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Dell debuts oven-ready AI platforms to ease researchers’ setup pain – Blocks and Files

Dell EMC announced yesterday a bunch of reference architectures and pre-defined workstation, server and Isilon scale-out filer bundles for data scientists and researchers working in artificial intelligence.

In effect these are recipes that are quicker to prepare and easier to cook than starting from scratch using raw ingredients only.

Dells purpose for the initiative is to reduce the time that customers take in setting up workstations, servers, filers, system software, and installing cloud-native working environments. That frees up more time for analysis and AI model runs.

A Dell spokesperson said: AI initiatives generally start small in the proof-of-concept stage but preparation and model training are often the most time-consuming portions of the job. Combining hardware and software, Dells new AI-ready solutions will hasten researchers ability to stand up AI applications efficiently.

David Frattura, a senior Dell technology strategist, details the eight AI reference bundles in this blog. The architectures encompass use cases such as machine learning, deep learning, artificial intelligence,high performance computing, data analytics, Splunk, data science and modelling.

The buzzword benefits are legion; deploy faster, achieve greater model accuracy, accelerate business value and more on your AI digital transformation journey.

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How is Coronavirus Affecting the Daily Lives of Architects? Our Readers Answer – ArchDaily

How is Coronavirus Affecting the Daily Lives of Architects? Our Readers Answer

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A glimpse of hope emerged from the endless loop of COVID-19 news this week when China announced the closure of their last temporary hospital in Wuhan due to their stabilization of the pandemic that has now taken the world by storm. Western countries have been enforcing more restrictive measures aiming to stop the spread of the virus, including mandating shelter-in-place orders and forcing any business deemed non-essential to close. Due to the quarantine and isolation politics imposed by the authorities around the globe, we asked you, our readers, how the coronavirus is affecting your daily life as architects and designers. These answers allowed us to compose an overall picture of the atmosphere established by the pandemic and the way we are adapting to it.

Our pollsurveyed our Spanish, English and Portuguese platforms, and more than 600 readers shared their experiences. Most of the participants (39%) were between 21 and 30 years old, followed by the groupranging between 31 and 40 years old (29%). Readers between ages 41 and 50 represent 13% of thesurvey participants, while 9% were between 50 and 60, and readers over 60 were 7% of the readers who shared their experiences since the outbreak.

We also discovered that approximately 65% of theparticipantsstated that they had already worked from home before the quarantine in some capacity, whether just for a few days, or as a part of their regular routine. For the others, the newreality of adapting to a home officehas broughtmany challenges, related to the ability to focus on work and finding new means of communication with colleagues.

For many of those surveyed, one of the main challenges of having to work from home is the inability to connect with colleagues for informal conversations. The idea of remaining isolated for an undefined period of time, compounded with the general sensation of anxiety has brought a variety of disruptions to usual work flow, demanding an additional layer of communication. Video and phone calls, social networks, and other technology platforms have helped maintain synergy among team members.

The ability to access to the files and digital drawings was another frequently mentioned topic in our survey, which have been supplemented with cloud servers and private company networks. The readers of our three platforms pointed out the slowness and instability of internet services as a major downside to working from home, thathas resulted in designers spending more time working than usual.

One of the main challenges of designers who have made the transition to working at home is the difficulty in maintaining their typical work pace and finding the discipline to focus on daily tasks. Distractions caused by other family members who are also facing quarantine lock down measures, pets, neighboring noises, and domestic activities were cited as a few of themain obstacles to work at home. The lack of spaces exclusively dedicated to work have forced some of our readers to improvise small offices in their living rooms or bedrooms, only further adding to the inefficiencies of having to work from home.

The absence of a barrier between domestic life and work also seems toconcernsomeof the readerswho have been working more hours than usual since the quarantine began.

Among the readers worries was the uncertainty of facing a potential economic recession. Projects that have alreadybegun design and construction phases are being closely monitored, and some architects are seeing that clients are hesitant to sign contracts and award more work. Thefear of this potential crisis and its immeasurably directly impact the concerns of architects andother design professionals around the globe.

