Category Archives: Quantum Computing

BERLIN (Reuters) - Germany will spend about 2 billion euros ($2.4 billion) to support the development of its first quantum computer and related technologies in the next four years, the economy and science ministries said on Tuesday.

FILE PHOTO: German Economy Minister Peter Altmaier addresses a news conference in Berlin, Germany, April 27, 2021. John Macdougall/Pool via REUTERS

The science ministry will spend 1.1 billion euros by 2025 to support research and development in quantum computing, which uses the phenomena of quantum mechanics to deliver a leap forward in computation.

The economy ministry will spend 878 million euros backing practical applications.

Germanys Aerospace Center (DLR) will receive the bulk of the subsidies - about 740 million euros - to team up with industrial companies, medium-sized enterprises and start-ups to forge two consortia, the economy ministry said.

Quantum computing has the potential to revolutionize key industries of our economy, Economy Minister Peter Altmaier said.

Altmaier pointed to applications in areas such as better management of supply and demand in the energy sector, improved traffic control and faster testing of new active substances.

Its our goal that Germany will become one of the best players worldwide in the development and practical application of quantum computing, he said.

The state subsidies involved need the approval of the European Commission, the European Unions executive, which has urged member states to team up and develop the EUs first quantum computer in five years, as part of efforts to reduce its dependence on non-European technologies..

Science Minister Anja Karliczek said the governments goal was to meet the target of building a competitive quantum computer in Germany in five years, and to create a network of companies in the field to develop cutting-edge applications.

Today, we start the mission quantum computer Made in Germany - and now we are ready for takeoff, Karliczek said.

($1 = 0.8239 euros)

Reporting by Michael Nienaber, Editing by Timothy Heritage

Excerpt from:

Germany to support quantum computing with 2 billion euros - Reuters

Using a combination of tweaked algorithms, improved control systems and a new quantum service called Qiskit Runtime, IBM researchers have managed to resolve a quantum problem 120 times faster than the previous time they gave it a go.

Back in 2017, Big Blue announced that, equipped with a seven-qubit quantum processor,its researchers had successfully simulated the behavior of a small moleculecalled lithium hydride (LiH). At the time, the operation took 45 days. Now, four years later, the IBM Quantum team has announced that the same problem was solved in only nine hours.

The simulation was run entirely on the cloud, through IBM's Qiskit platform an open-source library of tools that lets developers around the world create quantum programs and run them on prototype quantum devices that IBM makes available over the cloud.

SEE: Building the bionic brain (free PDF) (TechRepublic)

The speed-up that was observed was largely made possible thanks to a new quantum service, Qiskit Runtime, which was key to reducing latencies during the simulation.

IBMteased Qiskit Runtime earlier this yearas part of the company's software roadmap for quantum computing, and at the time estimated that the new service would lead to a 100-time speed-up in workloads. With a reported 120-time speed-up, therefore, it seems that Big Blue has exceeded its own objectives.

Classical computing remains a fundamental part of Qiskit, and of any quantum operation carried out over the cloud. A quantum program can effectively be broken down into two parts: using classical hardware, like a laptop, developers send queries over the cloud to the quantum hardware in this case, to IBM's quantum computation center in Poughkeepsie, New York.

"The quantum method isn't just a quantum circuit that you execute," Blake Johnson, quantum platform lead at IBM Quantum, tells ZDNet. "There is an interaction between a classical computing resource that makes queries to the quantum hardware, then interprets those results to make new queries. That conversation is not a one-off thing it's happening over and over again, and you need it to be fast."

With every request that is sent, a few tens of thousands of quantum circuits are executed. To simulate the small LiH molecule, for example, 4.1 billion circuits were executed, which corresponds to millions of queries going back and forth between the classical resource and the quantum one.

When this conversation happens in the cloud, over an internet connection, between a user's laptop and IBM's US-based quantum processors, latency can quickly become a significant hurdle.

Case in point: while solving a problem as complex as molecular simulation in 45 days is a start, it isn't enough to achieve the quantum strides that scientists are getting excited about.

"We currently have a system that isn't architected intrinsically around the fact that real workloads have these quantum-classical loops," says Johnson.

Based on this observation, IBM's quantum team set out to build Qiskit Runtime a system that is built to natively accelerate the execution of a quantum program by removing some of the friction associated with the back-and-forth that is on-going between the quantum and the classical world.

Qiskit Runtime creates a containerized execution environment located beside the quantum hardware. Rather than sending many queries from their device to the cloud-based quantum computer, developers can therefore send entire programs to the Runtime environment, where the IBM hybrid cloud uploads and executes the work for them.

In other words, the loops that happen between the classical and the quantum environment are contained within Runtime which itself is near to the quantum processor. This effectively slashes the latencies that emerge from communicating between a user's computer and the quantum processor.

"The classical part, which generates queries to the quantum hardware, can now be run in a container platform that is co-located with the quantum hardware," explains Johnson. "The program executing there can ask a question to the quantum hardware and get a response back very quickly. It is a very low-cost interaction, so those loops are now suddenly much faster."

Improving the accuracy and scale of quantum calculations is no easy task.

Until now, explains Johnson, much of the research effort has focused on improving the quality of the quantum circuit. In practice, this has meant developing software that helps correct errors and add fault tolerance to the quantum hardware.

Qiskit Runtime, in this sense, marks a change in thinking: instead of working on the quality of quantum hardware, says Johnson, the system increases the overall program's capacity.

It remains true that the 120-times speed-up would not have been possible without additional tweaks to the hardware performance.

Algorithmic improvements, for example, reduced the number of iterations of the model that were required to receive a final answer by two to 10 times; while better processor performance meant that each iteration of the algorithm required less circuit runs.

At the same time, upgrades to the system software and control systems reduced the amount of time per circuit execution for each iteration.

