are the direct product of certain U.S. software and technology." Previously on May 13, the FBI announced an investigation into Chinese hackers that it believes are targeting American health care and pharmaceutical companies in an effort to steal intellectual property relating to coronavirus medicines. Without specifying how, the Bureau said the hacks may be disrupting progress on medical research.

President Trump had already made it clear just how bitter he is at Beijing on May 7 when meeting with reporters at the White House. "We went through the worst attack we've ever had on our country," he said, "this is the worst attack we've ever had. This is worse than Pearl Harbor, this is worse than the World Trade Center. There's never been an attack like this. And it should have never happened. Could've been stopped at the source. Could've been stopped in China...And it wasn't."

The comparison of a virus, which originated in China and then spread globally, to the two most infamous attacks in U.S. history, stunned Trump's foreign policy adviserseven Beijing hard-liners. It will be impossible, U.S. officials acknowledge, for Trump to soften his hard line toward Beijing should he win reelection in November.

The president is right to reach for historical metaphor, given the weight of the moment. But the aftermath of the Wuhan outbreak more closely resembles the building of the Berlin Wall in 1961 than either Pearl Harbor or 9/11. What follows will not be a sharp burst of savage conflict, but a global scramble to shape the new order rising from the rubble of old. As with the Wall, the forces that led to the dispute over the Wuhan outbreak were unleashed years before the events that made history. And the change they represent is likely irreversible, no matter who sits in the White House.

Though Joe Biden has on occasion downplayed Beijing's rise as a threat to the U.S., and for sure would not be so rhetorically reckless as Trump, his foreign policy advisers acknowledge there's no turning back. Since Xi Jinping came to power seven years ago, China has imprisoned more than one million ethnic Muslims in "reeducation" camps, imposed an ever-tightening surveillance state on its own citizens and cracked down on all dissent. Overseas, Beijing's goal is to entice authoritarian regimes in the developing world to view it as a "model'' to be followed. And, of course, selling the technology those leaders need to create their own surveillance states.

"No one on either side of the political aisle in Washington is ignoring any of that," says one Biden adviser. "The era of hope that China might evolve into a normal country is over. No one with any brains denies that."

That notion has fully settled in here. Sixty-six percent of Americans now have a negative view of China, according to a recent Pew Research poll. At the same time, in China, state-owned media and a government-controlled internet whip up nationalism and anti-Americanism to levels unseen since the U.S. accidentally bombed Beijing's embassy in Belgrade during the Balkan wars in 1999.The world's two most powerful nations are now competing in every realm possible: militarily, for one, with constant cat-and-mouse games in the South China Sea and cyber warfare. The competition to dominate the key technologies of the 21st century is intensifying, too. This type of rivalry hasn't been seen since the Soviet Union collapsed in 1991.

Thus, a growing number of policymakers, current and former, and China hands old and new, acknowledge the obvious: Cold War 2.0 is here. To the generation of Americans who remember duck-and-cover drills in elementary school at the peak of the Cold War with the Soviet Union, the new global struggle will look very different. It will also, many U.S. strategists believe, be much harder for the West to wage successfully. "Another long twilight struggle may be upon us," says former Pentagon China planner Joseph Bosco, "and it may make the last one look easy."Now, U.S. policymakers are trying to discern what that struggle will look like, and how to win it.

New-Age War

The first major difference in the coming Cold War with Beijing is in the military realm. Beijing spends far less than the U.S. on its military, though its annual rate of spending is fast increasing. According to the Center For Strategic and International Studies, a Washington think tank, Beijing spent $50 billion on its military in 2001, the year it joined the World Trade Organization. In 2019 it spent $240 billion, compared to the U.S.' $633 billion.

