Monthly Archives: September 2021

Executive Summary:

The data presented in the research report on Quantum Computing in Agriculture market is compiled with an aim to provide readers with a thorough understanding of industry dynamics in this business sphere. It entails a detailed analysis of growth stimulants, opportunities, challenges, and all other critical factors that will dictate the growth trajectory of this domain.

Proceeding further, the research document analyses past records and recent data to predict market valuation and business expansion trends between 2021-2026. It also elaborates on the segmentations of this vertical and their respective contribution to the overall growth. Besides, competitive landscape of this industry is included to help stakeholders make right decisions for the future.

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In short, the Global Quantum Computing in Agriculture Market report offers a one-stop solution to all the key players covering various aspects of the industry like growth statistics, development history, industry share, Quantum Computing in Agriculture Market presence, potential buyers, consumption forecast, data sources, and beneficial conclusion.

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Quantum Computing in Agriculture Market to Witness Stellar CAGR During the Forecast Period 2021 -2026 - Northwest Diamond Notes

In the mid-twentieth century, particle physicists were peering deeper into the history and makeup of the universe than ever before. Over time, their calculations became too complex to fit on a blackboardor to farm out to armies of human computers doing calculations by hand.

To deal with this, they developed some of the worlds earliest electronic computers.

Physics has played an important role in the history of computing. The transistorthe switch that controls the flow of electrical signal within a computerwas invented by a group of physicists at Bell Labs. The incredible computational demands of particle physics and astrophysics experiments have consistently pushed the boundaries of what is possible. They have encouraged the development of new technologies to handle tasks from dealing with avalanches of data to simulating interactions on the scales of both the cosmos and the quantum realm.

But this influence doesnt just go one way. Computing plays an essential role in particle physics and astrophysics as well. As computing has grown increasingly more sophisticated, its own progress has enabled new scientific discoveries and breakthroughs.

Illustration by Sandbox Studio, Chicago with Ariel Davis

In 1973, scientists at Fermi National Accelerator Laboratory in Illinois got their first big mainframe computer: a 7-year-old hand-me-down from Lawrence Berkeley National Laboratory. Called the CDC 6600, it weighed about 6 tons. Over the next five years, Fermilab added five more large mainframe computers to its collection.

Then came the completion of the Tevatronat the time, the worlds highest-energy particle acceleratorwhich would provide the particle beams for numerous experiments at the lab. By the mid-1990s, two four-story particle detectors would begin selecting, storing and analyzing data from millions of particle collisions at the Tevatron per second. Called the Collider Detector at Fermilab and the DZero detector, these new experiments threatened to overpower the labs computational abilities.

In December of 1983, a committee of physicists and computer scientists released a 103-page report highlighting the urgent need for an upgrading of the laboratorys computer facilities. The report said the lab should continue the process of catching up in terms of computing ability, and that this should remain the laboratorys top computing priority for the next few years.

Instead of simply buying more large computers (which were incredibly expensive), the committee suggested a new approach: They recommended increasing computational power by distributing the burden over clusters or farms of hundreds of smaller computers.

Thanks to Intels 1971 development of a new commercially available microprocessor the size of a domino, computers were shrinking. Fermilab was one of the first national labs to try the concept of clustering these smaller computers together, treating each particle collision as a computationally independent event that could be analyzed on its own processor.

Like many new ideas in science, it wasnt accepted without some pushback.

Joel Butler, a physicist at Fermilab who was on the computing committee, recalls, There was a big fight about whether this was a good idea or a bad idea.

A lot of people were enchanted with the big computers, he says. They were impressive-looking and reliable, and people knew how to use them. And then along came this swarm of little tiny devices, packaged in breadbox-sized enclosures.

The computers were unfamiliar, and the companies building them werent well-established. On top of that, it wasnt clear how well the clustering strategy would work.

As for Butler? I raised my hand [at a meeting] and said, Good ideaand suddenly my entire career shifted from building detectors and beamlines to doing computing, he chuckles.