"The quality is a critical ingredient that also makes the whole system run faster," says Johnson. "It is the harmonious improvement of quality and capacity working together that makes the system faster."

Now that the speed-up has been demonstrated in simulating the LiH molecule, Johnson is hoping to see developers use the improved technology to experiment with quantum applications in a variety of different fields beyond chemistry.

In another demonstration, for example, IBM's quantum team used Qiskit Runtime to run a machine-learning program for a classification task. The new system was able to execute the workload and find the optimal model to label a set of data in a timescale that Johnson described as "meaningful".

Qiskit Runtime will initially be released in beta, for a select number of users from IBM's Q Network, and will come with a fixed set-up of programs that are configurable. IBM expects that the system will be available to every user of the company's quantum services in the third quarter of 2021.

Combined with the 127-qubit quantum processor, called the IBM Quantum Eagle, which is slated for later this year, Big Blue hopes that the speed-up enabled by Runtime will mean that a lot of tasks that were once thought impractical on quantum computers will now be achievable.

The system certainly sets IBM on track to meet the objectives laid out in the company's quantum software roadmap, which projects that there will be frictionless quantum computing in a number of applications by 2025.

Original post:

IBM just solved this quantum computing problem 120 times faster than previously possible - ZDNet

Without any of its own hardware and most of its software heavy-lifting dedicated to front-end development, security, and broader AWS systems integration, the cloud giant could own the quantum computing user base. The reasons are simple, even if the business could get complicated.

We often talk about future leadership in quantum computing by way of hardware innovation but if (or when) the technology suddenly takes off, the real differentiator will be accessibility and service. That might take an approach that is multi-platform with a defined pricing, support, and security model and while quantum startups can handle physics, building global front-end services is a different ballgame.

Amazon Web Services already knows how this story goes from its experiences building a multi-platform mega-platform for machine learning and expects the same lessons could carry forward for early quantum computing. The dual benefit for AWS with ML and now quantum is they can build a multi-tool foundation that is ready for an explosion of growth when it hitsone that is free from the vendor-specific negotiations of functionality, access, and pricing. And along the way, they get to evaluate every hardware and software vendors tooling, see inside each use case, and build their own profile of what the nascent quantum industry needs in advance.

There is a lot of learning but not much in the way of a viable business, according to the GM of AWS Braket service, Richard Moulds. Recall that Braket is AWSs multi-layered quantum service, consisting of dedicated professional services teams to deep dive into specific applications, a research center oriented at Caltech, and Braket itself, which pulls together the hardware and software tools from a growing list of quantum computing vendors into a more cogent whole for easier access to and between quantum platforms and services.

At the moment, we dont see [quantum] as a business, Moulds tells The Next Platform. These machines cannot outpace classical systems today. There isnt a commercial proposition for using these devices. He says that what is definitely happening is fierce evaluation from both the makers of quantum devices and software but also companies trying to understand what they might need in a few years and who to hire to make it work.

Right now, everyone is preparing for the future, seeing what it might look like. For us, were seeing what the value is, the pricing model and dimensions, what capacity and security implications there might be, and how people will think about accessing algorithms and if that is through a marketplace. Were fleshing out the dimensions of the commercial model, not fighting for market share yet as an industry.

While AWS is preparing for this future, they are learning how to provide the kind of platform that will make truly accessible quantum computing possible. Theyre building something that can work with the best in breed hardware and quantum approach (annealing, gate, trapped ion, etc) for various use cases and let users experiment with those relatively seamlessly. And all the while theyre learning what will be the most successful when the technology takes offand be ready for the growth at its initial point while the standalone quantum makers struggle to build robust, secure front ends, support, and services, often on startup capital.

At this stage, every quantum hardware maker has its own system, software stack, pricing, access policies, and limited experts for handling specific algorithms. More important, that growing handful of quantum systems vendors will be tasked with building sophisticated front ends that have all the security users will demand. Seems like a tall order, one the early quantum startups like D-Wave and Rigetti had to manage because there was no Braket-like service at the time.

The challenges are clear for quantum systems makers, but for users, theyre even more pressing. Weve heard from customers and software partners that all of this hard to navigate, all this wrestling with multiple services, different commercial models, different tooling. If they want to switch between annealers to gate, for instance, its all inconsistent. The message we got was we needed to deliver a consistent multi-technology platform around quantum computing that gets around all this jumping. We wanted to build a platform for quantum computing, not a showcase for a particular technology, Moulds explains.

The goal is to build a mainstream cloud experience, no matter if the world isnt ready to launch into quantum. This will let users and AWS see what it means to have such a service sitting alongside classical compute resources, how it plays with storage systems, and how it might interface with other data science services, not to mention looking at what new access and security controls need to be envisioned.

Moulds points to the many operational hassles of running a global commercial service that many smaller quantum startups with standout hardware will have trouble managing alone. I think youll see a shift, the landscape is moving from a set of fragmented quantum services to a world where there are a set of platform services that the hardware providers gravitate toward.

That movement has already begun with quantum hardware makers, some of whom had to go it alone in the early days, including Rigetti and D-Wave. AWS also has IonQ devices as part of Braket. In short, this represents the three main approaches to quantum (gate, gate-based ion traps, and annealing), which means the cloud giant can explore the strengths and weaknesses of all three in the context of real-world applications their employees at the Caltech center help create. This kind of deep competitive understand can also come in handy if and when quantum computing blows up enough for AWS to truly leverage the Braket service at meaningful scale.

The moment quantum computing does something interesting in a provable fashion there will be a landslide of demand. Whatever industry that happens in first, everyone will want to take advantage of that technology and suddenly, the industry will be in a position where it will need to scale rapidly and have some of the basics in place that exist for other industries. This includes a commercial model, support, a functioning ecosystem, and tooling for users without a PhD in physics. Were trying to get ahead of the curve as a platform and be ready to absorb demand.