For a few decades at least, the U.S.-China military competition will look vastly different from the hair-trigger nuclear standoff with Moscow. Instead, China will seek asymmetric advantages, rooted where possible in technology. It has, for example, already developed an arsenal of hypersonic missiles, which fly low and are hard for radar to detect. They are known as "carrier killers" because of their ability to strike U.S. aircraft carriers in the Pacific from long distances. These weapons could be critical in "area denial" operations, as military planners put it. For example, should the day come when Beijing seeks to take Taiwan by force, hyper-sonics could keep U.S. carriers far from the island nation once a war began.

China's pursuit of preeminence across a wide range of technologies, in areas like quantum computing and artificial intelligence, are central to the economic clash with the U.S. But they also have significant military components. Since the 1990s, when Chinese military planners were stunned by the U.S.' lightning victory in the first Iraq war, they have consistently focused their efforts on developing war-fighting capabilities relevant to their immediate strategic goalsTaiwan is an examplewhile creating the ability to one day leapfrog U.S. military technologies.That may be drawing nearer. Quantum computing is an example. In an era in which digital networks underpin virtually every aspect of war, "quantum is king," says Elsa Kania, a former DOD official who is now a Senior Fellow at the Center for a New American Security. Take cyber warfarethe ability to protect against an enemy disrupting your own networks, while maintaining the ability to disrupt the adversary's. Quantum networks are far more secure against cyber espionage, and Kania believes China's "future quantum capacity has the potential to leapfrog U.S. cyber capabilities."

That's not the only advantage of quantum technology. Beijing is also exploring the potential for quantum-based radar systems that can defeat stealth technology, a critical U.S. war-fighting advantage. "These disruptive technologiesquantum communications, quantum computing and potentially quantum radarmay have the potential to undermine cornerstones of U.S. technological dominance in information-age warfare, its sophisticated intelligence apparatus, satellites and secure communications networks and stealth technologies," says Kania. "China's concentrated pursuit of quantum technologies could have much more far-reaching impacts than the asymmetric approach to defense that has characterized its strategic posture thus far." That is a big reason why Pan Jianwei, the father of China's quantum computing research effort, has said the nation's goal is nothing less than "quantum supremacy."

Washington, and its allies in East Asia and Europe, are paying attention. In a just-published bookThe Dragons and the Snakes: How the Rest Learned to Fight the WestDavid Kilcullen, a former Australian military officer who served as special adviser to U.S. General David Petraeus in Iraq, writes: "our enemies have caught up or overtaken us in critical technologies, or have expanded their concept of war beyond the narrow boundaries within which our traditional approach can be brought to bear. They have adapted, and unless we too adapt, our decline is only a matter of time."

The book is being widely read in U.S. national security circles.China is not yet a "peer power," as U.S. national defense analysts put it. But the steadily aggressive pursuit of quantum technologiesand a wide array of others that also have dual-use applicationsincreasingly convince Pentagon planners that Beijing will one day be one. China, says Michael Pillsbury, one of Trump's key informal advisers on relations with Beijing, "is nothing if not patient." The year 2049 will mark the Chinese Communist Party's 100th anniversary of taking power in Beijing. That's the year Chinese propaganda outlets have said will see the completion of China's rise to the dominant power on earth.

An Economic Divorce?

The most significant difference in the emerging geopolitical standoff between Washington and Beijing is obvious: China is economically powerful, and deeply integrated with both the developed and developing worlds. That was never the case with the former Soviet Union, which was largely isolated economically, trading only with its east bloc neighbors. China, by contrast, trades with everyone, and it continues to grow richer. It is sophisticated across a wide range of critical technologies, including telecommunications and artificial intelligence. It has set as a national goalin its so-called Made in China 2025 planpreeminence not just in quantum computing and AI, but in biotech, advanced telecommunications, green energy and a host of others.

But the U.S. and the rest of the world have problems in the present as well. The pandemic has exposed the vulnerability of locating supply chains for personal protective equipment as well as pharmaceutical supplies in China. That's a significant strategic vulnerability. If China shut the door on exports of medicines and their key ingredients and raw material, U.S. hospitals, military hospitals and clinics would cease to function within months if not days, says Rosemary Gibson, author of a book on the subject, China Rx. Late last month, Arkansas Senator Tom Cotton introduced legislation mandating that U.S. pharmaceutical companies bring production back from China to the U.S.

