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Nov. 11, 2020 Since 1987, the Association for Computing Machinery has awarded the annual Gordon Bell Prize to recognize outstanding achievements in high-performance computing (HPC). Presented each year at the International Conference for High-Performance Computing, Networking, Storage and Analysis (SC), the prizes not only reward innovative projects that employ HPC for applications in science, engineering, and large-scale data analytics but also provide a timeline of milestones in parallel computing.

As a frequent home to the worlds most powerful and smartest scientific supercomputers, the US Department of Energys (DOEs) Oak Ridge National Laboratory (ORNL) has hosted many previous Gordon Bell honorees on its HPC systems. The Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility located at ORNL, manages these systems and makes them available to scientists around the world to accelerate scientific discovery and engineering progress. Consequently, the OLCF has provided the HPC systems for 25 previous Gordon Bell Prize finalists and eight winners, including last years team from ETH Zrich.

This year, four projects that used ORNLs IBM AC922 Summit supercomputer are finalists. The 2020 Gordon Bell Prize will be award November 19 at SC20. Here are the finalists that used Summit.

DeePMD-kit: A New Paradigm for Molecular Dynamics Modeling

The code produced by Team DeePMD, with its ability to scale to huge numbers of atoms, while retaining chemical accuracy, is poised to transform the field of materials research. Applications to other fields will surely follow. Michael Klein, Laura H. Camell Professor of Science, Temple University

Molecular dynamics modeling has become a primary tool in scientific inquiry, allowing scientists to analyze the movements of interacting atoms over a set period of time, which helps them determine the properties of different materials or organisms. These computer simulations often lead the way in designing everything from new drugs to improved alloys. However, the two most popular methodologies come with caveats.

Classical molecular dynamics (MD), using Newtonian physics, can simulate trillions of particles on a modern supercomputerhowever, its accuracy for more intricate simulations has limitations. Ab initio (from the beginning) molecular dynamics (AIMD), using quantum physics at each time step, can produce much more accurate resultsbut its inherent computational complexity limits the size and time span of its simulations. But what if there was a way to bridge the gap between MD and AIMD, to produce complex simulations that are both large and accurate?

With the power of ORNLs Summit supercomputer, researchers from Lawrence Berkeley National Laboratorys Computational Research Division; the University of California, Berkeley; the Institute of Applied Physics and Computational Mathematics, Peking University; and Princeton University successfully tested a software package that offers a potential solution: DeePMD-kit, named for deep potential molecular dynamics.

The team refers to DeePMD-kit as a HPC+AI+Physical model in that it combines high-performance computing (HPC), artificial intelligence (AI), and physical principles to achieve both speed and accuracy. It uses a neural network to assist its calculations by approximating the ab initio data, thereby reducing the computational complexity from cubic to linear scaling.

Simulating a block of copper atoms, the team put DeePMD-kit to the test on Summit with the goal of seeing how far they could push the simulations size and timescales beyond AIMDs accepted limitations. They were able to simulate a system of 127.4 million atomsmore than 100 times larger than the current state of the art. Furthermore, the simulation achieved a time-to-solution mark of at least 1,000 times faster at 2.5 nanoseconds per day for mixed-half precision, with a peak performance of 275 petaflops (one thousand million million floating-point operations per second) for mixed-half precision.

By combining physical principles and the representation power of deep neural networks, the Deep Potential method can achieve very good accuracy, especially for complex problems, said Weile Jia, a postdoc in applied mathematics in Professor Lin Lins group at the Math Department of UC Berkeley, who co-led the project with Linfeng Zhang of Princeton. Then we reorganize the data layout for bigger granularity on GPU and use data compression to significantly speedup the bottleneck. The neural network operators are optimized to the extreme, and most importantly, we successfully use half-precision in our code without losing accuracy.

Square Kilometre Array: Massive Data Processing to Explore the Universe

The innovative results already achieved and goals being pursued by this international team will greatly benefit the Next Generation Very Large Array, the Square Kilometre Array, and the next generation of radio interferometer facilities around the world. Tony Beasley, Director, National Radio Astronomy Observatory

Scheduled to begin construction in 2021, the Square Kilometre Array (SKA) promises to become one of the biggest Big Science projects of all time (in physical size): a radio telescope array with a combined collecting area of over 1 square kilometer, or 1 million square meters. Once completed in the deserts of South Africa and Australia in the late 2020s, SKAs thousands of dishes and low-frequency antennas will plumb the universe to figure out its mysteries.

