Category Archives: Quantum Computing

IQM Finland Oy (IQM), a European startup which makes hardware for quantum computers, has raised a 15M equity investment round from the EIC Accelerator program for the development of quantum computers. This is in addition to a raise of 3.3M from the Business Finland government agency. This takes the companys funding to over 32M. The company previously raised a 11.4M seed round.

IQM has hired a lot of engineers in its short life, and now says it plans to hire one quantum engineer per week on the pathway to commercializing its technology through the collaborative design of quantum-computing hardware and applications.

Dr. Jan Goetz, CEO and co-founder of IQM said: Quantum computers will be funded by European governments, supporting IQM s expansion strategy to build quantum computers in Germany, in a statement.

The news comes as the Finnish government announced only last week that it would acquire a quantum computer with 20.7M for the Finnish State Research center VTT.

It has been a mind-blowing forty-million past week for quantum computers in Finland. IQM staff is excited to work together with VTT, Aalto University, and CSC in this ecosystem, rejoices Prof. Mikko Mttnen, Chief Scientist and co-founder of IQM.

Previously, the German government said it would put 2bn into commissioning at least two quantum computers.

IQM thus now plans to expand its operations in Germany via its team in Munich.

IQM will build co-design quantum computers for commercial applications and install testing facilities for quantum processors, said Prof. Enrique Solano, CEO of IQM Germany.

The company is focusing on superconducting quantum processors, which are streamlined for commercial applications in a Co-Design approach. This works by providing the full hardware stack for a quantum computer, integrating different technologies, and then invites collaborations with quantum software companies.

IQM was one of the 72 to succeed in the selection process of the EIC. Altogether 3969 companies applied for this funding.

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European quantum computing startup takes its funding to 32M with fresh raise - TechCrunch

Quantum computers stand a good chance of changing the face computing, and that goes double for encryption. For encryption methods that rely on the fact that brute-forcing the key takes too long with classical computers, quantum computing seems like its logical nemesis.

For instance, the mathematical problem that lies at the heart of RSA and other public-key encryption schemes is factoring a product of two prime numbers. Searching for the right pair using classical methods takes approximately forever, but Shors algorithm can be used on a suitable quantum computer to do the required factorization of integers in almost no time.

When quantum computers become capable enough, the threat to a lot of our encrypted communication is a real one. If one can no longer rely on simply making the brute-forcing of a decryption computationally heavy, all of todays public-key encryption algorithms are essentially useless. This is the doomsday scenario, but how close are we to this actually happening, and what can be done?

To ascertain the real threat, one has to look at the classical encryption algorithms in use today to see which parts of them would be susceptible to being solved by a quantum algorithm in significantly less time than it would take for a classical computer. In particular, we should make the distinction between symmetric and asymmetric encryption.

Symmetric algorithms can be encoded and decoded with the same secret key, and that has to be shared between communication partners through a secure channel. Asymmetric encryption uses a private key for decryption and a public key for encryption onlytwo keys: a private key and a public key. A message encrypted with the public key can only be decrypted with the private key. This enables public-key cryptography: the public key can be shared freely without fear of impersonation because it can only be used to encrypt and not decrypt.

As mentioned earlier, RSA is one cryptosystem which is vulnerable to quantum algorithms, on account of its reliance on integer factorization. RSA is an asymmetric encryption algorithm, involving a public and private key, which creates the so-called RSA problem. This occurs when one tries to perform a private-key operation when only the public key is known, requiring finding the eth roots of an arbitrary number, modulo N. Currently this is unrealistic to classically solve for >1024 bit RSA key sizes.

Here we see again the thing that makes quantum computing so fascinating: the ability to quickly solve non-deterministic polynomial (NP) problems. Whereas some NP problems can be solved quickly by classical computers, they do this by approximating a solution. NP-complete problems are those for which no classical approximation algorithm can be devised. An example of this is the Travelling Salesman Problem (TSP), which asks to determine the shortest possible route between a list of cities, while visiting each city once and returning to the origin city.

Even though TSP can be solved with classical computing for smaller number of cities (tens of thousands), larger numbers require approximation to get within 1%, as solving them would require excessively long running times.

Symmetric encryption algorithms are commonly used for live traffic, with only handshake and the initial establishing of a connection done using (slower) asymmetric encryption as a secure channel for exchanging of the symmetric keys. Although symmetric encryption tends to be faster than asymmetric encryption, it relies on both parties having access to the shared secret, instead of being able to use a public key.