While AWS is working to standardize as much as they can, the devices themselves have too much variation to be fit nicely into instance-style boxes at this point. There is separate pricing by the minute for their managed simulator, D-Wave, Rigetti, and IonQ as well as per-task and per-shot. This gets a little tricky depending on the algorithm and while it sounds cheap from the outset (three cents for a per-task run on the D-Wave 2000Q) what that gets you in terms of results is experiment/tire-kicking level. Then again, perhaps thats all the industry needs now.

So far, Amazon Braket provides integrations with Amazon CloudWatch, Amazon EventBridge, AWS Identity and Access Management (IAM), and AWS CloudTrail for monitoring, event-based processing, user access management, and logs. S3 is the expected storage backed for results. AWS has its own Braket SDK but open source Penny lane is also available for hybrid algorithm development with the ability to tap into TensorFlow and PyTorch.

In short, the tooling and pricing are still evolving but if this continues to play out, and if AWS can capture the emerging quantum hardware makers, theyve provided a platform to let early users easily test the different hardware platforms and switch more easily between them, something that is not possible today with relatively steep vendor-specific learning curves. IBM has its own cloud and is likely to go it alone but there are a host of quantum startups on the horizonlets see how they decide to interface with the world.

See the rest here:

Why AWS Could Own the Future of Quantum Computing - The Next Platform

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PsiQuantum and Globalfoundries have teamed up to manufacture the chips that will become part of the Q1 quantum computer.

Palo Alto, California-based PsiQuantum has plans to create a million-qubit quantum computer. Globalfoundries is a major chipmaker that will manufacture the silicon photonic and electronic chips that are part of the Q1.

The system theyre working on now is the first milestone in PsiQuantums roadmap to deliver a commercially viable quantum computer with 1 million qubits (the basic unit of quantum information) and beyond. PsiQuantum believes silicon photonics, or combining optics with silicon chips, is the only way to scale beyond 1 million qubits and deliver an error-corrected, fault-tolerant, general-purpose quantum computer. PsiQuantum wants to deliver quantum capabilities that drive advances with customers and partners across climate, health care, finance, energy, agriculture, transportation, and communications.

PsiQuantum and GF have now demonstrated a world-first ability to manufacture core quantum components, such as single-photon sources and single-photon detectors, with precision and in volume, using the standard manufacturing processes of GFs world-leading semiconductor fab. The companies have also installed proprietary production and manufacturing equipment in two of Globalfoundries 300-millimeter factories to produce thousands of Q1 silicon photonic chips at its facility in upstate New York and state-of-the-art electronic control chips at its Fab 1 facility in Dresden, Germany.

Above: A Globalfoundries cleanroom.

Image Credit: Globalfoundries

PsiQuantums Q1 system represents breakthroughs in silicon photonics, which the company believes is the only way to scale to a million or more qubits to deliver an error-corrected, fault-tolerant, general-purpose quantum computer.

The Q1 system is the result of five years of development at PsiQuantum by the worlds foremost experts in photonic quantum computing. The team made it their mission to bring the world-changing benefits of quantum computing to reality, based on two fundamental understandings. Globalfoundries is fast becoming a leader in silicon photonics, Moor Insights & Strategy analyst Patrick Moorhead said in an email to VentureBeat. Its announcement with PsiQuantum now adds quantum computing to its SiPho repertoire of datacenter and chip-level connectivity.

First, it focused on a quantum computer capable of performing otherwise impossible calculations requiring a million physical qubits. Second, it leveraged more than 50 years and trillions of dollars invested in the semiconductor industry as the path to creating a commercially viable quantum computer.

Globalfoundries Amir Faintuch said in a statement that we have experienced a decade of technological change in the past year and that the digital transformation and explosion of data now requires quantum computing to accelerate a compute renaissance.

Globalfoundries silicon photonics manufacturing platform enables PsiQuantum to develop quantum chips that can be measured and tested for long-term performance reliability. This is critical to the ability to execute quantum algorithms, which require millions or billions of gate operations. PsiQuantum is collaborating with researchers, scientists, and developers at leading companies to explore and test quantum use cases across a range of industries, including energy, health care, finance, agriculture, transportation, and communications.

Pete Shadbolt, chief strategy officer at PsiQuantum, said in a statement that this is a major achievement for both the quantum and semiconductor industries, demonstrating that its possible to build the critical components of a quantum computer on a silicon chip, using standard manufacturing processes. He said PsiQuantum knew that scaling the system was key. By the middle of the decade, PsiQuantum and Globalfoundries hope to create all the manufacturing lines and processes needed to begin assembling a final machine.

PsiQuantum and Globalfoundries want to play a critical role in ensuring the United States becomes a global leader in quantum computing, supported by a secure, domestic supply chain.

See more here:

GlobalFoundries and PsiQuantum partner on full-scale quantum computer - VentureBeat

ARMONK, N.Y., May 7, 2021 IBM announced today it has extended its IBM Global University Program with historically black colleges and universities (HBCUs) to 40 schools.

IBM is now working with the American Association of Blacks in Higher Education (AABHE), 100 Black Men of America, Inc., Advancing Minorities Interest in Engineering (AMIE) and the United Negro College Fund (UNCF) to better prepare HBCU students for in-demand jobs in the digital economy.

In parallel, the IBM Institute for Business Value released a newreportwith broad-ranging recommendations on how businesses can cultivate more diverse, inclusive workforces by establishing similar programs and deepening engagement with HBCUs.

IBMs HBCU program momentum has been strong in an environment where only 43% of leaders across industry and academia believe higher education prepares students with necessary workforce skills.* In September 2020,IBM announcedthe investment of$100 millionin assets, technology and resources to HBCUs acrossthe United States. Through IBM Global University Programs, which include the continuously enhanced IBM Academic Initiative and IBM Skills Academy, IBM has now:

Building on this work, IBM and key HBCU ecosystem partners are now collaborating to expedite faculty and student access and use of IBMs industry resources.