Not long afterward, innovation that sparked for the benefit of particle physics enabled another leap in computing. In 1989, Tim Berners-Lee, a computer scientist at CERN, launched the World Wide Web to help CERN physicists share data with research collaborators all over the world.

To be clear, Berners-Lee didnt create the internetthat was already underway in the form the ARPANET, developed by the US Department of Defense. But the ARPANET connected only a few hundred computers, and it was difficult to share information across machines with different operating systems.

The web Berners-Lee created was an application that ran on the internet, like email, and started as a collection of documents connected by hyperlinks. To get around the problem of accessing files between different types of computers, he developed HTML (HyperText Markup Language), a programming language that formatted and displayed files in a web browser independent of the local computers operating system.

Berners-Lee also developed the first web browser, allowing users to access files stored on the first web server (Berners-Lees computer at CERN). He implemented the concept of a URL (Uniform Resource Locator), specifying how and where to access desired web pages.

What started out as an internal project to help particle physicists share data within their institution fundamentally changed not just computing, but how most people experience the digital world today.

Back at Fermilab, cluster computing wound up working well for handling the Tevatron data. Eventually, it became industry standard for tech giants like Google and Amazon.

Over the next decade, other US national laboratories adopted the idea, too. SLAC National Accelerator Laboratorythen called Stanford Linear Accelerator Centertransitioned from big mainframes to clusters of smaller computers to prepare for its own extremely data-hungry experiment, BaBar. Both SLAC and Fermilab also were early adopters of Lees web server. The labs set up the first two websites in the United States, paving the way for this innovation to spread across the continent.

In 1989, in recognition of the growing importance of computing in physics, Fermilab Director John Peoples elevated the computing department to a full-fledged division. The head of a division reports directly to the lab director, making it easier to get resources and set priorities. Physicist Tom Nash formed the new Computing Division, along with Butler and two other scientists, Irwin Gaines and Victoria White. Butler led the division from 1994 to 1998.

These computational systems worked well for particle physicists for a long time, says Berkeley Lab astrophysicist Peter Nugent. That is, until Moores Law started grinding to a halt.

Moores Law is the idea that the number of transistors in a circuit will double, making computers faster and cheaper, every two years. The term was first coined in the mid-1970s, and the trend reliably proceeded for decades. But now, computer manufacturers are starting to hit the physical limit of how many tiny transistors they can cram onto a single microchip.

Because of this, says Nugent, particle physicists have been looking to take advantage of high-performance computing instead.

Nugent says high-performance computing is something more than a cluster, or a cloud-computing environment that you could get from Google or AWS, or at your local university.

What it typically means, he says, is that you have high-speed networking between computational nodes, allowing them to share information with each other very, very quickly. When you are computing on up to hundreds of thousands of nodes simultaneously, it massively speeds up the process.

On a single traditional computer, he says, 100 million CPU hours translates to more than 11,000 years of continuous calculations. But for scientists using a high-performance computing facility at Berkeley Lab, Argonne National Laboratory or Oak Ridge National Laboratory, 100 million hours is a typical, large allocation for one year at these facilities.

Although astrophysicists have always relied on high-performance computing for simulating the birth of stars or modeling the evolution of the cosmos, Nugent says they are now using it for their data analysis as well.

This includes rapid image-processing computations that have enabled the observations of several supernovae, including SN 2011fe, captured just after it began. We found it just a few hours after it exploded, all because we were able to run these pipelines so efficiently and quickly, Nugent says.

According to Berkeley Lab physicist Paolo Calafiura, particle physicists also use high-performance computing for simulationsfor modeling not the evolution of the cosmos, but rather what happens inside a particle detector. Detector simulation is significantly the most computing-intensive problem that we have, he says.

Scientists need to evaluate multiple possibilities for what can happen when particles collide. To properly correct for detector effects when analyzing particle detector experiments, they need to simulate more data than they collect. If you collect 1 billion collision events a year, Calafiura says, you want to simulate 10 billion collision events.

Calafiura says that right now, hes more worried about finding a way to store all of the simulated and actual detector data than he is about producing it, but he knows that wont last.