While a home office might be a temporary solution for many architects and designers, it only works to a certain extent. Throughout this quarantine, many countries have deemed construction services as essential, which means that sites are still being built, evenas architects are required to stay home. The amount of on-site meetings and coordination that traditionally happens through face to face social interactions needs to find a new medium in order to continue to have successfully completed projects.

"Workingfrom home in a third world country is a privilegenot often shared by the laborers. These skilled workers are forced tochoose between going to work and being exposed to the virus, or to stay home, depriving themselves of basic needs since they live exclusively from their work. Some of these countries have governments that lack of humanitarian initiatives to help them financially during this crisis."

Jeric Rustia, Philippines Architect

Some readers also expressed that they have experienced local building departmentsinvolved in the project approvalshalting not only the start of new construction, but also not approving drawingsthat have been completed since the quarantine period began.

On the other hand, some readers said that despite the myriad of challenges and problems imposed by the isolation, there are a few advantages of remote working. No longer having to spend the time commuting intothe office, which in cities like So Paulo or New York can sometimes take up two hours, designers have gained additionaltime that before was not available for leisurely activities. Some survey participants noted that spending more time with their families, cooking, reading, and watching TV are activities that they now havemore time for.

The greatest opportunity though, is how to undermine this moment of crisis and rethink the modes of work that have become commonplace in most architecture officesaroundthe world. Improving remote communication abilities, storing project files in the cloud, and implementing the use of BIM models are just a few ways that offices have come to adapt and modernize their methods of practice.

"It is mandatory we rethink completely our role as architecture professionals. Will we all be seen as necessary in this field? I think not. In Italy, we are 153,000 strong, and architectural design is still been seen as a luxury service. The Coronavirus will change the priorities of people for better. This is a great opportunity to define how architectural projects positively affect the lives of the people who will ultimately inhabit them."

- Francesca Perani, Italian architect.

With any global crisis of this scale, there are many fears and unknowns that our readers have expressed that they face in their new ways of working. As seen from our perspective, this might be the starting pointfora deeply-rooted transformation in the way we work, communicate, and practice architecture. Despite the fear of a possible recession, our readers as designers fromaround theworld, seem to seek strength and believe that together we will not only overcome this, but we will also discover a more human futurein our profession.

We invite you to check out ArchDaily's coverage related to COVID-19, read our tips and articles on Productivity When Working from Home and learn about technical recommendations for Healthy Design in your future projects. Also, remember to review the latest advice and information on COVID-19 from the World Health Organization (WHO) website.

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Enabling AI with edge computing and HCI – Techerati

There is a constant stream of innovation happening in storage technology, and the hyperconverged infrastructure (HCI) market is leading the way

According to this report, the HCI market is expected to be worth $17.1 billion by 2023. This projected growth could be put down to the myriad of advantages that HCI offers, including single-pane-of-glass management, reduced rackspace and power which means greener data centres, and improved disaster recovery capabilities to list a few.

Logically, the next step for HCI in accelerating its evolution has been its move to the edge of the network. As the demand for supercharged instances of data-use is growing, such as artificial intelligence (AI), its not surprising that enterprises are looking to edge computing and HCI to enable them to capture data from the very start of their projects. By combining edge computing with HCI, businesses can enable their AI tools to make more intelligent decisions.

With the days of pen and paper behind us, digitalisation has become a necessity across industries. As a result, we are creating a tonne of data, which of course needs to be stored somewhere. More often than not, this data is stored on-site at the edge of a network not your traditional data centre architecture.

One key benefit of edge computing is that it takes up a lot less hardware space than traditional hardware storage. By deploying this infrastructure at the edge of the network, it has the ability to not only handle and compile the data, but also compress the large amount of data so that it can be easily transferred into the cloud or into a centralised data centre at another site. This method grants access for the data to be handled and reviewed closer to where it was created, rather than trying to transmit it further away. This is why edge computing is often used by various distributed enterprises like fast-food restaurants, supermarkets, and petrol stations, as well as industrial surroundings like mines and solar energy plants.

Data collated at the edge of the network is not always being utilised to its full capacity. AI, for example, albeit still at the beginning of its journey, requires vast quantities of resources to develop and train its models. However, with edge computing the data is able to move freely into the cloud. From there the data can be analysed and the AI models can be trained before then extending it back to the edge. The best way for AI to be optimally used to generate these models is to make use of the data centre or the cloud.