Symmetric encryption is used with forward secrecy (also known as perfect forward secrecy). The idea behind FS being that instead of only relying on the security provided by the initial encrypted channel, one also encrypts the messages before they are being sent. This way even if the keys for the encryption channel got compromised, all an attacker would end up with are more encrypted messages, each encrypted using a different ephemeral key.

FS tends to use Diffie-Hellman key exchange or similar, resulting in a system that is comparable to a One-Time Pad (OTP) type of encryption, that only uses the encryption key once. Using traditional methods, this means that even after obtaining the private key and cracking a single message, one has to spend the same effort on every other message as on that first one in order to read the entire conversation. This is the reason why many secure chat programs like Signal as well as increasingly more HTTPS-enabled servers use FS.

It was already back in 1996 that Lov Grover came up with Grovers algorithm, which allows for a roughly quadratic speed-up as a black box search algorithm. Specifically it finds with high probability the likely input to a black box (like an encryption algorithm) which produced the known output (the encrypted message).

As noted by Daniel J. Bernstein, the creation of quantum computers that can effectively execute Grovers algorithm would necessitate at least the doubling of todays symmetric key lengths. This in addition to breaking RSA, DSA, ECDSA and many other cryptographic systems.

The observant among us may have noticed that despite some spurious marketing claims over the past years, we are rather short on actual quantum computers today. When it comes to quantum computers that have actually made it out of the laboratory and into a commercial setting, we have quantum annealing systems, with D-Wave being a well-known manufacturer of such systems.

Quantum annealing systems can only solve a subset of NP-complete problems, of which the travelling salesman problem, with a discrete search space. It would for example not be possible to run Shors algorithm on a quantum annealing system. Adiabatic quantum computation is closely related to quantum annealing and therefore equally unsuitable for a general-purpose quantum computing system.

This leaves todays quantum computing research thus mostly in the realm of simulations, and classical encryption mostly secure (for now).

When can we expect to see quantum computers that can decrypt every single one of our communications with nary any effort? This is a tricky question. Much of it relies on when we can get a significant number of quantum bits, or qubits, together into something like a quantum circuit model with sufficient error correction to make the results anywhere as reliable as those of classical computers.

At this point in time one could say that we are still trying to figure out what the basic elements of a quantum computer will look like. This has led to the following quantum computing models:

Of these four models, quantum annealing has been implemented and commercialized. The others have seen many physical realizations in laboratory settings, but arent up to scale yet. In many ways it isnt dissimilar to the situation that classical computers found themselves in throughout the 19th and early 20th century when successive computers found themselves moving from mechanical systems to relays and valves, followed by discrete transistors and ultimately (for now) countless transistors integrated into singular chips.

It was the discovery of semiconducting materials and new production processes that allowed classical computers to flourish. For quantum computing the question appears to be mostly a matter of when well manage to do the same there.

Even if in a decade or more from the quantum computing revolution will suddenly make our triple-strength, military-grade encryption look as robust as DES does today, we can always comfort ourselves with the knowledge that along with quantum computing we are also increasingly learning more about quantum cryptography.

In many ways quantum cryptography is even more exciting than classical cryptography, as it can exploit quantum mechanical properties. Best known is quantum key distribution (QKD), which uses the process of quantum communication to establish a shared key between two parties. The fascinating property of QKD is that the mere act of listening in on this communication will cause measurable changes. Essentially this provides unconditional security in distributing symmetric key material, and symmetric encryption is significantly more quantum-resistant.

All of this means that even if the coming decades are likely to bring some form of upheaval that may or may not mean the end of classical computing and cryptography with it, not all is lost. As usual, science and technology with it will progress, and future generations will look back on todays primitive technology with some level of puzzlement.

For now, using TLS 1.3 and any other protocols that support forward secrecy, and symmetric encryption in general, is your best bet.

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Quantum Computing And The End Of Encryption - Hackaday

On Tuesday, 2 June, student of the University of Tartu Institute of Computer Science Mykhailo Nitsenko defended his thesis Quantum Circuit Fusion in the Presence of Quantum Noise on NISQ Devices, the first masters thesis defended in the field of quantum computing at the University of Tartu.