In its new report,Investing in Black Technical Talent: The Power of Partnering with HBCUs,IBM describes how HBCUs succeed in realizing their mission and innovate to produce an exceptional talent pipeline, despite serious funding challenges. IBM explains its approach to broad-based HBCU collaboration with a series of best-practices for industry organizations.

IBMs series of best practices include:

To download the full report, please visit:LINK.

HBCU students continue to engage with IBM on a wide range of opportunities. These include students taking artificial intelligence, cybersecurity or cloud e-learning courses and receiving a foundational industry badge certificate in four hours. Many also attend IBMs virtual student Wednesday seminars with leading experts, such as IBM neuroscientists who discuss the implications of ethics in neurotechnology.

Statements from Collaborators

HBCUs typically deliver a high return on investment. They have less money in their endowments, faculty is responsible for teaching a larger volume of classes per term and they receive less revenue per student than non-HBCUs. Yet, HBCUs produce almost a third of all African-American STEM graduates,** saidValinda Kennedy, HBCU Program Manager, IBM Global University Programs and co-author ofInvesting in Black Technical Talent: The Power of Partnering with HBCUs.It is both a racial equity and an economic imperative for U.S. industry competitiveness to develop the most in-demand skills and jobs for all students and seek out HBCU students who are typically underrepresented in many of the most high-demand areas.

100 Black Men of America, Inc. is proud to collaboratewith IBM to deliver these exceptional and needed resources to the HBCU community and students attending these institutions. The 100 has long supported and sought to identify mechanisms that aid in the sustainability of historically black colleges and universities. This collaboration and the access and opportunities provided by IBM will make great strides in advancing that goal, stated100 Black Men of America ChairmanThomas W. Dortch, Jr.

The American Association of Blacks in Higher Education is proud to collaborate with IBM, saidDereck Rovaris, President, AABHE. Our mission to be the premier organization to drive leadership development, access and vital issues concerning Blacks in higher education works perfectly with IBMs mission to lead in the creation, development and manufacture of the industrys most advanced information technologies.Togetherthis collaboration will enhance both organizations and the many people we serve.

IBM is a strong AMIE partnerwhose role is strategic and support is significant in developing a diverse engineering workforce through AMIE and our HBCU community.IBMs presence on AMIEs Board of Directors provides leadership for AMIEs strategies,key initiatives and programsto achieve our goal of a diverse engineering workforce, saidVeronica Nelson, Executive Director, AMIE.IBM programslike the IBM Academic Initiative and the IBM Skills Academyprovideaccess, assets and opportunities for our HBCU faculty and students to gain high-demand skills in areas like AI, cybersecurity, blockchain, quantum computing and cloud computing. IBM is a key sponsor of the annual AMIE Design Challenge introducing students to new and emerging technologies through industry collaborations and providing experiential activities like IBM Enterprise Design Thinking, which is the foundational platform for the Design Challenge. The IBM Masters and PhD Fellowship Awards program supports our HBCU students with mentoring, collaboration opportunities on disruptive technologies as well as a financial award. The IBM Blue Movement HBCU Coding Boot Camp enables and recognizes programming competencies. IBM also sponsors scholarships for the students at the 15 HBCU Schools of Engineering to support their educational pursuits. IBM continues to evolve its engagement with AMIE and the HBCU Schools of Engineering.

The IBM Skills Academy is timely in providing resources that support the creativity of my students in the Dual Degree Engineering Program atClark Atlanta University, saidDr.Olugbemiga A. Olatidoye, Professor, Dual Degree Engineering and Director, Visualization, Stimulation and Design Laboratory,Clark Atlanta University. It also allows my students to be skillful in their design thinking process, which resulted in an IBM digital badge certificate and a stackable credential for their future endeavors.

We truly value the IBM skills programs and have benefitted from the Academic Initiative, Skills Academy and Global University Awards across all five campuses, saidDr.Derrick Warren, Interim Associate Dean and MBA Director,Southern University. Over 24 faculty and staff have received instructor training and more than 300 students now have micro-certifications in AI, cloud, cybersecurity, data science, design thinking, Internet of Things, quantum computing and other offerings.

At UNCF, we have a history of supporting HBCUs as they amplify their outsized impact on the Black community, and our work would not be possible without transformational partnerships with organizations like IBM and their IBM Global University Programs, saidEd Smith-Lewis, Executive Director of UNCFs Institute for Capacity Building. We are excited to bring the resources of IBM to HBCUs, their faculty, and their students.

IBM Skills Academy is an ideal platform for faculty to teach their students the latest in computing and internet technologies, saidDr. Sridhar Malkaram, WestVirginia State University. It helped the students in my Applied Data Mining course experience the state of the art in data science methods and analysis tools. The course completion badge/certificate has been an additional and useful incentive for students, which promoted their interest. The Skills Academy courses can be advantageously adapted by faculty, either as stand-alone courses or as part of existing courses.

AboutIBM

IBM is a leading global hybrid cloud, AI and business services provider. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. For more information visit:https://newsroom.ibm.com/home.

*King, Michael,Anthony Marshall,Dave Zaharchuk. Pursuit of relevance: How higher education remains viable in todays dynamic world. IBM Institute for Business Value. AccessedMarch 23, 2021.https://www.ibm.com/thought-leadership/institute-business-value/report/education-relevance

**Source: National Center for Education Statistics, Integrated Postsecondary Education Data System

Source: IBM

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IBM Extends HBCU Initiatives Through New Industry Collaborations - HPCwire

CSC, Finlands IT Center for Science, is home to a variety of computing resources, including the 1.7 petaflops Puhti supercomputer. The 682-node, Intel Cascade Lake-powered system, which places about halfway down the Top500 list, has managed to make major cultural and scientific waves over the last year thanks to its extensive use for COVID-19 research. In a new review article, CSC is highlighting the wide variety of coronavirus research hosted by Puhti through the course of the pandemic.