SKAs mission ultimately means it will produce massive amounts of informationan estimated 600 petabytes of data per year. Collecting, storing, and analyzing that data will be critical in producing SKAs scientific discoveries. How will it be managed?

Building an end-to-end data-processing system on such an unprecedented scale is the task of an international team of radio astronomers, computer scientists, and software engineers. Workflow experts from the International Centre for Radio Astronomy Research (ICRAR) in Australia and the Shanghai Astronomical Observatory (SHAO) in China are developing the Daliuge workflow management system; GPU experts from Oxford University are optimizing the performance of the data generator; and input/output (I/O) experts at ORNL are producing I/O middleware based on the ORNL-developed Adaptable IO System (ADIOS). These three core software packages were completely developed by the team, with the original scope of running on top supercomputers.

Because SKA does not yet exist, its huge data output was simulated on Summit in order to test the teams work, running a complete end-to-end workflow for a typical 6-hour SKA Phase 1 Low Frequency Array observation. The team used 99 percent of Summit, achieving 130 petaflops peak performance for single-precision, 247 gigabytes per second data generation rate, and 925 gigabytes per second pure I/O rate.

For the first time, an end-to-end SKA data-processing workflow was executed in a production environment. It helps the SKA communityas well as the entire radio astronomy communitydetermine critical design factors for multi-billion-dollar next generation radio telescopes, said Ruonan Wang, a software engineer in ORNLs Scientific Data Group who works on the project. It validated our ability, from both software and hardware perspectives, to process a key science case of SKA, which will answer some of the fundamental questions of our universe.

DSNAPSHOT: An Accelerated Approach to Literature-Based Discovery

The DSNAPSHOT algorithm approach enables the identification of meaningful paths and novel relations on a previously unseen scale. Consequently, it moves the biomedical research community closer to a framework for analyzing how novel relations can be identified across the entire body of scientific literature. Michael Weiner, PhD, VP AxioMx, Molecular Sciences and Head, Global Research of Abcam

In 1986, the late information scientist Don Swanson introduced the concept of undiscovered public knowledge in the field of biomedical research. His idea was both intriguing and straightforward: Out of the millions of published pieces of medical literature, what if there are yet unseen connections between their findings that could lead to new treatments? If, for example, A affects B in one study and B affects C in another, perhaps A and C have undiscovered commonalities worth investigating. Swanson proved his point by analyzing unrelated papers for such links, leading to hypothetical treatments that were later supported by clinical studies, such as taking magnesium supplements to help prevent migraine headaches. This process became known as Swanson Linking.

But in light of the enormous size of scientific literature in existence, mining it for undiscovered connections cannot be effectively conducted on a mass scale by mere humans. For example, the US National Library of Medicines PubMed database contains over 30 million citations and abstracts for biomedical literature. How can researchers possibly track that much information in its totality and find the patterns that may help identify new treatments?

One answer may be data-mining algorithms optimized for GPU-accelerated supercomputers such as ORNLs Summit. When the federal government mobilized its national labs in the fight against COVID-19 in March, a team of ORNL and Georgia Tech researchers was assembled by ORNL computer scientist Ramakrishnan Kannan and Thomas E. Potok, head of ORNLs Data and AI Systems Section of the Computer Science and Mathematics Division. The teams mission was to investigate new ways of searching large-scale bodies of scholarly literatureand they ultimately found a way to conduct Swanson Linking on huge datasets at unprecedented speed.

Dasha Herrmannova from Kannans team began by creating a graph dataset based on Semantic MEDLINEa dataset of biomedical concepts and the relations between themextracted from PubMed. Then they expanded the graph with information extracted from the COVID-19 Open Research Dataset (CORD-19), resulting in a dataset of 18.5 million nodes representing concepts and papers, with 213 million relationships between them.

To search this massive dataset (via knowledge graph representations) for potential COVID-19 treatments, the team developed a new high-performance implementation of the Floyd-Warshall algorithm. The classic algorithm, originally published in 1962, determines the shortest distances between every pair of vertices in a given graph. (In terms of literature-based discovery, the shortest paths are usually more likely to reveal new connections between scholarly works.) Wanting to overcome the computational bottleneck of tackling massive graphs, Kannan, Piyush Sao, Hao Lu, and Robert Patton from ORNL, in collaboration with Vijay Thakkar and Rich Vuduc from Georgia Tech, optimized their version of the algorithm for distributed-memory parallel computers accelerated by GPUs. They named it Distributed Accelerated Semiring All-Pairs Shortest Path (DSNAPSHOT).