In his thesis supervised by Dirk Oliver Theis and Dominique Unruh, Mykhailo Nitsenko studied a concept called circuit fusion, which proposes to reduce stochastic noise in estimating the expectation values of measurements at the end of quantum computations. But near-term quantum computing devices are also subject to quantum noise (such as decoherence etc.), and circuit fusion aggravates that problem.

Mykhailo Nitsenko ran thousands of experiments on IBMs cloud quantum computers and used Fourier analysis techniques to quantify and visualise noise and the resulting information loss.

According to Mykhailo Nitsenko, before he enrolled in the University of Tartu he had a strong opinion that quantum computing is an abstract idea that we will never be able to use or even implement. I just could not imagine how it is even possible to do computations on things without directly observing them. Quantum computing class showed me how it is done, and it became apparent to me that it is something I want to dedicate my academic efforts to, said Nitsenko.

If you dont want to wait for fault-tolerant quantum computers, you may endeavour to use the noisy quantum computing devices that can be built already now. In that case, researching the effects of quantum noise on computations becomes important: these effects must be mitigated, said Dirk Oliver Theis, Associate Professor of Theoretical Computer Science at the University of Tartu Institute of Computer Science. Theis added that he had expected that the mathematics which Mykhailo Nitsenko implemented in his thesis would help us understand some aspects of quantum noise which can be devastating to quantum computations, rendering the result pure gibberish.

In near-term quantum computing, one tries to run quantum circuits which are just short enough so that the correct output can be somehow reconstructed from the distorted measurement results. But quantum noise affects the results of computations on near-term quantum computers in complicated ways. In the mathematical approach based on Fourier analysis that Nitsenko implemented, some effects were predictable, such as a decrease in the amplitudes due to decoherence. What was surprising was that the low frequencies of the quantum noise showed distinct patterns. In future research, this might be exploited to mitigate the effect of quantum noise on the computation, said Theis.

This year, the Information Technology Foundation for Education (HITSA) granted funding to the University of Tartu Institute of Physics to continue and increase the training and research in the field of quantum computing at the university. With the support of this funding, new interdisciplinary courses focusing on quantum programming will be created.

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First master's thesis in Quantum Computing defended at the University of Tartu - Baltic Times

As time goes on, the value of efficiency and convenience becomes more and more important. Weve seen this in many examples from talk-to-text, to ordering food directly to your door without ever even speaking to another human.

Now coming into the convenience game is a keyboard that allows you to scan instead of type. Anyline is the new keyboard that instantly collects data with the snap of a camera.

Scan ID information, serial numbers, vouchers, IBANs, and barcodes in an instant with your smartphone, as it is compatible with Android and iOS. The app also allows you to scan things such as gift card barcodes, phone numbers you see on street advertisements, and more so, in a sense, it brings CTRL + C to real life.

With your smartphone, you can instantly collect data with the scan function on your keyboard. The platform is compatible with messenger, email, and browser apps. You scan the data and instantly paste it where you want it, saving the time of manual data entry.

This would be useful for scanning things to your notes section that you may refer to often, like your health insurance ID number, your WiFi router information, credit card info and what not.With anything else like this, the concern of privacy is always there so make sure youre doing what you can to protect your information (using a passcode and/or Face ID, not using shared/public networks, etc.) While you should know it by heart, I would recommend not ever scanning your social security number.

However, something like this does save a lot of time as it doesnt involve mistyping it picks up a barcode accurately. Also, you wont need someone reading something back to you so you can accurately type it down into your phone.

This could be a simple way to save time and become a more efficient person in general, and it makes it easier to share information with others. This is also super helpful for people who have trouble reading the teeny tiny type that barcodes are often displayed in.

Comment your thoughts below, and share any tips you use to help further your efficiency!

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The future of quantum computing is Azure bright and you can try it - The American Genius

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Global Quantum Computing for Enterprise Market Expected to Reach Highest CAGR by 2025 Top Players: 1QB Information Technologies, Airbus, Anyon...

Jun 11th 2020

THE FUNDAMENTAL assumption of the computing industry is that number-crunching gets cheaper all the time. Moores law, the industrys master metronome, predicts that the number of components that can be squeezed onto a microchip of a given sizeand thus, loosely, the amount of computational power available at a given costdoubles every two years.

For many comparatively simple AI applications, that means that the cost of training a computer is falling, says Christopher Manning, the director of Stanford Universitys AI Lab. But that is not true everywhere. A combination of ballooning complexity and competition means costs at the cutting edge are rising sharply.