When does physics push computing? he says. When computing is not good enough We see that in five years, computers will not be powerful enough for our problems, so we are pushing hard with some radically new ideas, and lots of detailed optimization work.

Thats why the Department of Energys Exascale Computing Project aims to build, in the next few years, computers capable of performing a quintillion (that is, a billion billion) operations per second. The new computers will be 1000 times faster than the current fastest computers.

The exascale computers will also be used for other applications ranging from precision medicine to climate modeling to national security.

Innovations in computer hardware have enabled astrophysicists to push the kinds of simulations and analyses they can do. For example, Nugent says, the introduction of graphics processing units has sped up astrophysicists ability to do calculations used in machine learning, leading to an explosive growth of machine learning in astrophysics.

With machine learning, which uses algorithms and statistics to identify patterns in data, astrophysicists can simulate entire universes in microseconds.

Machine learning has been important in particle physics as well, says Fermilab scientist Nhan Tran. [Physicists] have very high-dimensional data, very complex data, he says. Machine learning is an optimal way to find interesting structures in that data.

The same way a computer can be trained to tell the difference between cats and dogs in pictures, it can learn how to identify particles from physics datasets, distinguishing between things like pions and photons.

Tran says using computation this way can accelerate discovery. As physicists, weve been able to learn a lot about particle physics and nature using non-machine-learning algorithms, he says. But machine learning can drastically accelerate and augment that processand potentially provide deeper insight into the data.

And while teams of researchers are busy building exascale computers, others are hard at work trying to build another type of supercomputer: the quantum computer.

Remember Moores Law? Previously, engineers were able to make computer chips faster by shrinking the size of electrical circuits, reducing the amount of time it takes for electrical signals to travel. Now our technology is so good that literally the distance between transistors is the size of an atom, Tran says. So we cant keep scaling down the technology and expect the same gains weve seen in the past."

To get around this, some researchers are redefining how computation works at a fundamental levellike, really fundamental.

The basic unit of data in a classical computer is called a bit, which can hold one of two values: 1, if it has an electrical signal, or 0, if it has none. But in quantum computing, data is stored in quantum systemsthings like electrons, which have either up or down spins, or photons, which are polarized either vertically or horizontally. These data units are called qubits.

Heres where it gets weird. Through a quantum property called superposition, qubits have more than just two possible states. An electron can be up, down, or in a variety of stages in between.

What does this mean for computing? A collection of three classical bits can exist in only one of eight possible configurations: 000, 001, 010, 100, 011, 110, 101 or 111. But through superposition, three qubits can be in all eight of these configurations at once. A quantum computer can use that information to tackle problems that are impossible to solve with a classical computer.

Fermilab scientist Aaron Chou likens quantum problem-solving to throwing a pebble into a pond. The ripples move through the water in every possible direction, simultaneously exploring all of the possible things that it might encounter.

In contrast, a classical computer can only move in one direction at a time.

But this makes quantum computers faster than classical computers only when it comes to solving certain types of problems. Its not like you can take any classical algorithm and put it on a quantum computer and make it better, says University of California, Santa Barbara physicist John Martinis, who helped build Googles quantum computer.

Although quantum computers work in a fundamentally different way than classical computers, designing and building them wouldnt be possible without traditional computing laying the foundation, Martinis says. We're really piggybacking on a lot of the technology of the last 50 years or more.

The kinds of problems that are well suited to quantum computing are intrinsically quantum mechanical in nature, says Chou.

For instance, Martinis says, consider quantum chemistry. Solving quantum chemistry problems with classical computers is so difficult, he says, that 10 to 15% of the worlds supercomputer usage is currently dedicated to the task. Quantum chemistry problems are hard for the very reason why a quantum computer is powerfulbecause to complete them, you have to consider all the different quantum-mechanical states of all the individual atoms involved.

Because making better quantum computers would be so useful in physics research, and because building them requires skills and knowledge that physicists possess, physicists are ramping up their quantum efforts. In the United States, the National Quantum Initiative Act of 2018 called for the National Institute of Standards and Technology, the National Science Foundation and the Department of Energy to support programs, centers and consortia devoted to quantum information science.