In March, CSC like many supercomputing centers around the world announced that it would be fast-tracking COVID-19 research. In CSCs case, approved projects were directed specifically to its Puhti system, with the center initially allocating a third of the supercomputer to the fast track for COVID-19.

In total, CSC awarded 15 applicant projects access to the Puhti fast track. The projects occupied much of the system from the spring through the middle of summer. However, CSC notes, the final fast track load on the system did not occupy that full third of the supercomputer for the duration of the pandemic: rather, it consumed a seemingly paltry 5.42 percent of Puhtis total usage over the course of 2020.

CSC and Puhtis breakthrough moment in COVID-19 research came early in the pandemic with bombshell simulations of viral particle spread (pictured in the header) that showed that a cough could transmit infectious COVID particles at considerable levels as far as 13 feet away, where they would linger for a number of minutes. These simulations one of which was presented in terms of a grocery store aisle captured the worlds attention at a time when the extent of COVIDs airborne transmission was not yet fully known or feared.

But this was far from the only application for Puhti, which is also involved in the global hunt for therapeutics and vaccines. One University of Helsinki researcher, for instance, is using Puhti to study cotransins, small molecules that are sometimes able to obstruct SARS-CoV-2 as it attempts to infect human cells; another group used Puhti to combine molecular dynamics and machine learning and gain better insights into the main protease of the virus. Yet others explored aspects of COVID-19 drug development ranging from the spike proteins and ACE2 receptors to drug repurposing and protein-protein inhibitors.

Other researchers used Puhti to investigate variants and mutations of the virus, which are of increasing concern as vaccinations promise to stamp out current forms of the virus in a number of hotspot countries. CSC reports that virtually all of the thousands of coronavirus genomes detected in Finnish patients were classified using Puhti, with new samples regularly arriving.

With the need for the fast track dwindling (CSC also introduced its 5.39 Linpack petaflops Mahti supercomputer last summer, diminishing competition for time on Mahti), the center is phasing out its COVID fast track this month. Mahti will itself be dwarfed by the 375 Linpack petaflops LUMI, the pre-exascale EuroHPC supercomputer that is slated to begin operations this year.

To learn more about the COVID research hosted by Puhti over the last year, visit CSCs roundup article here.

See the original post:

Finland's CSC Chronicles the COVID Research Performed on Its 'Puhti' Supercomputer - HPCwire

The business intelligence report on Quantum Computing Technologies Market hosts latest industry data and projections supported by historical statistics and growth opportunities linked to the industry expansion over 2021-2026.

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This Quantum Computing Technologies market Research/analysis Report Contains Answers to Your Following Questions:

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Quantum Computing Technologies Market to witness an impressive growth during th - News By ReportsGO

Imaginary numbers have a real physical meaning, according to a new set of studies.

Imaginary numbers, which can be combined with real numbers to form complex numbers, are numbers that were thought not to have any sort of analogue in daily life. Real numbers, by contrast, are clearly observable: 1 or 2 is easy enough to recognize in the real world; pi is the ratio of a circle's circumference to its diameter; 32 degrees Fahrenheit (0 degrees Celsius) is the freezing point of water. But there's nothing in the real world that can represent an imaginary number like the square root of negative 1.

Until now, perhaps: New research, conducted by a team led by Alexander Streltsov of the University of Warsaw in Poland and Kang-Da Wu of the University of Science and Technology of China in Hefei, finds that imaginary numbers actually carry real information about quantum states.

"They are not a mere mathematical artifact," said study co-author Carlo Maria Scandolo, a mathematical physicist at the University of Calgary in Canada. Instead, he said, "complex numbers really do exist."

Related: The 11 most beautiful mathematical equations

Imaginary numbers have always had a place in quantum theory. The equations used to describe the behavior of tiny quantum particles are expressed with these complex numbers. This raised a question, Scandolo told Live Science: Are these numbers just mathematical tools, or do they represent something real about the quantum states these equations describe?

To find out, the researchers used a mathematical framework to determine if imaginary numbers are a "resource." In quantum theory, "resource" has a specific meaning: a property that enables new actions that would otherwise be impossible. Quantum entanglement is a resource in quantum theory, because it allows actions such as quantum teleportation, or the transfer of information between locations.

If imaginary numbers are a resource, they'd enable physicists to do more than they could if imaginary numbers weren't present. The team's calculations suggested that imaginary numbers are indeed a resource. But the next step was to check that math in the real world.

To do so, the researchers set up an optics experiment in which a source sent entangled photons (particles of light) to two receivers, "Alice" and "Bob." The goal was for Alice and Bob to determine the quantum states of the photons. They could perform local measurements on their own photons and then compare the measurements, which would allow Alice and Bob to calculate their probability of guessing the correct state for the opposite photon.

For some pairs of quantum states, the researchers found, Alice and Bob could guess the states with 100% accuracy but only if they were allowed to use imaginary numbers in their local measurements. When they were forbidden from using imaginary numbers, it became impossible to accurately tell the two states apart.

"If I remove complex numbers, in these cases, I completely lose my ability to distinguish these two states," Scandolo said.

In other words, the experiment found the same thing as the math: The loss of complex numbers equaled the loss of real information about a quantum system.

The information these complex numbers carry isn't related to a simple physical property, like the spin of an electron. Instead, Scandolo said, it has to do with the ability to extract information from a particle where this particle is located, without considering interactions with other particles at a distance.