Dr Manning gives the example of BERT, an AI language model built by Google in 2018 and used in the firms search engine. It had more than 350m internal parameters and a prodigious appetite for data. It was trained using 3.3bn words of text culled mostly from Wikipedia, an online encyclopedia. These days, says Dr Manning, Wikipedia is not such a large data-set. If you can train a system on 30bn words its going to perform better than one trained on 3bn. And more data means more computing power to crunch it all.

OpenAI, a research firm based in California, says demand for processing power took off in 2012, as excitement around machine learning was starting to build. It has accelerated sharply. By 2018, the computer power used to train big models had risen 300,000-fold, and was doubling every three and a half months (see chart). It should knowto train its own OpenAI Five system, designed to beat humans at Defense of the Ancients 2, a popular video game, it scaled machine learning to unprecedented levels, running thousands of chips non-stop for more than ten months.

Exact figures on how much this all costs are scarce. But a paper published in 2019 by researchers at the University of Massachusetts Amherst estimated that training one version of Transformer, another big language model, could cost as much as $3m. Jerome Pesenti, Facebooks head of AI, says that one round of training for the biggest models can cost millions of dollars in electricity consumption.

Facebook, which turned a profit of $18.5bn in 2019, can afford those bills. Those less flush with cash are feeling the pinch. Andreessen Horowitz, an influential American venture-capital firm, has pointed out that many AI startups rent their processing power from cloud-computing firms like Amazon and Microsoft. The resulting billssometimes 25% of revenue or moreare one reason, it says, that AI startups may make for less attractive investments than old-style software companies. In March Dr Mannings colleagues at Stanford, including Fei-Fei Li, an AI luminary, launched the National Research Cloud, a cloud-computing initiative to help American AI researchers keep up with spiralling bills.

The growing demand for computing power has fuelled a boom in chip design and specialised devices that can perform the calculations used in AI efficiently. The first wave of specialist chips were graphics processing units (GPUs), designed in the 1990s to boost video-game graphics. As luck would have it, GPUs are also fairly well-suited to the sort of mathematics found in AI.

Further specialisation is possible, and companies are piling in to provide it. In December, Intel, a giant chipmaker, bought Habana Labs, an Israeli firm, for $2bn. Graphcore, a British firm founded in 2016, was valued at $2bn in 2019. Incumbents such as Nvidia, the biggest GPU-maker, have reworked their designs to accommodate AI. Google has designed its own tensor-processing unit (TPU) chips in-house. Baidu, a Chinese tech giant, has done the same with its own Kunlun chips. Alfonso Marone at KPMG reckons the market for specialised AI chips is already worth around $10bn, and could reach $80bn by 2025.

Computer architectures need to follow the structure of the data theyre processing, says Nigel Toon, one of Graphcores co-founders. The most basic feature of AI workloads is that they are embarrassingly parallel, which means they can be cut into thousands of chunks which can all be worked on at the same time. Graphcores chips, for instance, have more than 1,200 individual number-crunching cores, and can be linked together to provide still more power. Cerebras, a Californian startup, has taken an extreme approach. Chips are usually made in batches, with dozens or hundreds etched onto standard silicon wafers 300mm in diameter. Each of Cerebrass chips takes up an entire wafer by itself. That lets the firm cram 400,000 cores onto each.

Other optimisations are important, too. Andrew Feldman, one of Cerebrass founders, points out that AI models spend a lot of their time multiplying numbers by zero. Since those calculations always yield zero, each one is unnecessary, and Cerebrass chips are designed to avoid performing them. Unlike many tasks, says Mr Toon at Graphcore, ultra-precise calculations are not needed in AI. That means chip designers can save energy by reducing the fidelity of the numbers their creations are juggling. (Exactly how fuzzy the calculations can get remains an open question.)

All that can add up to big gains. Mr Toon reckons that Graphcores current chips are anywhere between ten and 50 times more efficient than GPUs. They have already found their way into specialised computers sold by Dell, as well as into Azure, Microsofts cloud-computing service. Cerebras has delivered equipment to two big American government laboratories.

Moores law isnt possible any more

Such innovations will be increasingly important, for the AIfuelled explosion in demand for computer power comes just as Moores law is running out of steam. Shrinking chips is getting harder, and the benefits of doing so are not what they were. Last year Jensen Huang, Nvidias founder, opined bluntly that Moores law isnt possible any more.