China's explicit desire to dominate the industries of the future is bad news for foreign multinational companies that have staked so much on the allure of the China market. If China's steep rise up the technology ladder continues, American and other foreign multinationals are likely to get turfed out of the market entirely. "China 2025 is all about replacing anything that American companies sell of any value, just taking the Americans out of that," says Stewart Paterson, author of China, Trade and Power, Why the West's Economic Engagement Has Failed.Donald Trump's tariffs, and China's public desire to dominate key industries, have pushed American multinational and U.S. policymakers to ask: should the U.S. get an economic divorce from Beijing? And if so, what would that look like?

The COVID-19 outbreak and China's response to it has greatly intensified that debate. Trump's trade war had triggered a slow-motion move toward an economic "decoupling," as companies in low-tech, low- margin industries began to move production out of China to avoid tariffs. The textile, footwear and furniture business have all seen significant movement out of China so far. But pre-pandemic, there was no mad rush for the exits and there was no reason to expect one anytime soon. As recently as last October, 66 percent of American companies operating in China surveyed by the American Chamber of Commerce in Beijing said "decoupling" would be impossible, so interlinked are the world's two largest economies.

Things have changed. The number who now believe decoupling is impossible, according to the same survey, has dropped to 44 percent. If reelected, Trump's advisers say, the president will likely pressure other industries beyond pharmaceuticals and medical equipment to bring back production. How he would actually do that is unclear, but aides are looking at the example of Japan. The Japanese legislature recently approved a program in which the government will offer subsidiesup to $2.25 billion worthto any company that brings its supply chain back home.

As negative perceptions of China harden in the U.S., executives are faced with a stark choice: as Paterson puts it, "do you really want to be seen doing business with an adversary?"

The answer isn't that easy. In the U.S., a lot of companies simply do not want to reduce their exposure to China. They spent yearsand billionsbuilding up supply lines and are loath to give them up. Consider the semiconductor industry, a critical area in which the U.S. is still technologically more advanced than China. A complete cessation of semiconductor sales to China would mean U.S. firms lose about 18 percent of their global market shareand an estimated 37 percent of overall revenues. That in turn would likely force reductions in research and development. The U.S. spent $312 billion on R&D over the last decade, more than double the amount spent by its foreign competitorsand it's that R&D which allows them to stay ahead of competitors.

Paterson argues that the costs of total divorce from China is often overstated. He calculates that about 2 percent of U.S. corporate profits come from sales in the Chinese market, mostly from companies that manufacture there in order to sell there. Corporate profits overall are 10 percent of U.S. GDP. Eliminating the China portion of that "is a rounding error," he says.

But getting companies such as Caterpillar Inc., which operates 30 factories in China and gets 10 percent of its annual revenue from sales there, is an uphill lift. There are scores of companies like Caterpillar, who have no intention of leaving China, even if relations between Washington and Beijing are at new lows. And there are also scores of companies like Starbucks, which operates 42,000 stores across China, or Walmart, whose revenue in the country is more than $10 billion annually. Those companies don't have critical technology to steal and may be little worry to the U.S. if they continue to operate in China.

But other companies do. Tesla, to take one example, is a company whose advanced technology should be protected at all costs. Which is why some in Washington are scratching their heads at both Elon Musk and the Trump administration. Musk said on May 10th that he was so angry at the shutdown orders in the state of California, he might move the Tesla factory in Fremont to Texas. Meanwhile, he manufactures his cars in Shanghai, which is an obvious target for intellectual property theft and industrial espionage, given that electric vehicles are one of the industries targeted in the China 2025 plan. "California bad, Shanghai good is not a formulation that's going to hold up well in the post-COVID environment," says Paterson.