The researchers now plan to expand their search for other situations in quantum theory in which imaginary numbers might be a quantum resource. They also want to find out more about how imaginary numbers play a role in situations in which using quantum information is advantageous. For example, the information carried by imaginary numbers might also help explain the underlying reasons why quantum computing allows for actions that traditional computing doesn't, Scandolo said.

"It's important both from a foundational point of view but also as a way of understanding how we can better harness quantum resources and how the quantum world works," he said.

The research was published March 1 in the journals Physical Review A and Physical Review Letters.

Originally published on Live Science.

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'Imaginary' numbers are real (sort of) - Livescience.com

According to this report by Tech Nation, Cambridge is positioned among one of the eight cities in the UK with a higher digital density than the average The city is home to some of the fastest-growing tech companies in the UK such as Darktrace, GeoSpock and more has been estimated to have a turnover of 2.4 billion.

When detailing investments, Oxford, Bristol, Cambridge and Edinburgh are top for tech investments outside of London. At the same time, in 2020, VC investment in deep tech increased by 17% rising to nearly $4 billion. This investment trend in the deep tech companies builds on the UKs rich history in R&D and strong deeptech ecosystem with established companies ARM and Graphcore driving global success.

Notably, one of the popular Cambridge-based companies Five (formerly known as Five AI) received the biggest impact funding of $40 million in 2020 amidst the pandemic crisis. So, which are the other deep tech companies in Cambridge that are making a buzz and got some fundings lately. Today at UKTN, we take a brief look into these stars shining high.

Founder/s: Steve Brierley

Founded year: 2017

Funding: NA

Riverlane builds ground-breaking software to unleash the power of quantum computers. Backed by leading venture-capital funds and the University of Cambridge, the company develops software that transforms quantum computers into commercial products. Riverlane works with chemical, pharmaceutical and materials industries to improve algorithms.

Earlier this year, Riverlane raised $20 million (nearly 14.2 million) in Series A funding round led by European technology venture capital fund Draper Esprit along with existing investors, Cambridge Innovation Capital, Amadeus Capital Partners, and the University of Cambridge.

The company will use the investment to build Deltaflow, its operating system for quantum computers. Also, it will focus on expanding internationally to the US, Europe and beyond. The funding will accelerate Riverlanes mission to build a high-performance operating system that makes quantum computers useful.

Founder/s: Ramsey Faragher

Founded year: 2015

Funding: M 12.5

Focal Point Positioning has revolutionised navigation and positioning software for smartphones, wearables and autonomous vehicles through the development of two core products: S-GNSS and D-Tail. S-GNSS improves the sensitivity and accuracy of GPS receivers, making it far easier to track people indoors and in remote environments. D-Tail taps into sensors within a smartphone or wearable device to more accurately track users in three-dimensions, producing data that can be trusted more than ever before.

In March, Focal Point Positioning secured 6 millionSeries B funding from Draper Esprit. The company will use the investment to accelerate deployment of next-generation positioning technology into chipsets and devices across mobile, wearables, vehicles and IoT.

Founder/s: Humayun Sheikh, Thomas Hain, Toby Simpson

Founded year: 2017

Funding: M 19.6

Fetch.AI is a blockchain-based application development platform. It provides open access, tokenised, decentralised machine learning network to deliver a self-organising framework for transactions. Its system consists of autonomous economic agents that are digital entities that can transact independently of human intervention and can represent people, machines or themselves. It allows users to buy and sell digital assets autonomously with contracts, payments, and execution handled automatically.

Earlier this year, Fetch.AI bagged 5 million investment from GDA Group and affiliates. In conjunction with the newest injection of funds, FET, Fetch.ais native token, will be added to Fireblocks secure wallet and infrastructure platform for institutional investments.

Founder/s: Chiraz Ennaceur, Mehrdad Silatani, Prafull Sharma

Founded year: 2017

Funding: M 7.3

CorrosionRADAR, which is based in Cambridge is a predictive corrosion monitoring tech company that energises the global push on industrial digitalisation. By using cutting-edge technologies from its patent pending distributed sensing technology to Industrial Internet of Things (IIOT) and advanced analytics, the company creates a game-changing solution for tackling corrosion management.

The UK startup bagged 2.9M in a funding round led by Saudi Aramco Energy Ventures (SAEV) in February this year. The investment will be used to drive the next phase of the global growth of CorrosionRadar. Furthermore, the company will strengthen its operations and widen its efforts to address Corrosion Under Insulation (CUI) and other operational challenges with the help of digitalisation.

Founder/s: Simon Hombersley

Founded year: 2018

Funding: M 10.2

Xampla creates plant protein material for commercial use. Its next-generation material performs like synthetic polymers, but decomposes naturally and fully without harming the environment. The company wants to replace single-use plastics used on an everyday basis.

In January this year, the next-generation plastic replacement company secured 6.2M seed funding led by Horizon Ventures, a private investment arm of Mr Li Ka-shring along with participation from Amadeus Capital Partners. Xampla will use the investment to accelerate the rollout of its natural plant-protein alternative to plastic.

Founder/s: Jamil Shah Foridi, Serena Patel

Founded year: 2020

Funding: NA

JSF Healthcare provides a communication solution for healthcare professionals. The company has developed a mobile and web application called ReBleep, which allows users to communicate with team members, share medical documents, manage tasks, and more. The platform offers features for messaging, tracking directories, integrating users EHR systems, and tools to transfer files.

It also designed NovaBleep, which is a secure software replacement to pagers for use in healthcare, incorporating technology to not only decrease the time needed for communication. It also enables easier task prioritisation and reduces burnout of healthcare staff.

Founder/s: Tim Guilliams, David Brown

Founded year: 2014

Funding: 61.8m

Healx is an AI-powered and patient-inspired technology company, accelerating the discovery and development of rare disease treatments. Its AI drug discovery platform leverages public and proprietary biomedical data and features the worlds leading knowledge graph for rare diseases. Healx works with the mission to advance 100 rare disease treatments towards the clinic by 2025.