Take the silicon chip company, Cerebras, which dedicates its work to accelerating deep learning. It has recently introduced its new Wafer Scale Engine which has been purposefully built for deep learning. This new chip is incredibly fast and 56 times bigger than the largest Graphics Processing Unit. Despite its grand size however, it does mean that its power consumption is at such a high capacity that most edge deployments would not be able to handle it.

That said, there is still hope, as businesses are able to amalgamate edge computing tasks using hyper converged infrastructure, enabling them to build and make the most of data lakes. By placing the data within a data lake, companies are able to use this to analyse it against all applications. The machine learning aspect is also able to unveil new insights through the use of its shared data against the diverse applications and devices.

When thinking about edge computing, HCI has made it much easier to use by combining servers, storage, and networking all in one box. Not to mention, it doesnt face the configuration or networking issues it previously had. To add to this, the platform can administer integrated management for a high quantity of edge devices located in different parts of the country, with various forms of networks and interfaces, and thereby undoubtedly decrease operational expenses.

The surge in use cases for things like smart home devices, self-driving cars, and wearable technology means that AI is already prevalent in our everyday lives. According to Gartner, AI will continue to flourish with 80% of smart devices to contain on-device AI capabilities by 2022.

However, AIs data collection does come up against a problem, because most of the technology powering it is hugely reliant on the cloud, and therefore can only come to a conclusion based on the data it has access to in the cloud. This results in a delayed response because the data first has to travel to the cloud, before heading back to the device. In the case of technologies like self-driving cars, which require instantaneous decision-making, any lag could result in huge complications.

In this scenario, edge computing has one up on the cloud and the potential to take AI to the next level. Any data required for that AI application is able to reside in close proximity to the device, therefore increasing the speed in which it is able to access and process the data. AI devices which are dependent on data conversion benefit the most from this application because they wont always be able to connect to the cloud, as it requires access to bandwidth and network availability.

Another advantage of combining edge computing with HCI for AI is that it requires a smaller amount of storage space. The best operational feature about HCI is that technology is able to function, within a smaller hardware design. It will soon be commonplace to find companies launching highly available HCI edge compute clusters which are comparable to the size of a cup of tea.

If AI is to truly succeed, it will need to depend on HCI and edge computing to work together side by side, allowing AI to function on its own merit, and with minimal support. AI will be able to make the most of its deep learning asset, as well as improve its ability to make better decisions.

AI has the ability to be accessible to the vast majority thanks to technological advances in the cloud. However, it is the marriage of HCI and edge computing that will provide AI with the means it needs to surge into new territories, providing more intelligent and efficient methods to find a solution for all companies.

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Quantum Computing strikes technology partnership with Splunk – Proactive Investors USA & Canada

Initial efforts with San Franciscos Splunk will focus on three key challenges: network security, dynamic logistics and scheduling

Quantum Computing Inc (OTC:QUBT), an advanced technology company developing quantum-ready applications and tools, said Tuesday that it has struck a technology alliance partnership with ().

San Francisco, California-based Splunk creates software for searching, monitoring, and analyzing machine-generated big data via a web-style interface.

Meanwhile, staffed by experts in mathematics, quantum physics, supercomputing, financing and cryptography, Leesburg, Virginia-based Quantum Computing is developing an array of applications to allow companies to exploit the power of quantum computing to their advantage. It is a leader in the development of quantum ready software with deep experience developing applications and tools for early quantum computers.

Splunk brings a leading big-data-analytics platform to the partnership, notably existing capabilities in its Machine/Deep Learning Toolkit in current use by Splunk customers, said the company.

Implementation of quantum computing applications will be significantly accelerated by tools that allow the development and execution of applications independent of any particular quantum computing architecture.

We are excited about this partnership opportunity, said Quantum Computing CEO Robert Liscouski. Splunk is a proven technology leader with over 17,500 customers world-wide, that has the potential to provide great opportunities for QCIs quantum ready software technologies.