In the early days of computational physics, the line between who was a particle physicist and who was a computer scientist could be fuzzy. Physicists used commercially available microprocessors to build custom computers for experiments. They also wrote much of their own softwareranging from printer drivers to the software that coordinated the analysis between the clustered computers.

Nowadays, roles have somewhat shifted. Most physicists use commercially available devices and software, allowing them to focus more on the physics, Butler says. But some people, like Anshu Dubey, work right at the intersection of the two fields. Dubey is a computational scientist at Argonne National Laboratory who works with computational physicists.

When a physicist needs to computationally interpret or model a phenomenon, sometimes they will sign up a student or postdoc in their research group for a programming course or two and then ask them to write the code to do the job. Although these codes are mathematically complex, Dubey says, they arent logically complex, making them relatively easy to write.

A simulation of a single physical phenomenon can be neatly packaged within fairly straightforward code. But the real world doesnt want to cooperate with you in terms of its modularity and encapsularity, she says.

Multiple forces are always at play, so to accurately model real-world complexity, you have to use more complex softwareideally software that doesnt become impossible to maintain as it gets updated over time. All of a sudden, says Dubey, you start to require people who are creative in their own rightin terms of being able to architect software.

Thats where people like Dubey come in. At Argonne, Dubey develops software that researchers use to model complex multi-physics systemsincorporating processes like fluid dynamics, radiation transfer and nuclear burning.

Hiring computer scientists for research projects in physics and other fields of science can be a challenge, Dubey says. Most funding agencies specify that research money can be used for hiring students and postdocs, but not paying for software development or hiring dedicated engineers. There is no viable career path in academia for people whose careers are like mine, she says.

In an ideal world, universities would establish endowed positions for a team of research software engineers in physics departments with a nontrivial amount of computational research, Dubey says. These engineers would write reliable, well-architected code, and their institutional knowledge would stay with a team.

Physics and computing have been closely intertwined for decades. However the two developtoward new analyses using artificial intelligence, for example, or toward the creation of better and better quantum computersit seems they will remain on this path together.

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The coevolution of particle physics and computing - Symmetry magazine

OXFORD, England, Sept. 29, 2021 /PRNewswire/ --PQShield, the cybersecurity company specialising in post-quantum cryptography, today announces a reciprocal agreement with Kudelski Security, the cybersecurity division within the Kudelski Group (SIX:KUD.S) to work together to address data protection as we near the era of quantum computing.

Within the agreement, PQShield will offer expert training and advisory services to Kudelski's clients, alongside post-quantum cryptography solutions for both software and hardware, to make preparations to address quantum threats. In addition to advisory services, there will be a collaborative referral and co-marketing relationship to support Kudelski Security's go-to-market strategy.

Kudelski Security launched its quantum security practice in December 2020 in recognition of the threat quantum technology raises in the future. Working with PQShield will bolster the practice by expanding availability of expert advisory and applied security resources, as well as providing clients the potential to utilise PQShield's quantum-ready cryptographic solutions for both software and hardware.

Headquartered in Oxford, with additional teams in the Netherlands and France, PQShield is helping businesses prepare for the quantum threat by pioneering the development and commercial deployment of quantum-ready cryptographic solutions for hardware, software and communications. With one of the UK's highest concentrations of cryptography PhDs outside academia and the classified sector, the PQShield team is also a leading contributor to NIST's post-quantum cryptographystandardisation project, now in its concluding stages, having contributed two of the seven finalist algorithms.

Several leading device manufacturers have already chosen PQShield's PQC IP for integration into hardware security solutions that will be compliant with upcoming US NIST/FIPS post-quantum cryptography standards, as well as localised standards from the likes of BSI in Germany.

Dr. Ali El Kaafarani, CEO and Founder of PQShield, said:

"Our partnership with Kudelski Security offers a great opportunity for us to educate the wider business world on the risks quantum technologies pose to secure data. We want to support Kudelski Security - whose commitment to empowering businesses to tackle the quantum threat is laudable and necessary - in advising companies looking to take a proactive approach in mitigating new risks.