In 2019, Healx $56 million (nearly million) Series B funding in a round led by one of the largest VC firms in Europe Atomico. The other investors such as Intel Capital, btov Partners, and Global Brain and existing investors, including Balderton Capital, Amadeus Capital Partners, and Jonathan Milner also participated in the round.

Founder/s: Dr Sabine Bahn, Dan Cowell

Founded year: 2015

Funding: 2.8M

Psyomics is an innovative healthtech company, with a diagnostic platform called Censeo that draws on the psychiatric process. which seeks to transform the way mental health concerns are identified and defined, paving the way for earlier diagnosis and improved outcomes. A spinout from the University of Cambridge, Psyomics has developed unique technology to radically improve the way mental health conditions are assessed and triaged.

Back in October 2020, Psyomics bagged 1.5 million led by University spinout specialists Parkwalk along with fellow existing investors Jonathan Milner, Martlet, and Cambridge Enterprise. The funds will be used to bring its mental health assessment and diagnosis platform, Censeo, to market in the UK.

Founder/s: Tony Robinson

Founded year: 2009

Funding: 11.8m

Speechmatics provides automatic speech recognition technologies. Speech recognition is one of the hardest challenges to solve due to the complexity of human speech, resulting in large memory footprints. Using neural networks with the latest developments from academia and industry, Speechmatics has developed cloud-based and real-time speech-recognition technology in many languages. The technology can be used anywhere, by anyone, in any language.

Back in 2019, Speechmatics secured 6.35 million Series A funding led by AlbionVC; IQ Capital alongside several angel investors. The funding was announced to be used to support Speechmatics global growth ambitions.it will be used to enable product development and geographical expansion, with new offices globally.

CEO: Carmen Palacios-Berraquero

Founded year: 2018

Funding: 3.3M

Nu Quantum is a Cambridge University spinout. Quantum computing is set to make most encryption algorithms obsolete. Nu Quantum is producing single photon emitters and receivers that can operate outside of laboratory conditions. This will help raise cybersecurity into the quantum age.

Last year, Nu Quantum, a Cambridge, raised 2.1 million in seed funding from existing investors Amadeus Capital Partners, Ahren Innovation Capital, IQ Capital, Cambridge Enterprise and Martlet Capital and new investor Seraphim Capital. The company intends to use the funds to build its photonics lab in Cambridge and recruit scientists, product team members, and business functions.

Founder/s: Kent J. Griffith, Sai Shivareddy

Founded year: 2020

Funding: 9.1M

Nyobolt has developed a proprietary process using niobium-based anode materials to create batteries that deliver record high power, ultrafast charge and high energy. The world-leading charge and discharge rate capability of Nyobolt batteries, extensively recorded in academic journals, presents a huge opportunity to supercharge the electric revolution. The batteries also address other major challenges facing much existing technology, as they operate within a wide temperature range and are highly durable.

Earlier this year, Nyobolt pocketed $10 million (nearly million) in Series A funding. The round was led by IQ Capital with participation from Cambridge Enterprise and Silicon Valley investors. The funds will be used to expand globally, building new facilities, and growing the engineering and operational teams.

Founder/s: Mike Hulse

Founded year: 2017

Funding: 4.61M

Agile Analog is a company with a mission to transform the landscape of the Analog IP market space. Analog chip design is slow, complex and manual. Agile Analog is changing the way analog chip circuits are designed. Agile Analog allows customers to customise the chip for their specific needs, whether the Internet of Things, Security, Automotive, Artificial Intelligence, or other segments within the vast semiconductor space.

Back in 2019, Agile Analog grabbed $5.1 million (nearly million) in a pre-Series A funding round led by Delin Ventures, firstminute Capital and MMC Ventures. The company will use the funding to expand the existing engineering team in Cambridge and deliver analog IP products to a wide range of waiting customers.

Founder/s: Giorgia Longobardi, Florin Udrea

Funding: 10m

Founded: 2016

CGD has been created to explore and develop a number of unique opportunities in power electronics made possible by the teams proprietary application of Gallium Nitride to the silicon-based semiconductor transistor manufacturing process.

With silicon transistors widely acknowledged as having attained maximum efficiency, CGDs power design engineers have developed a range of Gallium Nitride transistors that are over 100 times faster, lose five to ten times less power and are four times smaller than existing silicon equivalents, and provide ease of use against alternative Gallium Nitride transistors. The possibilities and range of potential applications for these transistors reside in the energy-efficiency and power density applications, supporting energy savings and making the world a greener and better place.

In February, Cambridge GaN Devices (CGD), secured a $9.5 million (nearly 6.8 million) funding in a Series A round. The funding round was co-led by IQ Capital, Parkwalk Advisors, and BGF. It also included investment from Foresight Williams, Cambridge Enterprise, Martlet Capital, Cambridge Angels, and Cambridge Capital Group. The funds will be used to double staff and expand its GaN product portfolio following decades of research in power devices.

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The most promising deep tech startups of Cambridge in 2021 - UKTN (UK Technology News

Since the 1940s, classical computers have improved at breakneck speed. Today you can buy a wristwatch with more computing power than the state-of-the-art, room-sized computer from half a century ago. These advances have typically come through electrical engineers ability to fashion ever smaller transistors and circuits, and to pack them ever closer together.

But that downsizing will eventually hit a physical limit as computer electronics approach the atomic level, it will become impossible to control individual components without impacting neighboring ones. Classical computers cannot keep improving indefinitely using conventional scaling.

Quantum computing, an idea spawned in the 1980s, could one day carry the baton into a new era of powerful high-speed computing. The method uses quantum mechanical phenomena to run complex calculations not feasible for classical computers. In theory, quantum computing could solve problems in minutes that would take classical computers millennia. Already, Google has demonstrated quantum computings ability to outperform the worlds best supercomputer for certain tasks.