Other researchers are therefore looking at more exotic ideas. One is quantum computing, which uses the counter-intuitive properties of quantum mechanics to provide big speed-ups for some sorts of computation. One way to think about machine learning is as an optimisation problem, in which a computer is trying to make trade-offs between millions of variables to arrive at a solution that minimises as many as possible. A quantum-computing technique called Grovers algorithm offers big potential speed-ups, says Krysta Svore, who leads the Quantum Architectures and Computation Group at Microsoft Research.

Another idea is to take inspiration from biology, which proves that current brute-force approaches are not the only way. Cerebrass chips consume around 15kW when running flat-out, enough to power dozens of houses (an equivalent number of GPUs consumes many times more). A human brain, by contrast, uses about 20W of energyabout a thousandth as muchand is in many ways cleverer than its silicon counterpart. Firms such as Intel and IBM are therefore investigating neuromorphic chips, which contain components designed to mimic more closely the electrical behaviour of the neurons that make up biological brains.

For now, though, all that is far off. Quantum computers are relatively well-understood in theory, but despite billions of dollars in funding from tech giants such as Google, Microsoft and IBM, actually building them remains an engineering challenge. Neuromorphic chips have been built with existing technologies, but their designers are hamstrung by the fact that neuroscientists still do not understand what exactly brains do, or how they do it.

That means that, for the foreseeable future, AI researchers will have to squeeze every drop of performance from existing computing technologies. Mr Toon is bullish, arguing that there are plenty of gains to be had from more specialised hardware and from tweaking existing software to run faster. To quantify the nascent fields progress, he offers an analogy with video games: Were past Pong, he says. Were maybe at Pac-Man by now. All those without millions to spend will be hoping he is right.

This article appeared in the Technology Quarterly section of the print edition under the headline "Machine, learning"

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The cost of training machines is becoming a problem - The Economist

SavantX Inc. has chosen Santa Fe for its corporate research headquarters.Courtesy/EDD

BUSINESS News:

SANTA FE New Mexico Economic Development Department Cabinet Secretary Alicia J. Keyes announced today that a company at the leading edge of quantum computing is relocating some operations to Santa Fe and plans to hire more than 100 employees.

SavantX CEO Ed Heinbockel

After considering locations in Utah, Idaho, Oregon and California, SavantX Inc. has chosen Santa Fe for its corporate research headquarters. The operations, sales and customer support arms of the business will remain in Jackson, Wyo.

The need to diversify our economy has never been clearer, and its truly happening as more and more cutting-edge businesses like SavantX realize the advantages of locating in New Mexico, Gov. Michelle Lujan Gisham said. These are companies that will disrupt the status quo and reshape the future of commerce and industry for the whole country. Its gratifying that New Mexicans will play a significant role in those changes.

SavantX is using its expertise in artificial intelligence and machine learning to solve complex business problems at nuclear power plants, transportation hubs, and in health care. For example, the company is partnering with Fenix Marine Services at Pier 300 on the Port of Los Angeles to optimize logistics on the spacing and placement of shipping containers to better integrate with inbound trucks and freight trains. The results are expected to yield a 50 percent increase in efficiency.

SavantX also has deployed a no-cost AI-enabled web application to help medical and research professionals search and analyze COVID-19 datasets.

SavantX signed a lease for a building at 504 Jose St. in Santa Fe and expects to hire new local employees by the end of July. Positions are listed on the Indeed website. CEO Ed Heinbockel hopes to eventually move to Santa Fe, and six other employees also are planning to move to Santa Fe in 2020. Research efforts will require the hiring of 116 employees over the next 10 years.

The average salary for the jobs will be more than $90,000 annually, with total compensation reaching $170,000 and a payroll of $109 million.

The New Mexico Economic Development Department has pledged $450,000 in economic assistance to the project from its LEDA closing fund and the City of Santa Fe has pledged $50,000.

LEDA and other economic assistance is helping to bring SavantX and its world-class computing expertise to New Mexico. With that comes national recognition in a burgeoning high tech-sector, well-paying jobs, and better opportunities for our students to stay here and build a career, Secretary Keyes said.