"I am proud of the endorsement in PQShield's expertise in quantum-resistant cryptography indicated by this partnership, and am excited to see where the relationship takes us."

Andrew Howard, chief executive officer, Kudelski Security, said:

"This partnership will build on our internal knowledge across our teams of researchers, analysis and cryptographers and bolster them with the industry leading expertise from PQShield."

Tommaso Gagliardoni, Senior Expert in Quantum Security and Cryptography at Kudelski Security, added:

"There are certain sectors in business and government where there is a desire to secure sensitive data over a longer time span. These sectors are already starting to address the quantum threat to prevent bad actors from leveraging this technology against their encrypted data. I look forward to working alongside the experts at PQShield to educate industry and share best practice to help organisations secure their sensitive information now and for years to come."

About PQShield

PQShield is a cybersecurity startup that specialises in post-quantum cryptography, protecting information from today's attacks while readying organisations for the threat landscape of tomorrow. It is the only cybersecurity company that can demonstrate quantum-safe cryptography on chips, in applications and in the cloud. Headquartered in Oxford, with additional teams in the Netherlands and France, its quantum-secure cryptographic solutions work with companies' legacy systems to protect devices and sensitive data now and for years to come. PQShield is principally backed by Kindred Capital, Crane Venture Partners, Oxford Sciences Innovation, and InnovateUK. Its latest white papers are available to readhere.

http://www.pqshield.com

Public Press contact: [emailprotected]

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PQShield and Kudelski Security Partner to Address Quantum Threat - PRNewswire

Quantum computing has the potential to change the world and disrupt every industry by providing the opportunity to solve critical problems that modern-day supercomputers just cant achieve. The potentiality of quantum computers also includes the mapping of extremely complex weather patterns. This article is focused on forecasting the weather using quantum computers. Lets see how.

Forecasting the weather can be difficult. In weather prediction, it is hard to get 100% accuracy especially when the weather is considered changeable and the information available is limited. Advanced warnings of wild weather are necessary to minimize the impact of catastrophic events and the ensuing devastation and loss, but current models can only predict regional-scale weather events such as snowstorms and hurricanes, not more localized events such as thunderstorms. Thus, there is a requirement of computing power to keep an eye on the whole globe and predict when a simple storm might become dangerous. But this is not available. Many of the worlds largest supercomputers are already dedicated to weather forecasting but to achieve greater accuracy, they need even more computational brute force. Here comes the emergence of quantum computing.

Every year there are hurricanes, extreme heatwaves, tornadoes, and other extreme weather events, resulting in thousands of deaths and billions of dollars in damages. Prediction of extreme weather further in advance and with increased accuracy could allow for targeted regions to be better prepared to reduce loss of life and property damage.

Granted, there has been much work undertaken in the development of advanced computational models to enhance forecasting over the years, and much progress has been made. Weather forecasting requires analyzing huge amounts of data containing several dynamic variables, such as air temperature, pressure, and density that interact in a non-trivial way. However, there are limitations to using classical computers and even supercomputers in developing numerical weather and climate prediction models. Also, the process of analyzing the weather data by traditional computers may not be fast enough to keep up with ever-changing weather conditions.

Even local weather forecasting, which is rapidly evolving all the time, can stand to benefit from improved forecasting. Take, for example, thunderstorms, where highly accurate and advanced prediction by improved data analysis could minimize the resulting damage, as there could be warning further in advance about potential power outages, and increased preparedness, allowing the local community to restore power faster.

Quantum computing will serve to benefit weather forecasting on both the local scale as well as on a grander scale for more advanced and accurate warning of extreme weather events, potentially saving lives and reducing property damage annually. Beyond weather prediction, to stay informed on the state of quantum computing and its increasing impact on a variety of industries, keep up to date with the 1QBit blog, and follow us on social media.

Quantum computing has the potential to improve conventional numerical methods to boost tracking and predictions of meteorological conditions by handling huge amounts of data containing many variables efficiently and quickly, by harnessing the computing power of qubits, and by using quantum-inspired optimization algorithms. Moreover, pattern recognition, crucial for understanding the weather, can be enhanced utilizing quantum machine learning.