A smarter U.S. strategy than "divorce" is "economic distancing," says John Lee, a Senior Fellow at the Hudson Institute, a Washington think tank. The goal of U.S. industrial policy should be "ensuring that China is not in a position to dominate key technologies and assume the leading role in dominating supply and value chains for these emerging technologies," he says. Rationing access to large and advanced markets is critical. "It becomes much more challenging [for Beijing] if China's access to markets in the U.S. Europe and East Asia is restricted, and it is denied key inputs from those areas."

That presumes coordination with allies, which has not been a Trump administration strong suit. But that would change under a President Joe Biden. Even before the pandemic, key European and Asian allies were souring on their relations with China. That includes Canada as well. A former senior Canadian official said Ottawa wanted to work with Trump and the Europeans to map out a tougher, united front on trade. The only problem? "You were sanctioning our steel exports on 'national security grounds,'" this official says. "We are a NATO ally, for godssake!"

The opportunity to work more closely to form a united front versus Beijing is something Biden advisers are intent on doing. A reconfigured Trans Pacific Partnership, which Barack Obama pushed, is likely the first order of business in a Biden administrationthis time more explicitly targeted at excluding Beijing from free trade deals among U.S. allies.That is, if there is a Biden administration.

What's Next?

In the context of the new Cold War, the move toward a smart economic distancing, as Hudson's Lee and others call for, will gain momentum. "Washington put too much faith in its power to shape China's trajectory. All sides of the policy debate [in the U.S.] erred," says Kurt Campbell, former assistant secretary of state under Obama. Biden's people are already spreading the word that there will be no return to the laissez faire attitudes that governed Washington's approach to China. The U.S. may also have to overtly subsidize companies in the Made in China 2025 industries that Beijing has targeted.

Beijing had resisted suspending its own industrial subsidies to state-owned industries in the Trump trade negotiations and had shown few signs of backing off from the goals expressed in Made in China 2025. In the wake of the global fury kicked up by the coronavirus, an economic rapprochement appears unthinkable.Militarily and geopolitically, no matter who wins the next election, the U.S. will work hard to bring India, which has hedged its bets between Washington and Beijing as China rose, more closely into the fold of a "free and open Indo-Pacific," as the Trump administration has called its policy toward Asia. The ability to work more closely with allies, both in East Asia and in Europe, in creating a united front against Beijing has never been stronger.

"No one that we talk to is happy," says Rand Corporation's Scott Harold.

What many look for is steadier and clearer public messaging from Washington. As Harold puts it, as the ideological competition with Beijing intensifies, "the defenders of the liberal international order, like-minded democracies, should grow more active in defense of their interests and values.''

In the wake of the pandemic, the U.S. is suffering a defeat that should be unthinkable: it is losing the propaganda war, particularly in the developing world. Both internally and abroad, the Chinese Communist party's propaganda outlets, digital and broadcast, are trumpeting Xi Jinping's handling of COVID-19, and contrasting it with the Trump administration's shambolic efforts to deal with the virus. State media outlets chronicled how badly the U.S. and others have managed the crisis. Their message: Those countries should copy China's model.

As competition between the United States and China grows, the information wars will be critical. In this, the "America First" Trump administration has been mostly AWOLthe President has not been able to rouse himself to support pro-democracy demonstrators in Hong Kong, so desperate was he for a trade deal with Xi Jinping. But, Trump and Biden have some good role models and, thus, there's hope. U.S. presidents have defended the country's values quite well, and steadily, throughout the last Cold War, none more ably than Ronald Reagan, who left office a year before the Berlin Wall came down.

We will see, of course, if the next administration is up for the fight. Washington has at least recognized, as Kurt Campbell observes, that it overvalued its ability to influence China's development" Presumably it won't make that mistake again. Instead, Washington and its allies need to focus more on how to cope effectively with a powerful rival.

The mission: Wage the 21st century's Cold War, while ensuring it never turns hot.