Both the companies will partner to do fundamental and applied research and develop analytics that exploit conventional large-data cybersecurity stores and data-analytics workflows, combined with quantum-ready graph and constrained-optimization algorithms.

The company explained that these algorithms will initially be developed using Quantums Mukai software platform, which enables quantum-ready algorithms to execute on classical hardware and also to run without modification on quantum computing hardware when ready.

Once proofs of concept are completed, QCI and Splunk will develop new analytics with these algorithms in the Splunk data-analytics platform, to evaluate quantum analytics readiness on real-world data, noted the company.

The Splunk platform/toolkits help customers address challenging analytical problems via neural nets or custom algorithms, extensible to Deep Learning frameworks through an open source approach that incorporates existing and custom libraries.

The initial efforts of our partnership with Splunk will focus on three key challenges: network security, dynamic logistics and scheduling, said Quantum Computing.

Contact the author Uttara Choudhury at[emailprotected]

Follow her onTwitter:@UttaraProactive

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Devs: Alex Garland on Tech Company Cults, Quantum Computing, and Determinism – Den of Geek UK

Yet that difference between the common things a company can sell and the uncommon things they quietly develop is profoundly important. In Devs, the friendly exterior of Amaya with its enormous statue of a childa literal monument to Forests lost daughteris a public face to the actual profound work his Devs team is doing in a separate, highly secretive facility. Seemingly based in part on mysterious research and development wings of tech giantsthink Googles moonshot organizations at X Development and DeepMindDevs is using quantum computing to change the world, all while keeping Forests Zen ambition as its shield.

I think it helps, actually, Garland says about Forest not being a genius. Because I think what happens is that these [CEO] guys present as a kind of front between what the company is doing and the rest of the world, including the kind of inspection that the rest of the world might want on the company if they knew what the company was doing. So our belief and enthusiasm in the leader stops us from looking too hard at what the people behind-the-scenes are doing. And from my point of view thats quite common.

A lifelong man of words, Garland describes himself as a writer with a laymans interest in science. Yet its fair to say he studies almost obsessively whatever field of science hes writing about, which now pertains to quantum computing. A still largely unexplored frontier in the tech world, quantum computing is the use of technology to apply quantum-mechanical phenomena to data a traditional computer could never process. Its still so unknown that Google AI and NASA published a paper only six months ago in which they claimed to have achieved quantum supremacy (the creation of a quantum device that can actually solve problems a classical computer cannot).

Whereas binary computers work with gates that are either a one or a zero, a quantum qubit [a basic unit of measurement] can deal with a one and a zero concurrently, and all points in between, says Garland. So you get a staggering amount of exponential power as you start to run those qubits in tandem with each other. What the filmmaker is especially fascinated by is using a quantum system to model another quantum system. That is to say using a quantum computer with true supremacy to solve other theoretical problems in quantum physics. If we use a binary way of doing that, youre essentially using a filing system to model something that is emphatically not binary.

So in Devs, quantum computing is a gateway into a hell of a trippy concept: a quantum computer so powerful that it can analyze the theoretical data of everything that has or will occur. In essence, Forest and his team are creating a time machine that can project through a probabilistic system how events happened in the past, will happen in the future, and are happening right now. It thus acts as an omnipotent surveillance system far beyond any neocons dreams.

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Is Machine Learning The Quantum Physics Of Computer Science ? – Forbes

Preamble: Intermittently, I will be introducing some columns which introduce some seemingly outlandish concepts. The purpose is a bit of humor, but also to provoke some thought. Enjoy.

atom orbit abstract

God does not play dice with the universe, Albert Einstein is reported to have said about the field of Quantum Physics. He was referring to the great divide at the time in the physics community between general relativity and quantum physics. General relativity was a theory which beautifully explained a great deal of physical phenomena in a deterministic fashion. Meanwhile, quantum physics grew out of a model which fundamentally had a probabilistic view of the world. Since Einstein made that statement in the mid 1950s, quantum physics has proven to be quite a durable theory, and in fact, it is used in a variety of applications such as semiconductors.