As Mayor of Santa Feand an entrepreneur myselfI am delighted to welcome SavantX to our city, Santa Fe Mayor Alan Webber said. We are continuing to grow our technology community, see the digital ecosystem flourish, and attract talented, cutting-edge thinkers and do-ers. Welcome to SavantX!

SavantX was founded by its Chief Executive Officer Ed Heinbockel, who holds an MBA and an engineering degree from Cal Poly, and Chief Science Officer David Ostby, a data scientist with a background in artificial intelligence. Both have experience with startup companies and Heinbockel was named as one of the Top 100 Thinkers of Our Time by Newsweek Magazine.

With its proximity to Los Alamos National Laboratory and Sandia National Labs, Santa Fe has unique advantages for SavantX.

Santa Fe has a built-in workforce from the labs of people already doing quantum computing. In a lot of ways, Santa Fe is the nexus of this new computing, Heinbockel said. New Mexicos assistance from LEDA and the Job Training Incentive Program comes at a time when there is more economic uncertainty. Knowing the incentives are in place was a major factor in bringing SavantX operations to New Mexico.

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Newsweek Named 'Top 100 Thinker Of Our Time' SavantX CEO Ed Heinbockel Brings Research Center To Santa Fe - Los Alamos Daily Post

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Market segment by Type, the product can be split into HardwareSoftwareServices

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The global Quantum Computing report shows deep information about the business outlining, its requirements, required contact information either phone or email and product image of important manufacturers who manufacture the goods or its components for the companies of Quantum Computing. The global keyword market report analysis report similarly reduces the present, past and in future Quantum Computing business strategies that have been followed by the key players, company extent, reasons of development and time period, share and estimate analysis having a place with the predicted circumstances which will give a fair idea to the investor or the company owner about the global keyword market to take decisions according to these analysis reports.It also suggests the business models, innovations, growth and every information about the big manufacturers that will be present the future market estimates.

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

This report vastly covers profiles of the companies who have made it big in this particular field along with their sales data and other data. In conclusion, the Quantum Computing report, demonstrate business enhancement projects, the Quantum Computing deals network, retailers, consumers, suppliers, research findings, reference section, data sources and moreover. Additionally, the Quantum Computing report contains market dynamics such as market restraints, growth drivers, opportunities, service providers, stakeholders, investors, key market players, profile assessment, and challenges of the global market.

For Enquiry before buying report @ https://www.orbismarketreports.com/enquiry-before-buying/67848?utm_source=Puja

About Us : With unfailing market gauging skills, has been excelling in curating tailored business intelligence data across industry verticals. Constantly thriving to expand our skill development, our strength lies in dedicated intellectuals with dynamic problem solving intent, ever willing to mold boundaries to scale heights in market interpretation.

Contact Us : Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155

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Global Quantum Computing Market Expected to Reach Highest CAGR by 2025 Top Players: D-Wave Systems, Google, IBM, Intel, Microsoft, 1QB Information...

The herculean efforts to re-start Dukes campus and medical school research laboratories are nearly complete.

Thousands of lab workers, kept from their benches and equipment for months by the COVID pandemic, are shaking off the cobwebs and getting back to work generating data. But with some significant differences.

I think it'll take me a few weeks to actually get back into the rhythm, said Tatiana Segura, a professor of biomedical engineering who has a large team in two laboratory spaces in Fitzpatrick-CIEMAS.

Segura had asked all of her trainees to review their lab protocols and have a detailed plan for what they should be doing in their newly limited lab hours. But, she notes, It takes time when you start out, like if you are trying to cook or do something you haven't done for a long time, it still takes you a while to remember how to do it.

Each of the reopened labs has been left to decide the finer details about spacing and timing for itself, in a move campus leadership has called states rights. For many labs, that means coordinating through instant messages on Slack and a shared calendar on the cloud. Smaller rooms and shared equipment pose a particular scheduling challenge because of personal spacing requirements.

In all cases, the new safety rules mean fewer people in the lab and a highly structured end to the old free-wheeling, all-hours culture of laboratory work. Now lab workers start each day by recording their temperature and filling out a symptom survey. Their badges give them entry to buildings and elevators that used to be wide open. And they wear masks at all times. When its time to leave, they have to leave.

This whole thing has been pretty challenging for us because we're used to working in teams, said research scientist Stephen Crain, who typically works on Jungsang Kims quantum computing hardware with two grad students in a fourth-floor lab of the Chesterfield Building downtown. Its normally a pretty collaborative effort where, if we get stuck, we kind of work together. But now we're one person at a time. It's just hard to be as productive.