In effect, the teams DSNAPSHOT is a supercharged version of Floyd-Warshall, able to identify the shortest paths in huge graphs in a matter of minutes. Using 90 percent of the Summit supercomputeror 4,096 nodes, adding up to 24,576 GPUsthe team was able to compute an All-Pairs Shortest Path computation on a graph with 4.43 million vertices in 21.3 minutes. Peak performance reached 136 petaflops for single-precision. If every person on Earth completed one calculation per second, it would take the worlds population (~7 billion) 7 and a half months to complete what DSNAPSHOT can do in 1 second on Summit.

To the best of our knowledge, DSNAPSHOT is the first method capable of calculating shortest path between all pairs of entities in a biomedical knowledge graph, thereby enabling the discovery of meaningful relations across the whole of biomedical knowledge, Kannan said. Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligencedriven natural language processing workflows at scale.

BerkeleyGW: A New View into Excited-State Electrons

The BerkeleyGW teams demonstration of excited-state calculations with the GW method for 1,000-atom systems on accessible HPC facilities will be a game-changer. Researchers with diverse interests will be able to pursue fundamental understanding of excited states and physical processes in materials systems including novel two-dimensional semiconductors, electrochemical interfaces, organic molecular energy harvesting systems, and materials proposed for quantum information systems. Mark S. Hybertsen, Group Leader, Theory & Computation Group Center for Functional Nanomaterials, Brookhaven National Laboratory

Historical epochs are often delineated by the materials that helped shape civilization, from the Stone Age to the Steel Age. Our current period is often referred to as the Silicon Agebut while those earlier eras were characterized by the structural properties of their predominant materials, silicon is different. Rather than ushering in new ways of building big things, its technological leap takes place on an atomic level, facilitating an information revolution.

Used as the main material in integrated circuits (AKA, the microchip), silicon has enabled the world of data processing we currently live in, from ever-more-powerful computers to unavoidable handheld devices. Central to its success has been the ability of chip designers to engineer these circuits to be increasingly faster and smaller, yet with more capacity as they add more and more transistors. But can microprocessor architects keep up with Moores law and continue to double the number of transistors in an integrated circuit every 2 years?

One route forward may be found in the work of a team of six physicists, materials scientists, and HPC specialists from the Berkeley Lab, UC Berkeley, and Stanford University that performed the largest-ever study of excited-state electrons using ORNLs Summit supercomputer. Understanding and controlling such electronic excitation in silicon and other materials is key to designing the electronic and optoelectronic devices that have sparked the current information era. Whats more, the accurate modeling of excited-state properties of electrons in materials plays a crucial role in the rational design of other transformative technologies, including photovoltaics, batteries, and qubits for quantum information and quantum computing. In essence, the teams high-performance calculations could help design new materials for these next generation technologies.

A state-of-the-art tool for determining excitations in materials is the GW method, an approach for calculating the self-energy (the quantum energy that a particle acquired from interactions with its surrounding environment) of a system of interacting electrons. The team adapted its own software package: BerkeleyGWa quantum many-body perturbation theory code for excited statesto run on Summits GPU accelerators.

The teams study of a system of defects in silicon and silicon carbide resulted in groundbreaking performance: the largest high-fidelity GW calculations ever made, with 10,986 valence electrons. By running on the entire Summit supercomputer, they also achieved 105.9 petaflops of double-precision performance with a time to solution of roughly 10 minutes.

Whats really exciting about these numbers is that together they usher in the practical use of the high-fidelity GW method to the study of realistic complex materials, said Jack Deslippe, team leader and head of the Applications Performance Group at the National Energy Research Scientific Computing Center, or NERSC. These will be materials with defects, with interfaces, and with large geometries that drive real device design in quantum information, energy generation and storage, and next-gen electronics.

UT-Battelle LLC manages Oak Ridge National Laboratory for DOEs Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOEs Office of Science is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science.

More info: https://www.olcf.ornl.gov/2020/11/10/four-teams-using-ornls-summit-supercomputer-named-finalists-in-2020-gordon-bell-prize/

Source: COURY TURCZYN, ORNL

Original post:
Four Teams Using ORNL's Summit Supercomputer Named Finalists in 2020 Gordon Bell Prize - HPCwire

Supermarket aisles filled with fresh produce are probably not where you would expect to discover some of the first benefits of quantum computing.