One might imagine a past leader in computer science such as Donald Knuth exclaiming, Algorithms should be deterministic. That is, given any input, the output should be exact and known. Indeed, since its formation, the field of computer science has focused on building elegant deterministic algorithms which have a clear view of the transformation between inputs and outputs. Even in the regime of non-determinism such as parallel processing, the objective of the overall algorithm is to be deterministic. That is, despite the fact that operations can run out-of-order, the outputs are still exact and known. Computer scientists work very hard to make that a reality.

As computer scientists have engaged with the real world, they frequently face very noisy inputs such as sensors or even worse, human beings. Computer algorithms continue to focus on faithfully and precisely translating input noise to output noise. This has given rise to the Junk In Junk Out (JIJO) paradigm. One of the key motivations for pursuing such a structure has been the notion of causality and diagnosability. After all, if the algorithms are noisy, how is one to know the issue is not a bug in the algorithm? Good point.

With machine learning, computer science has transitioned to a model where one trains a machine to build an algorithm, and this machine can then be used to transform inputs to outputs. Since the process of training is dynamic and often ongoing, the data and the algorithm are intertwined in a manner which is not easily unwound. Similar to quantum physics, there is a class of applications where this model seems to work. Recognizing patterns seems to be a good application. This is a key building block for autonomous vehicles, but the results are probabilistic in nature.

In quantum physics, there is an implicit understanding that the answers are often probabilistic Perhaps this is the key insight which can allow us to leverage the power of machine learning techniques and avoid the pitfalls. That is, if the requirements of the algorithm must be exact, perhaps machine learning methods are not appropriate. As an example, if your bank statement was correct with somewhat high probability, this may not be comforting. However, if machine learning algorithms can provide with high probability the instances of potential fraud, the job of a forensic CPA is made quite a bit more productive. Similar analogies exist in the area of autonomous vehicles.

Overall, machine learning seems to define the notion of probabilistic algorithms in computer science in a similar manner as quantum physics. The critical challenge for computing is to find the correct mechanisms to design and validate probabilistic results.

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Organisms grow in wave pattern, similar to ocean circulation – Big Think

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the egg's surface.

These waves are produced by billions of activated proteins that surge through the egg's membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.

Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.

In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an egg's surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.

The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.

"Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems," says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. "It's a manifestation of this very universal wave pattern."

"It opens a completely new perspective," adds Jrn Dunkel, associate professor of mathematics at MIT. "You can borrow a lot of techniques people have developed to study similar patterns in other systems, to learn something about biology."

Fakhri and Dunkel have published their results today in the journal Nature Physics. Their co-authors are Tzer Han Tan, Jinghui Liu, Pearson Miller, and Melis Tekant of MIT.

Previous studies have shown that the fertilization of an egg immediately activates Rho-GTP, a protein within the egg which normally floats around in the cell's cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasm's morass to attach to the egg's membrane, snaking along the wall in waves.

"Imagine if you have a very dirty aquarium, and once a fish swims close to the glass, you can see it," Dunkel explains. "In a similar way, the proteins are somewhere inside the cell, and when they become activated, they attach to the membrane, and you start to see them move."

Fakhri says the waves of proteins moving across the egg's membrane serve, in part, to organize cell division around the cell's core.

"The egg is a huge cell, and these proteins have to work together to find its center, so that the cell knows where to divide and fold, many times over, to form an organism," Fakhri says. "Without these proteins making waves, there would be no cell division."

MIT researchers observe ripples across a newly fertilized egg that are similar to other systems, from ocean and atmospheric circulations to quantum fluids. Courtesy of the researchers.

In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an egg's surface when they altered the protein's concentration.

For their experiments, they obtained about 10 eggs from the ovaries of starfish through a minimally invasive surgical procedure. They introduced a hormone to stimulate maturation, and also injected fluorescent markers to attach to any active forms of Rho-GTP that rose up in response. They then observed each egg through a confocal microscope and watched as billions of the proteins activated and rippled across the egg's surface in response to varying concentrations of the artificial hormonal protein.

"In this way, we created a kaleidoscope of different patterns and looked at their resulting dynamics," Fakhri says.

The researchers first assembled black-and-white videos of each egg, showing the bright waves that traveled over its surface. The brighter a region in a wave, the higher the concentration of Rho-GTP in that particular region. For each video, they compared the brightness, or concentration of protein from pixel to pixel, and used these comparisons to generate an animation of the same wave patterns.