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America Is in a New Cold War and This Time the Communists Might Win - Newsweek

Drug Use is Transmitted from Old to Young – UPJ Athletics

Up until now, research into the demographics of drug use has focused more on age, finding that midlife is the riskiest time for drug-related death, but Burke and colleagues saw that the year a person was born also has a large effect.

These phases map onto the previously identified drug waves that came with the waxing and waning popularity of prescription opioids, heroin and fentanyl, each in turn.

Peering within each generation, Jalal and colleagues saw a steady march toward greater overdose risk at younger ages for each successive birth year, which they found quite surprising.

Theres no reason why the lines should be fanning like this, Jalal said. If you look at breast cancer, for example, or any other mortality curves, they dont look like that.

Its not clear why this is happening, Jalal said, but the pattern is too clean to chalk up to chance. And an overall rise in drug overdose deathsalthough that is happening in the background of these datadoes not explain away the results presented in this study.

Burke uses an analogy borrowed from infectious diseases to explain the progressive shift of drug overdose deaths to younger ages.

Burke hopes that the highly regular patterns uncovered in this analysis will give policy makers a tool for testing whether their measures to curb drug overdose deaths are working over the long termany effective intervention should disrupt the pattern.

Additional authors on the study are Jeanine Buchanich, David Sinclair and Mark Roberts, all of Pitt Public Health. Funding was provided by theNational Center for Advancing Translational Sciencesand theRobert Wood Johnson Foundation.

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Drug Use is Transmitted from Old to Young - UPJ Athletics

Quantum computing analytics: Put this on your IT roadmap – TechRepublic

Quantum is the next step toward the future of analytics and computing. Is your organization ready for it?

Quantum computing can solve challenges that modern computers can't--or it might take them a billion years to do so. It can crack any encryption and make your data completely safe. Google reports that it has seen a quantum computer that performed at least 100 million times faster than any classical computer in its lab.

Quantum blows away the processing of data and algorithms on conventional computers because of its ability to operate on electrical circuits that can be in more than one state at once. A quantum computer operates on Qubits (quantum bits) instead of on the standard bits that are used in conventional computing.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

Quantum results can quickly make an impact on life science and pharmaceutical companies, for financial institutions evaluating portfolio risks, and for other organizations that want to expedite time-to-results for processing that on conventional computing platforms would take days to complete.

Few corporate CEOs are comfortable trying to explain to their boards what quantum computing is and why it is important to invest in it.

"There are three major areas where we see immediate corporate engagement with quantum computing," said Christopher Savoie, CEO and co-founder of Zapata Quantum Computing Software Company, a quantum computing solutions provider backed by Honeywell. "These areas are machine learning, optimization problems, and molecular simulation."

Savoie said quantum computing can bring better results in machine learning than conventional computing because of its speed. This rapid processing of data enables a machine learning application to consume large amounts of multi-dimensional data that can generate more sophisticated models of a particular problem or phenomenon under study.

SEE: Forget quantum supremacy: This quantum-computing milestone could be just as important (TechRepublic)

Quantum computing is also well suited for solving problems in optimization. "The mathematics of optimization in supply and distribution chains is highly complex," Savoie said. "You can optimize five nodes of a supply chain with conventional computing, but what about 15 nodes with over 85 million different routes? Add to this the optimization of work processes and people, and you have a very complex problem that can be overwhelming for a conventional computing approach."

A third application area is molecular simulation in chemistry and pharmaceuticals, which can be quite complex.

In each of these cases, models of circumstances, events, and problems can be rapidly developed and evaluated from a variety of dimensions that collate data from many diverse sources into a model.

SEE:Inside UPS: The logistics company's never-ending digital transformation (free PDF)(TechRepublic)

"The current COVID-19 crisis is a prime example," Savoie said. "Bill Gates knew in 2015 that handling such a pandemic would present enormous challengesbut until recently, we didn't have the models to understand the complexities of those challenges."