The enhancement of weather forecasting using quantum computing has already been set to become a reality in the coming future.

The UK Met Office has already heavily invested in quantum computing to help improve forecasting, while IBM Research has collaborated with The Weather Company, the University Corporation for Atmospheric Research (UCAR), and the National Centre for Atmospheric Research (NCAR) in America to develop a rapidly-updating, storm-scale model that could predict thunderstorms at a local level. Their model is the first to cover the entire globe and will provide high-resolution forecasts even in the most underserved areas. It employs IBMs supercomputing technology and geographical processing units and, in the future, has the potential to combine with quantum computing to help track and predict the meteorological conditions in ways that classical supercomputers are unable to achieve.

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Quantum Predictions: Weather Forecasting with Quantum Computers - Analytics Insight

Survey shows a wide range of skills and educational levels needed to support a diversity of jobs

ARLINGTON, Va., Sept. 28, 2021 /PRNewswire/ -- The Quantum Economic Development Consortium (QED-C) recently released an assessment based on a survey of U.S. quantum businesses outlining the diversity of jobs in the quantum industry requiring various skills and education levels. The study provides guidance to educators, policy makers and students to help grow a quantum-ready workforce. The analysis identified skills, several of which are not quantum-specific, relevant for multiple jobs.

"This study provides timely insight into the wide variety of jobs required to support the emerging quantum industry. The study results will help the U.S. grow a quantum workforce with the relevant skills," said Corey Stambaugh with the National Quantum Coordination Office in the White House Office of Science and Technology Policy.

The paper includes recommendations for educators preparing students for the quantum industry and advises those developing new degree programs should provide both quantum-specific and general science, technology, engineering and mathematics (STEM) courses. It also guides educators to consider adding broad quantum courses for students pursuing non-quantum degree programs, equipping them for multiple quantum-related roles.

The report acknowledges business skills will become increasingly important as the industry continues its progress from research to commercialization and suggests universities seek ways to prepare students for roles in sales and marketing.

"The QED-C workforce study highlights the opportunities and challenges for employers and prospective students for the quantum industry. The study also provides guidance to policy makers and educators on how best to prepare the future quantum workforce," said Alan Ho, Google Quantum AI product manager and QED-C steering committee member.

Information gathered from 57 QED-C member companies detailed specific work roles expected to be filled in the next five years and for each role, the associated skills and degrees required. Respondents were representative of the entire quantum supply chain, including hardware and software developers, component suppliers and end users.

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An assessment by QED-C and Hyperion Research forecasted the quantum computing industry could grow to $830 million by 2024 with an estimated compound annual growth rate of 27 percent. Such growth in quantum computing and other areas of application requires thousands of additional scientists, engineers, technicians and other employees to fill the variety of jobs, including those identified in the new survey. Skills sought by the employers surveyed include quantum algorithm development, circuit design, systems architecture, and technical sales and marketing. The preferred degree varies by job categoryfrom PhD to associate degree.

Growing the quantum workforce has been identified as an enabling factor to ensure the industry's success. The new report reveals the breadth of jobs and skills needed and can aid both educators and students to prepare for careers in this emerging field.

About Quantum Economic Development Consortium The Quantum Economic Development Consortium (QED-C) is an industry-driven consortium managed by SRI International and established in response to the 2018 National Quantum Initiative Act. Membership includes more than 120 US companies from across the supply chain and more than 40 academic institutions and other stakeholders. The consortium seeks to enable and grow the quantum industry and associated supply chain. For more about QED-C, visit quantumconsortium.org and follow us on Twitter @The_QEDC.

ContactCelia MerzbacherQED-C Executive Director319822@email4pr.com

Media Contact:Shannon Blood(949)-777-2428319822@email4pr.com

Amanda TomasettiSRI International319822@email4pr.com

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Job Hunting? The Quantum Industry is Hiring for Diverse Positions: New Assessment by the Quantum Economic Development Consortium Shows Many...