From their videos, the team observed that waves seemed to oscillate outward as tiny, hurricane-like spirals. The researchers traced the origin of each wave to the core of each spiral, which they refer to as a "topological defect." Out of curiosity, they tracked the movement of these defects themselves. They did some statistical analysis to determine how fast certain defects moved across an egg's surface, and how often, and in what configurations the spirals popped up, collided, and disappeared.

In a surprising twist, they found that their statistical results, and the behavior of waves in an egg's surface, were the same as the behavior of waves in other larger and seemingly unrelated systems.

"When you look at the statistics of these defects, it's essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale," Dunkel says. "It's the same universal phenomenon, just scaled down to the level of a cell."

The researchers are particularly interested in the waves' similarity to ideas in quantum computing. Just as the pattern of waves in an egg convey specific signals, in this case of cell division, quantum computing is a field that aims to manipulate atoms in a fluid, in precise patterns, in order to translate information and perform calculations.

"Perhaps now we can borrow ideas from quantum fluids, to build minicomputers from biological cells," Fakhri says. "We expect some differences, but we will try to explore [biological signaling waves] further as a tool for computation."

This research was supported, in part, by the James S. McDonnell Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation.

Reprinted with permission of MIT News. Read the original article.

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Recent PDF Report : Quantum Computing Market 2020: In-depth Industry Analysis By Size, Share, Competition, Opportunities And Growth By 2029 – Sound On…

MarketResearch.biz sets out the latest report on the Global Quantum Computing Market that includes an in-depth analysis of competition, segmentation, regional expansion, market dynamics and forecast 2020-2029.

The demand for Global Quantum Computing Market is anticipated to be high for the next ten years. By considering this demand we provide the latest Quantum Computing Market Report which gives complete industry analysis, market outlook, size, shares, restains, drivers, challenges, risk factors, growth, and forecast till 2029. This report also provides assistance in analyzing the current and future business trends, sales and revenue forecasts.

This research report provides a collective data on the Quantum Computing market, that also contains an intricate valuation of this business vertical. This report clearly explained the segments of the Quantum Computing market. This report provides a basic overview of the market in terms of its current status as well as market size, in terms of returns and volume parameters.

A basic outline of the competitive landscape:

The Quantum Computing market report includes a thorough analysis of the competitive landscape of this industry.

The report also encompasses a thorough analysis of the markets competitors scope based on the segmentation of the same into companies such as International Business Machines (IBM) Corporation, Google Inc, Microsoft Corporation, Qxbranch LLC, Cambridge Quantum Computing Ltd, 1QB Information Technologies Inc, QC Ware Corp., Magiq Technologies Inc, D-Wave Systems Inc, Rigetti Computing.

The study covers details on the individual market share of each industry contributor, the region served and more.

Key players Profiles covered in the report alongside facts concerning its futuristic strategies, financials, technological developments, supply chain study, collaboration & mergers, gross margins and price models.

To Obtain All-Inclusive Information On Forecast Analysis Of GlobalQuantum ComputingMarket, Download FREE SamplePDF Report Herehttps://marketresearch.biz/report/quantum-computing-market/request-sample

A complete outline of the regional spectrum:

A crisp outline of the market segmentation:

The Quantum Computing market is segmented on the basis of component, application, end-use industry, and region.

Segmentation by Component:

GeneratorConversion DevicesDistribution DevicesBattery Management SystemsOthers (Alternators, etc.)Segmentation by System:

Power GenerationPower DistributionPower ConversionEnergy StorageSegmentation by Platform:

Military AviationCommercial AviationBusiness & General AviationSegmentation by Application:

Cabin SystemFlight Control & OperationConfiguration ManagementPower Generation ManagementAir Pressurization & Conditioning

Inquire/Speak To Expert for Further Detailed Information About Quantum Computing Report:https://marketresearch.biz/report/quantum-computing-market/#inquiry

Different questions addressed through this research report:

What are the affecting factors for the growth of the market?

What are the major restraints and drivers of market?

What will be the market size in 2029?

Which are the most demanding regions in terms of consumption and production?

key outcomes of industry analysis techniques?