But Canadian grocery chain Save-On-Foods has become an unlikely pioneer, using quantum technology to improve the management of in-store logistics. In collaboration with quantum computing company D-Wave, Save-On-Foods is using a new type of computing, which is based on the downright weird behaviour of matter at the quantum level. And it's already seeing promising results.

The company's engineers approached D-Wave with a logistics problem that classical computers were incapable of solving. Within two months, the concept had translated into a hybrid quantum algorithm that was running in one of the supermarket stores, reducing the computing time for some tasks from 25 hours per week down to mere seconds.

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Save-On-Foods is now looking at expanding the technology to other stores, and exploring new ways that quantum could help with other issues. "We now have the capability to run tests and simulations by adjusting variables and see the results, so we can optimize performance, which simply isn't feasible using traditional methods," a Save-On-Foods spokesperson tells ZDNet.

"While the results are outstanding, the two most important things from this are that we were able to use quantum computing to attack our most complex problems across the organization, and can do it on an ongoing basis."

The remarkable properties of quantum computing boil down to the behaviour of qubits -- the quantum equivalent of classical bits that encode information for today's computers in strings of 0s and 1s. But contrary to bits, which can be represented by either 0 or 1, qubits can take on a state that is quantum-specific, in which they exist as 0 and 1 in parallel, or superposition.

Qubits, therefore, enable quantum algorithms to run various calculations at the same time, and at exponential scale: the more qubits, the more variables can be explored, and all in parallel. Some of the largest problems, which would take classical computers tens of thousands of years to explore with single-state bits, could be harnessed by qubits in minutes.

The challenge lies in building quantum computers that contain enough qubits for useful calculations to be carried out. Qubits are temperamental: they are error-prone, hard to control, and always on the verge of falling out of their quantum state. Typically, scientists have to encase quantum computers in extremely cold, large-scale refrigerators, just to make sure that qubits remain stable. That's impractical, to say the least.

This is, in essence, why quantum computing is still in its infancy. Most quantum computers currently work with less than 100 qubits, and tech giants such as IBM and Google are racing to increase that number in order to build a meaningful quantum computer as early as possible. Recently, IBM ambitiously unveiled a roadmap to a million-qubit system, and said that it expects a fault-tolerant quantum computer to be an achievable goal during the next ten years.

IBM's CEO Arvind Krishna and director of research Dario Gil in front of a ten-foot-tall super-fridge for the company's next-generation quantum computers.

Although it's early days for quantum computing, there is still plenty of interest from businesses willing to experiment with what could prove to be a significant development. "Multiple companies are conducting learning experiments to help quantum computing move from the experimentation phase to commercial use at scale," Ivan Ostojic, partner at consultant McKinsey, tells ZDNet.

Certainly tech companies are racing to be seen as early leaders. IBM's Q Network started running in 2016 to provide developers and industry professionals with access to the company's quantum processors, the latest of which, a 65-qubit device called Hummingbird, was released on the platform last month. Recently, US multinational Honeywell took its first steps on the quantum stage, making the company's trapped-ion quantum computer available to customers over the cloud. Rigetti Computing, which has been operating since 2017, is also providing cloud-based access to a 31-qubit quantum computer.

Another approach, called quantum annealing, is especially suitable for optimisation tasks such as the logistics problems faced by Save-On-Foods. D-Wave has proven a popular choice in this field, and has offered a quantum annealer over the cloud since 2010, which it has now upgraded to a 5,000-qubit-strong processor.

A quantum annealing processor is much easier to control and operate than the devices that IBM, Honeywell and Rigetti are working on, which are called gate-model quantum computers. This is why D-Wave's team has already hit much higher numbers of qubits. However, quantum annealing is only suited to specific optimisation problems, and experts argue that the technology will be comparatively limited when gate-model quantum computers reach maturity.

The suppliers of quantum processing power are increasingly surrounded by third-party companies that act as intermediaries with customers. Zapata, QC Ware or 1QBit, for example, provide tools ranging from software stacks to training, to help business leaders get started with quantum experiments.

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In other words, the quantum ecosystem is buzzing with activity, and is growing fast. "Companies in the industries where quantum will have the greatest potential for complete disruption should get involved in quantum right now," says Ostojic.