As an assistant professor of chemistry and mother, Amanda Hargrove said she talks about time management with her trainees all the time. Now theyre living it. If you're working between daycare hours, that eight hours is crazy-efficient, she said. So I'm a little interested to see how much more efficient people become in the four hours that they can be there.

Hargrove has split her lab into three four-hour shifts from 8 a.m. to 8 p.m. with the mandated hour for cleaning and leaving the lab between shifts. Its all coordinated through an online calendar and Slack messaging so trainees can arrange when and how they want to work, including taking two 4-hour shifts if need be.

Third-year graduate student Martina Zafferani of Hargroves lab in the French Family Science Center prefers to work 10 or 12 hours at a time, much of it standing. Now shes taking two times four hours, with an hour between to go outside, have lunch and get some Vitamin D.

Zafferani works in a lab that typically might hold up to 15 workers, from undergraduates to post-docs, flowing in and out during the day. On one in early June, it was just her and fifth-year grad student Sarah Wicks, wearing masks with their heads down, trying to restart their experiments on making molecules to control RNA and getting as much out of their limited time as they can.

It hasn't been completely lonesome, Wicks said. But I do miss the chatter of other group members being here. We usually have about nine lab members working, we have music playing, equipment humming, and to have such silence now does make it lonelier than it was.

Still, its great to be back, said first-year masters student Ameya Chaudhari as he returned to work on bio-compatible polymers in Seguras lab. I had been feeling lethargic at home, but now Im energized being back in the lab, Chaudhari said. He looked long overdue for a haircut but was wearing one of the labs sharp, Duke-blue lab coats.

Duke labs that were working on questions related to COVID-19 stayed on duty throughout the shutdown, of course. And others, including Hargrove and dermatology associate professor Amanda MacLeod, are shifting some of their attention to COVID-adjacent questions as they come back.

Its weird being careful with everyone around, says MSRB-III postdoctoral researcher Paula Mariottoni of MacLeods group. She did a sort of pausing dance to move from her bench to a tissue culture room as a colleague walked past. Even if its a slow pace, moving forward is good, Mariottoni said.

Red tape Xs mark the floors about eight feet apart in each bay of MacLeods MSRB-III laboratory space, indicating where people should stand to communicate or pass. The elevator is designated for one rider at a time, up only; exiting is by a designated stairway down.

Third-year medical student Vivian Lei was working in the next bay over in the MacLeod group. It was designed for four, but occupied by just her. My time during lockdown was reexamining data, she said from behind a white cloth mask. Weirdly enough, this was one of the most productive times in our lab.

The timing of the shutdown in late March hit MacLeods group perfectly, in fact. They had just completed a move from Duke South to MSRB-III, and anticipating the move would be disruptive, the group did a lot of experiments in January and February to create a hoard of data that kept them busy.

Our people had run a lot of the wet-lab experiments up front, and now all the analysis had to be done, said MacLeod, an associate professor of dermatology who is studying how the skin helps combat pathogens including viruses -- as the bodys first line of defense. During the shutdown, the lab submitted two manuscripts, three grant applications and a few fellowship applications.

Environmental toxicologist Richard DiGiulios lab in the Levine Science Research Center includes colorful tanks of living fish, so somebody was coming in to feed them the entire time, even though the science stopped.

We didn't see anybody else, except for the occasional custodial worker, for 2 1/2 months, said DiGiulio lab manager Melissa Chernick. We just never intersected with anybody, which I thought was good because it made me feel more comfortable that nobody was in the building -- it was less possible contacts.

Now DiGiulio, the Sally Kleberg Distinguished Professor in the Nicholas School of Environment, is waiting to hear about safety rules for field work. The labs supply of killifish, collected from a Superfund site on the Elizabeth River in Virginia, is beginning to wane. To get more from the river, we all pack in a van for four hours up there and four back, Chernick said.

The lab buildings feel different without students studying or hanging out in public areas and hallways. The coffee shops are shuttered. There are no voices, no footfalls, just whooshing air.

It was very creepy to be alone in French, said Zafferani, who was one of the first people back in the building. Still, it was better than working at home, she said.

I think the beginning is going to be a little bit slower maybe than everyone hopes, MacLeod said. It's challenging, but it's doable.