What are the successful key players in market?

Table of Content

1 Introduction of Quantum Computing Market

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

2 Executive Summary

3 Research Methodology of MarketResearch.biz

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

4 Quantum Computing Market Outlook

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.4 Value Chain Analysis

5 Quantum Computing Market , Segmentation

5.1 Overview

6 Quantum Computing Market , By Geography

6.1 Overview

6.2 North America

6.2.1 U.S.

6.2.2 Canada

6.2.3 Mexico

6.3 Europe

6.3.1 Germany

6.3.2 U.K.

6.3.3 France

6.3.4 Rest of Europe

6.4 Asia Pacific

6.4.1 China

6.4.2 Japan

6.4.3 India

6.4.4 Rest of Asia Pacific

6.5 Rest of the World

6.5.1 Latin America

6.5.2 Middle East

7 Quantum Computing Market Competitive Landscape

7.1 Overview

7.2 Company Market Ranking

7.3 Key Development Strategies

8 Company Profiles

8.1.1 Overview

8.1.2 Financial Performance

8.1.3 Product Outlook

8.1.4 Key Developments

9 Appendix

9.1 Related Research

Get Complete Table of Contents @https://marketresearch.biz/report/quantum-computing-market/#toc

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Tech incubator Fountech.Ventures launches in US and UK – UKTN

Fountech.Ventures, a next generation incubator for deep tech startups, has launched in the US and UK.

The subsidiary company ofFountech.ai, a four-year-old international AI think tank and parent company to a number of specialist AI and deep tech firms, is based in Austin, Texas, US and originated in London, UK.

Fountech.Ventures goes above and beyond a standard incubator it provides broader services over a longer timeframe so founders of deep tech startups can fast-track their businesses from ideation to commercial success.

Fountech.Ventures develops tailored programmes for members, sharing technical and commercial knowledge, along with the provision of interim CEOs, funding, business advice, office space and international networking opportunities.

Headed by Salvatore Minetti, a team of experienced tech experts will work with deep tech startups spanning artificial intelligence (AI), robotics, quantum computing and blockchain.

Based on progress and continuous assessments, Fountech.Ventures will invest its own funds into its portfolio companies, from pre-seed level right through to Series B.

PropTech platform launches to transform building performance

Salvatore Minetti, CEO of Fountech.Ventures, said: The US and UK are home to a vast number of deep tech startups that have immense growth potential. However, reaching that potential is difficult tech experts and PhD graduates have incredible ideas for how to use new and advanced technologies but often lack the skills and experience to transform them into successful businesses.

Fountech.Ventures will change all this by delivering the commercial expertise and infrastructure that is sorely needed. Whats more, the fact that our members can also access vital funding and our international hubs means we have a unique ability to bring products and services grounded in leading edge technologies to huge markets.

It is this end-to-end offering that makes us more than a typical incubator Fountech.Ventures is a next generation incubator.

Fountech.Ventures already has six portfolio companies. These include Soffos, an AI TutorBot; Prospex, an AI-powered lead generation tool; and Dinabite, a restaurant app built on an AI platform.

Banking alternative fintech company Lanistar launches

Rebecca Taylor and Joseph McCall have joined the Fountech.Ventures board as directors. The board is to be bolstered further with additional appointments in the coming weeks.

Nikolas Kairinos, CEO and founder of the parent company Fountech.ai, commented: We are delighted to unveil Fountech.Ventures today.

This next gen incubator is going to propel the growth of deep tech startups across both sides of the Atlantic. In doing so, we will enable innovative leading edge tech solutions to thrive and consequently improve the lives of consumers, businesses and societies.

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Tech incubator Fountech.Ventures launches in US and UK - UKTN

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Life after NKS. NetApp to work with ‘all flavours of Kubernetes’ – Blocks and Files

NetApp is developing its hyperconverged platform to deliver an automated Kubernetes facility. The storage giant has told us to expect a significant announcement in the Spring.

Longer term, NetApp will develop cloud-like products for file storage on-premises, based on NetApp HCI.

The company last week announced its decision to close NetApp Kubernetes Service (NKS), with effect from April 20. This week a spokesperson told Blocks & Files that it will subsume NKS technology into new offerings.