And the exponential compute power of quantum technologies, according to the analyst, will be a game-changer in many fields. Qubits, with their unprecedented ability to solve optimisation problems, will benefit any organisation with a supply chain and distribution route, while shaking up the finance industry by maximising gains from portfolios. Quantum-infused artificial intelligence also holds huge promise, with models expected to benefit from better training on bigger datasets.

One example: by simulating molecular interactions that are too complex for classical computers to handle, qubits will let biotech companies fast-track the discovery of new drugs and materials. Microsoft, for example, has already demonstrated how quantum computers can help manufacture fertilizers with better yields. This could have huge implications for the agricultural sector, as it faces the colossal task of sustainably feeding the growing global population in years to come.

Chemistry, oil and gas, transportation, logistics, banking and cybersecurity are often cited as sectors that quantum technology could significantly transform. "In principle, quantum will be relevant for all CIOs as it will accelerate solutions to a large range of problems," says Ostojic. "Those companies need to become owners of quantum capability."

Chemistry, oil and gas, transportation, logistics, banking or cybersecurity are among the industries that are often pointed to as examples of the fields that quantum technology could transform.

There is a caveat. No CIO should expect to achieve too much short-term value from quantum computing in its current form. However fast-growing the quantum industry is, the field remains defined by the stubborn instability of qubits, which still significantly limits the capability of quantum computers.

"Right now, there is no problem that a quantum computer can solve faster than a classical computer, which is of value to a CIO," insists Heike Riel, head of science and technology at IBM Research Quantum Europe. "But you have to be very careful, because the technology is evolving fast. Suddenly, there might be enough qubits to solve a problem that is of high value to a business with a quantum computer."

And when that day comes, there will be a divide between the companies that prepared for quantum compute power, and those that did not. This is what's at stake for business leaders who are already playing around with quantum, explains Riel. Although no CIO expects quantum to deliver value for the next five to ten years, the most forward-thinking businesses are already anticipating the wave of innovation that the technology will bring about eventually -- so that when it does, they will be the first to benefit from it.

This means planning staffing, skills and projects, and building an understanding of how quantum computing can help solve actual business problems. "This is where a lot of work is going on in different industries, to figure out what the true problems are, which can be solved with a quantum computer and not a classical computer, and which would make a big difference in terms of value," says Riel.

Riel points to the example of quantum simulation for battery development, which companies like car manufacturer Daimler are investigating in partnership with IBM. To increase the capacity and speed-of-charging of batteries for electric vehicles, Daimler's researchers are working on next-generation lithium-sulfur batteries, which require the alignment of various compounds in the most stable configuration possible. To find the best placement of molecules, all the possible interactions between the particles that make up the compound's molecules must be simulated.

This task can be carried out by current supercomputers for simple molecules, but a large-scale quantum solution could one day break new ground in developing the more complex compounds that are required for better batteries.

"Of course, right now the molecules we are simulating with quantum are small in size because of the limited size of the quantum computer," says Riel. "But when we scale the next generation of quantum computers, then we can solve the problem despite the complexity of the molecules."

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Similar thinking led oil and gas giant ExxonMobilto join the network of companies that are currently using IBM's cloud-based quantum processors. ExxonMobil started collaborating with IBM in 2019, with the objective of one day using quantum to design new chemicals for low energy processing and carbon capture.

The company's director of corporate strategic research Amy Herhold explains that for the past year, ExxonMobil's scientists have been tapping IBM's quantum capabilities to simulate macroscopic material properties such as heat capacity. The team has focused so far on the smallest of molecules, hydrogen gas, and is now working on ways to scale the method up to larger molecules as the hardware evolves.

A number of milestones still need to be achieved before quantum computing translates into an observable business impact, according to Herhold. Companies will need to have access to much larger quantum computers with low error rates, as well as to appropriate quantum algorithms that address key problems.

"While today's quantum computers cannot solve business-relevant problems -- they are too small and the qubits are too noisy -- the field is rapidly advancing," Herhold tells ZDNet. "We know that research and development is critical on both the hardware and the algorithm front, and given how different this is from classical computing, we knew it would take time to build up our internal capabilities. This is why we decided to get going."

Herhold anticipates that quantum hardware will grow at a fast pace in the next five years. The message is clear: when it does, ExxonMobil's research team will be ready.