For those engaging in quantum computing and analytics today, the relative newness of the technology presents its own share of glitches. This makes it important to have quantum computing experts on board. For this reason, most early adopter companies elect to go to the cloud for their quantum computing, partnering with a vendor that has the specialized expertise needed to run and maintain quantum analytics.

SEE: Rural America is in the midst of a mental health crisis. Tech could help some patients see a way forward. (cover story PDF) (TechRepublic)

"These companies typically use a Kubernetes cluster and management stack on premises," Savoie said. "They code a quantum circuit that contains information on how operations are to be performed on quantum qubits. From there, the circuit and the prepared data are sent to the cloud, which performs the quantum operations on the data. The data is processed in the cloud and sent back to the on-prem stack, and the process repeats itself until processing is complete."

Savoie estimated that broad adoption of quantum computing for analytics will occur within a three- to five-year timeframe, with early innovators in sectors like oil and gas, and chemistry, that already understand the value of the technology and are adopting sooner.

"Whether or not you adopt quantum analytics now, you should minimally have it on your IT roadmap," Savoie said. "Quantum computing is a bit like the COVID-19 crisis. At first, there were only two deaths; then two weeks later, there were ten thousand. Quantum computing and analytics is a highly disruptive technology that can exponentially advance some companies over others."

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

Image: sakkmesterke, Getty Images/iStockphoto

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Quantum computing analytics: Put this on your IT roadmap - TechRepublic

Seeqc UK Awarded 1.8M in Grants to Advance Quantum Computing Initiatives – HPCwire

LONDON Seeqc, the Digital Quantum Computing company, announced its UK team has been selected to receive two British grants totaling 1.8 million (~$2.1 million) from Innovate UKs Industrial Challenge Strategy Fund.

Quantum Foundry

The first 800,000 grant from Innovate UK is part of a 7M project dedicated to advancing the commercialization of superconducting technology. Its goal is to bring quantum computing closer to business-applicable solutions, cost-efficiently and at scale.

Seeqc UK is joining six UK-based companies and universities in a consortium to collaborate on the initiative. This is the first concerted effort to bring all leading experts across industry and academia together to advance the development of quantum technologies in the UK.

Othergrant recipientsinclude Oxford Quantum Circuits, Oxford Instruments, Kelvin Nanotechnology, University of Glasgow and the Royal Holloway University of London.

Quantum Operating System

The second 1 million grant is part of a 7.6 million seven-organization consortium dedicated to advancing the commercialization of quantum computers in the UK by building a highly innovative quantum operating system. A quantum operating system, Deltaflow.OS, will be installed on all quantum computers in the UK in order to accelerate the commercialization and collaboration of the British quantum computing community. The universal operating system promises to greatly increase the performance and accessibility of quantum computers in the UK.

Seeqc UK is joined by othergrant recipients, Riverlane, Hitachi Europe, Universal Quantum, Duality Quantum Photonics, Oxford Ionics, and Oxford Quantum Circuits, along with UK-based chip designer, ARM, and the National Physical Laboratory.

Advancing Digital Quantum Computing

Seeqc owns and operates a multi-layer superconductive electronics chip fabrication facility, which is among the most advanced in the world. The foundry serves as a testing and benchmarking facility for Seeqc and the global quantum community to deliver quantum technologies for specific use cases. This foundry and expertise will be critical to the success of the grants. Seeqcs Digital Quantum Computing solution is designed to manage and control qubits in quantum computers in a way that is cost-efficient and scalable for real-world business applications in industries such as pharmaceuticals, logistics and chemical manufacturing.

Seeqcs participation in these new industry-leading British grants accelerates our work in making quantum computing useful, commercially and at scale, said Dr. Matthew Hutchings, chief product officer and co-founder at Seeqc, Inc. We are looking forward to applying our deep expertise in design, testing and manufacturing of quantum-ready superconductors, along with our resource-efficient approach to qubit control and readout to this collaborative development of quantum circuits.