NKS enables customers to orchestrate a set of containers, but was focused too high up the Kubernetes stack, according to NetApp, which will swim downstream to provide lower-level support for many Kubernetes distributions. These may include Red Hat CoreOS, Canonical, Docker, Heptio, Kontena Pharos, Pivotal Container Service, Rancher, Red Hat OpenShift, SUSE Container as a Service and Telekube.

Blocks & Files asked NetApp for more details about the demise of NKS and Cloud Volumes services (CDS) on its hyperconverged platform. Here are the companys replies.

Blocks & Files: Does this mean the NetApp HCI product goes away?

NetApp: Absolutely not. NetApp has every intention to stay in the HCI space as it is a fast-growing market segment. NetApp HCI offers a unique value proposition for customers looking for cloud-like simplicity in an integrated appliance. With the added differentiation of NetApps data fabric strategy and integration, we believe we have a very competitive product.

In the future, we will be investing in NetApp HCI to become a simplified and automated infrastructure solution for on-premises Kubernetes environments. We will share more about our strategy in the Kubernetes market in the Spring.

Blocks & Files: What does a distribution-agnostic approach to Kubernetes mean?

NetApp: Distribution agnostic means we will work with all flavors of Kubernetes, currently there are more than 30 different distributions. Our storage needs to work with as many as customers demand.

Blocks & Files: How is the NKS offering not distribution-agnostic?

NetApp: NKS has been very much upstream-Kubernetes based and spins up an upstream-Kubernetes cluster. At the same time, customers may want to pick a different distribution of Kubernetes curated by a vendor. Being distribution-agnostic just means allowing more than upstream like an OpenShift.

Blocks & Files; Why is the NKS offering not being evolved into a distribution-agnostic one?

NetApp:The change in direction is an evolution to our approach to help customers simplify the Kubernetes environments. NKS is being consumed into new NetApp projects specific to Kubernetes, we have more than quadrupled the investment in our Kubernetes plans stay tuned for more on this soon.

Blocks & Files: Is the StackPointCloud technology being discarded?

NetApp: Absolutely not. The StackPoint technology and team are a central part of our investment in Kubernetes development and tools that will continue working on a focused set of solutions at NetApp to bring innovation and new capabilities to the Kubernetes ecosystem. Again, stay tuned.

Blocks & Files: Are the Cloud Volumes services on HCI, on-premises, AWS, Azure and GCP now all finished?

NetApp: Absolutely not, we have so much demand for CVS and Azure NetApp Files we have to allocate more of our resources and more of our infrastructure to Azure, Google and AWS. We have changed course for Cloud Volumes Service on premises and HCI to meet the demand on the three public clouds and focus our on-prem services with new investment areas.

For Cloud Volumes on HCI, in the near-term the service will be replaced by ONTAP Select included in the cost of the NetApp HCI appliance. Long-term,NetApp will use the feedback from theCloud Volumes on NetApp HCI preview and develop new, innovative cloud-like products for file storage on-premises on the NetApp HCI platform.

Blocks & Files: Are we looking in the future to a single replacement software product for NKS and the Cloud Volumes Service that covers the on-premises, AWS, Azure and GCP worlds with hardware supplier-agnostic on-premises converged and hyperconverged hardware?

NetApp: As we shared with our customers, NetApps goal in Kubernetes market will be tomake applications and associated data highly available, portable, and manageable across both on-premises and clouds through a software-defined set of data services. Please stay tuned for announcements this Spring.

Blocks & Files: Does that hyperconverged hardware include standard HCI offerings such as VxRail, Nutanix and HPE SimpliVity?

NetApp: NetApps goal is to continue investing in our converged andhyperconvergedsolutions, including NetApp HCI. Our investment is focused on continuing to offer a unique NetApp HCI solution with a focus on simplifying Kubernetes solutions while continuing to support our partners like VMware, Google, (Red Hat), and others through offering support for their software running on NetApp HCI. However, we do not have plans at this time to offer VxRail, Nutanix, or HPE HCI products.

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Life after NKS. NetApp to work with 'all flavours of Kubernetes' - Blocks and Files

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