And even though you have this mask on, she said, you can still talk and be friendly and kind to everyone.

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Duke's Labs Are Back in Business, But In a New Way - Duke Today

Overview:

Quantum cryptographyis a new method for secret communications that provides the assurance of security of digital data. Quantum cryptography is primarily based on the usage of individual particles/waves of light (photon) and their essential quantum properties for the development of an unbreakable cryptosystem, primarily because it is impossible to measure the quantum state of any system without disturbing that system. It is hypothetically possible that other particles could be used, but photons offer all the necessary qualities needed, the their behavior is comparatively understandable, and they are the information carriers in optical fiber cables, the most promising medium for very high-bandwidth communications.

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Quantum computing majorly focuses on the growing computer technology that is built on the platform of quantum theory which provides the description about the nature and behavior of energy and matter at quantum level. The fame of quantum mechanics in cryptography is growing because they are being used extensively in the encryption of information. Quantum cryptography allows the transmission of the most critical data at the most secured level, which in turn, propels the growth of the quantum computing market. Quantum computing has got a huge array of applications.

Market Analysis:

According to Infoholic Research, the Global Quantum cryptography Market is expected to reach $1.53 billion by 2023, growing at a CAGR of around 26.13% during the forecast period. The market is experiencing growth due to the increase in the data security and privacy concerns. In addition, with the growth in the adoption of cloud storage and computing technologies is driving the market forward. However, low customer awareness about quantum cryptography is hindering the market growth. The rising demands for security solutions across different verticals is expected to create lucrative opportunities for the market.

Market Segmentation Analysis:

The report provides a wide-ranging evaluation of the market. It provides in-depth qualitative insights, historical data, and supportable projections and assumptions about the market size. The projections featured in the report have been derived using proven research methodologies and assumptions based on the vendors portfolio, blogs, whitepapers, and vendor presentations. Thus, the research report serves every side of the market and is segmented based on regional markets, type, applications, and end-users.

Countries and Vertical Analysis:

The report contains an in-depth analysis of the vendor profiles, which include financial health, business units, key business priorities, SWOT, strategy, and views; and competitive landscape. The prominent vendors covered in the report include ID Quantique, MagiQ Technologies, Nucrypt, Infineon Technologies, Qutools, QuintenssenceLabs, Crypta Labs, PQ Solutions, and Qubitekk and others. The vendors have been identified based on the portfolio, geographical presence, marketing & distribution channels, revenue generation, and significant investments in R&D.

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Competitive Analysis

The report covers and analyzes the global intelligent apps market. Various strategies, such as joint ventures, partnerships, collaborations, and contracts, have been considered. In addition, as customers are in search of better solutions, there is expected to be a rising number of strategic partnerships for better product development. There is likely to be an increase in the number of mergers, acquisitions, and strategic partnerships during the forecast period.

Companies such as Nucrypt, Crypta Labs, Qutools, and Magiq Technologies are the key players in the global Quantum Cryptography market. Nucrypt has developed technologies for emerging applications in metrology and communication. The company has also produced and manufactured electronic and optical pulsers. In addition, Crypta Labs deals in application security for devices. The company deals in Quantum Random Number Generator products and solutions and Internet of Things (IoT). The major sectors the company is looking at are transport, military and medical.

The report includes the complete insight of the industry, and aims to provide an opportunity for the emerging and established players to understand the market trends, current scenario, initiatives taken by the government, and the latest technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and to take informed decisions.

Regional Analysis

The Americas held the largest chunk of market share in 2017 and is expected to dominate the quantum cryptography market during the forecast period. The region has always been a hub for high investments in research and development (R&D) activities, thus contributing to the development of new technologies. The growing concerns for the security of IT infrastructure and complex data in America have directed the enterprises in this region to adopt quantum cryptography and reliable authentication solutions.

Benefits

The report provides an in-depth analysis of the global intelligent apps market aiming to reduce the time to market the products and services, reduce operational cost, improve accuracy, and operational performance. With the help of quantum cryptography, various organizations can secure their crucial information, and increase productivity and efficiency. In addition, the solutions are proven to be reliable and improve scalability. The report discusses the types, applications, and regions related to this market. Further, the report provides details about the major challenges impacting the market growth.

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Quantum Cryptography Market to Grow at Robust CAGR in the COVID-19 Lockdown Scenario - Cole of Duty