We strongly support the Deltaflow.OS initiative and believe Seeqc can provide a strong contribution to both consortiums work and advance quantum technologies from the lab and into the hands of businesses via ultra-focused and problem-specific quantum computers, continued Hutchings.

Seeqcs solution combines classical and quantum computing to form an all-digital architecture through a system-on-a-chip design that utilizes 10-40 GHz superconductive classical co-processing to address the efficiency, stability and cost issues endemic to quantum computing systems.

Seeqc is receiving the nearly $2.3 million in grant funding weeks after closing its $6.8 million seed round from investors including BlueYard Capital, Cambium, NewLab and the Partnership Fund for New York City. The recent funding round is in addition to a $5 million investment from M Ventures, the strategic corporate venture capital arm of Merck KGaA, Darmstadt, Germany.

About Seeqc

Seeqc is developing the first fully digital quantum computing platform for global businesses. Seeqc combines classical and quantum technologies to address the efficiency, stability and cost issues endemic to quantum computing systems. The company applies classical and quantum technology through digital readout and control technology and a unique chip-scale architecture. Seeqcs quantum system provides the energy- and cost-efficiency, speed and digital control required to make quantum computing useful and bring the first commercially-scalable, problem-specific quantum computing applications to market.

Source: Seeqc

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Seeqc UK Awarded 1.8M in Grants to Advance Quantum Computing Initiatives - HPCwire

Video: The Future of Quantum Computing with IBM – insideHPC

Dario Gil from IBM Research

In this video, Dario Gil from IBM shares results from the IBM Quantum Challenge and describes how you can access and program quantum computers on the IBM Cloud today.

From May 4-8, we invited people from around the world to participate in the IBM Quantum Challengeon the IBM Cloud. We devised the Challenge as a global event to celebrateour fourth anniversary of having a real quantum computer on the cloud. Over those four days 1,745people from45countries came together to solve four problems ranging from introductory topics in quantum computing, to understanding how to mitigate noise in a real system, to learning about historic work inquantum cryptography, to seeing how close they could come to the best optimization result for a quantum circuit.

Those working in the Challenge joined all those who regularly make use of the 18quantum computing systems that IBM has on the cloud, includingthe 10 open systemsand the advanced machines available within theIBM Q Network. During the 96 hours of the Challenge, the total use of the 18 IBM Quantum systems on the IBM Cloud exceeded 1 billion circuits a day. Together, we made history every day the cloud users of the IBM Quantum systems made and then extended what can absolutely be called a world record in computing.

Every day we extend the science of quantum computing and advance engineering to build more powerful devices and systems. Weve put new two new systems on the cloud in the last month, and so our fleet of quantum systems on the cloud is getting bigger and better. Well be extending this cloud infrastructure later this year by installing quantum systems inGermanyand inJapan. Weve also gone more and more digital with our users with videos, online education, social media, Slack community discussions, and, of course, the Challenge.

Dr. Dario Gil is the Director of IBM Research, one of the worlds largest and most influential corporate research labs. IBM Research is a global organization with over 3,000 researchers at 12 laboratories on six continents advancing the future of computing. Dr. Gil leads innovation efforts at IBM, directing research strategies in Quantum, AI, Hybrid Cloud, Security, Industry Solutions, and Semiconductors and Systems. Dr. Gil is the 12th Director in its 74-year history. Prior to his current appointment, Dr. Gil served as Chief Operating Officer of IBM Research and the Vice President of AI and Quantum Computing, areas in which he continues to have broad responsibilities across IBM. Under his leadership, IBM was the first company in the world to build programmable quantum computers and make them universally available through the cloud. An advocate of collaborative research models, he co-chairs the MIT-IBM Watson AI Lab, a pioneering industrial-academic laboratory with a portfolio of more than 50 projects focused on advancing fundamental AI research to the broad benefit of industry and society.

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