One industry that has shown an eager interest in quantum technology is the financial sector. From JP Morgan Chase's partnerships with IBM and Honeywell, to BBVA's use of Zapata's services, banks are actively exploring the potential of qubits, and with good reason. Quantum computers, by accounting for exponentially high numbers of factors and variables, could generate much better predictions of financial risk and uncertainty, and boost the efficiency of key operations such as investment portfolio optimisation or options pricing.

Similar to other fields, most of the research is dedicated to exploring proof-of-concepts for the financial industry. In fact, when solving smaller problems, scientists still run quantum algorithms alongside classical computers to validate the results.

"The classical simulator has an exact answer, so you can check if you're getting this exact answer with the quantum computer," explains Tony Uttley, president of Honeywell Quantum Solutions, as he describes the process of quantum options pricing in finance.

"And you better be, because as soon as we cross that boundary, where we won't be able to classically simulate anymore, you better be convinced that your quantum computer is giving you the right answer. Because that's what you'll be taking into your business processes."

Companies that are currently working on quantum solutions are focusing on what Uttley calls the "path to value creation". In other words, they are using quantum capabilities as they stand to run small-scale problems, building trust in the technology as they do so, while they wait for capabilities to grow and enable bigger problems to be solved.

In many fields, most of the research is dedicated to exploring proof-of-concepts for quantum computing in industry.

Tempting as it might be for CIOs to hope for short-term value from quantum services, it's much more realistic to look at longer timescales, maintains Uttley. "Imagine you have a hammer, and somebody tells you they want to build a university campus with it," he says. "Well, looking at your hammer, you should ask yourself how long it's going to take to build that."

Quantum computing holds the promise that the hammer might, in the next few years, evolve into a drill and then a tower crane. The challenge, for CIOs, is to plan now for the time that the tools at their disposal get the dramatic boost that's expected by scientists and industry players alike.

It is hard to tell exactly when that boost will come. IBM's roadmap announces that the company will reach 1,000 qubits in 2023, which could mark the start of early value creation in pharmaceuticals and chemicals, thanks to the simulation of small molecules. But although the exact timeline is uncertain, Uttley is adamant that it's never too early to get involved.

"Companies that are forward-leaning already have teams focused on this and preparing their organisations to take advantage of it once we cross the threshold to value creation," he says. "So what I tend to say is: engage now. The capacity is scarce, and if you're not already at the front of the line, it may be quite a while before you get in."

Creating business value is a priority for every CIO. At the same time, the barrier to entry for quantum computing is lowering every time a new startup emerges to simplify the software infrastructure and assist non-experts in kickstarting their use of the technology. So there's no time to lose in embracing the technology. Securing a first-class spot in the quantum revolution, when it comes, is likely to be worth it.

Read this article:
Quantum computers are coming. Get ready for them to change everything - ZDNet

Quantum physics promises huge advances not just in quantum computing but also in a quantum internet a next-generation framework for transferring data from one place to another. Scientists have now invented technology suitable for a quantum modem that could act as a network gateway.

What makes a quantum internet superior to the regular, existing internet that you're reading this through is security: interfering with the data being transmitted with quantum techniques would essentially break the connection. It's as close to unhackable as you can possibly get.

As with trying to produce practical, commercial quantum computers though, turning the quantum internet from potential to reality is taking time not surprising, considering the incredibly complex physics involved. A quantum modem could be a very important step forward for the technology.

"In the future, a quantum internet could be used to connect quantum computers located in different places, which would considerably increase their computing power!" says physicist Andreas Reiserer, from the Max Planck Institute in Germany.

Quantum computing is built around the idea of qubits, which unlike classical computer bits can store several states simultaneously. The new research focuses on connecting stationary qubits in a quantum computer with moving qubits travelling between these machines.

That's a tough challenge when you're dealing with information that's stored as delicately as it is with quantum physics. In this setup, light photons are used to store quantum data in transit, photons that are precisely tuned to the infrared wavelength of laser light used in today's communication systems.

That gives the new system a key advantage in that it'll work with existing fibre optic networks, which would make a quantum upgrade much more straightforward when the technology is ready to roll out.

In figuring out how to get stored qubits at rest reacting just right with moving infrared photons, the researchers determined that the element erbium and its electrons were best suited for the job but erbium atoms aren't naturally inclined to make the necessary quantum leap between two states. To make that possible, the static erbium atoms and the moving infrared photons are essentially locked up together until they get along.