But its still early days quantum computing must clear a number of science and engineering hurdles before it can reliably solve practical problems. More than 100 researchers across MIT are helping develop the fundamental technologies necessary scale up quantum computing and turn its potential into reality.

What is quantum computing?

It helps to first understand the basics of classical computers, like the one youre using to read this story. Classical computers store and process information in binary bits, each of which holds a value of 0 or 1. A typical laptop could contain billions of transistors that use different levels of electrical voltage to represent either of these two values. While the shape, size, and power of classical computers vary widely, they all operate on the same basic system of binary logic.

Quantum computers are fundamentally different. Their quantum bits, called qubits, can each hold a value of 0, 1, or a simultaneous combination of the two states. Thats thanks to a quantum mechanical phenomenon called superposition. A quantum particle can act as if its in two places at once, explains John Chiaverini, a researcher at the MIT Lincoln Laboratorys Quantum Information and Integrated Nanosystems Group.

Particles can also be entangled with each other, as their quantum states become inextricably linked. Superposition and entanglement allow quantum computers to solve some kinds of problems exponentially faster than classical computers, Chiaverini says.

Chiaverini points to particular applications where quantum computers can shine. For example, theyre great at factoring large numbers, a vital tool in cryptography and digital security. They could also simulate complex molecular systems, which could aid drug discovery. In principle, quantum computers could turbocharge many areas of research and industry if only we could build reliable ones.

How do you build a quantum computer?

Quantum systems are not easy to manage, thanks to two related challenges. The first is that a qubits superposition state is highly sensitive. Minor environmental disturbances or material defects can cause qubits to err and lose their quantum information. This process, called decoherence, limits the useful lifetime of a qubit.

The second challenge lies in controlling the qubit to perform logical functions, often achieved through a finely tuned pulse of electromagnetic radiation. This manipulation process itself can generate enough incidental electromagnetic noise to cause decoherence. To scale up quantum computers, engineers will have to strike a balance between protecting qubits from potential disturbance and still allowing them to be manipulated for calculations. This balance could theoretically be attained by a range of physical systems, though two technologies currently show the most promise: superconductors and trapped ions.

A superconducting quantum computer uses the flow of paired electrons called Cooper pairs through a resistance-free circuit as the qubit. A superconductor is quite special, because below a certain temperature, its resistance goes away, says William Oliver, who is an associate professor in MITs Department of Electrical Engineering and Computer Science, a Lincoln Laboratory Fellow, and the director of the MIT Center for Quantum Engineering.

The computers Oliver engineers use qubits composed of superconducting aluminum circuits chilled close to absolute zero. The system acts as an anharmonic oscillator with two energy states, corresponding to 0 and 1, as current flows through the circuit one way or the other. These superconducting qubits are relatively large, about one tenth of a millimeter along each edge thats hundreds of thousands of times larger than a classical transistor. A superconducting qubits bulk makes it easy to manipulate for calculations.

But it also means Oliver is constantly fighting decoherence, seeking new ways to protect the qubits from environmental noise. His research mission is to iron out these technological kinks that could enable the fabrication of reliable superconducting quantum computers. I like to do fundamental research, but I like to do it in a way thats practical and scalable, Oliver says. Quantum engineering bridges quantum science and conventional engineering. Both science and engineering will be required to make quantum computing a reality.

Another solution to the challenge of manipulating qubits while protecting them against decoherence is a trapped ion quantum computer, which uses individual atoms and their natural quantum mechanical behavior as qubits. Atoms make for simpler qubits than supercooled circuits, according to Chiaverini. Luckily, I dont have to engineer the qubits themselves, he says. Nature gives me these really nice qubits. But the key is engineering the system and getting ahold of those things.

Chiaverinis qubits are charged ions, rather than neutral atoms, because theyre easier to contain and localize. He uses lasers to control the ions quantum behavior. Were manipulating the state of an electron. Were promoting one of the electrons in the atom to a higher energy level or a lower energy level, he says.

The ions themselves are held in place by applying voltage to an array of electrodes on a chip. If I do that correctly, then I can create an electromagnetic field that can hold on to a trapped ion just above the surface of the chip. By changing the voltages applied to the electrodes, Chiaverini can move the ions across the surface of the chip, allowing for multiqubit operations between separately trapped ions.

So, while the qubits themselves are simple, fine-tuning the system that surrounds them is an immense challenge. You need to engineer the control systems things like lasers, voltages, and radio frequency signals. Getting them all into a chip that also traps the ions is what we think is a key enabler.

Chiaverini notes that the engineering challenges facing trapped ion quantum computers generally relate to qubit control rather than preventing decoherence; the reverse is true for superconducting-based quantum computers. And of course, there are myriad other physical systems under investigation for their feasibility as quantum computers.

Where do we go from here?

If youre saving up to buy a quantum computer, dont hold your breath. Oliver and Chiaverini agree that quantum information processing will hit the commercial market only gradually in the coming years and decades as the science and engineering advance.

In the meantime, Chiaverini notes another application of the trapped ion technology hes developing: highly precise optical clocks, which could aid navigation and GPS. For his part, Oliver envisions a linked classical-quantum system, where a classical machine could run most of an algorithm, sending select calculations for the quantum machine to run before its qubits decohere. In the longer term, quantum computers could operate with more independence as improved error-correcting codes allow them to function indefinitely.

Quantum computing has been the future for several years, Chiaverini says. But now the technology appears to be reaching an inflection point, shifting from solely a scientific problem to a joint science and engineering one quantum engineering a shift aided in part by Chiaverini, Oliver, and dozens of other researchers at MITs Center for Quantum Engineering (CQE) and elsewhere.

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