Working out how to do this required a careful calculation of the space and conditions needed. Inside their modem, the researchers installed a miniature mirrored cabinet around a crystal made of ayttrium silicate compound. This set up was then was cooled to minus 271 degrees Celsius (minus 455.8 degrees Fahrenheit).

The modem mirror cabinet. (Max Planck Institute)

The cooled crystal kept the erbium atoms stable enough to force an interaction, while the mirrors bounced the infrared photons around tens of thousands of times essentially creating tens of thousands of chances for the necessary quantum leap to happen. The mirrors make the system 60 times faster and much more efficient than it would be otherwise, the researchers say.

Once that jump between the two states has been made, the information can be passed somewhere else. That data transfer raises a whole new set of problems to be overcome, but scientists are busy working on solutions.

As with many advances in quantum technology, it's going to take a while to get this from the lab into actual real-world systems, but it's another significant step forward and the same study could also help in quantum processors and quantum repeaters that pass data over longer distances.

"Our system thus enables efficient interactions between light and solid-state qubits while preserving the fragile quantum properties of the latter to an unprecedented degree," write the researchers in their published paper.

The research has been published in Physical Review X.

Read more:
A Modem With a Tiny Mirror Cabinet Could Help Connect The Quantum Internet - ScienceAlert

Until recently an abstract concept, quantum computing is gaining notice in several industries, including financial services, manufacturing and logistics.

In June, for example, JPMorgan Chase published data on its experiments using Honeywells quantum technology, describing its efforts to produce a quantum oracle, or to use math to better predict the future. The financial services giant is accessing the technology directly via API, according to Honeywell Quantum Solutions President Tony Uttley, who says the company is interested in tasks such as optimization around trading strategies and fraud detection.

The JPMorgan Chase study, while academic in nature, is being received in computer science and business circles as an exciting development.

Now you can actually start to use real quantum algorithms on real quantum computers, understand how they work, which classes are working better than others, and start to pinpoint those use cases you think are going to be the most profound, Uttley says.

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Instead of the binary 1s and 0s traditional computers use, quantum computing involves quantum bits, or qubits, which can be read as 1s, 0s or both.

That seemingly subtle difference will allow quantum computers to process massive amounts of information, solving drastically more complex problems than a regular computer would be able to in less time in the near future, according to Paul Smith-Goodson, quantum computing analyst with Moor Insights & Strategy.

While quantum usage is still in its early stages, several providers are offering cloud access to the technology, Smith-Goodson says. Its come a long way much faster than what was originally anticipated. A lot of companies are doing experimenting using quantum computing.

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IBM has offered cloud access to quantum computers since 2016 via its website-based IBM Quantum Experience; nearly 250,000 people have registered to do so, says Robert Sutor, vice president of IBM quantum ecosystem development.

We have democratized access to quantum computers since the very beginning because we felt it was such a new technology, and we have to get people ready, Sutor says.

Quantum computing still has some distance to go to reach its full potential. For now, error rates are too high, producing what researchers call noise in the data the machines produce.

The more qubits you have, the more noise you generate, he says. To do a really serious type of quantum computing, to model or create a new drug or simulate a very complex chemical, youre going to need millions to billions of qubits. Right now, were just not at the stage where we can scale up to that point because we have limitations with noise.

But the technologys potential is irresistible, and big companies are exploring it. Aerospace company Boeing, for example, is using it to model the movement of air and water over surfaces, and its helping Daimler Mercedes-Benz, in its work to create new lithium car batteries.

In this very short period of time, we have gotten people involved with business use cases: applications like chemistry and looking at how to do some aspect of artificial intelligence better, Sutor says. Financial companies are asking, How do we get the most accurate view of the price of a financial portfolio? People are on track to take better advantage as we create more powerful machines.

More here:
Quantum Computing Is Moving from Theory to Reality - BizTech Magazine

Walk into a quantum lab where scientists trap ions, and you'll find benchtops full of mirrors and lenses, all focusing lasers to hit an ion trapped in place above a chip. By using lasers to control ions, scientists have learned to harness ions as quantum bits, or qubits, the basic unit of data in a quantum computer. But this laser setup is holding research back making it difficult to experiment with more than a few ions and to take these systems out of the lab for real use.

Now, MIT Lincoln Laboratory researchers have developed a compact way to deliver laser light to trapped ions. In a recent paper published in Nature, the researchers describe a fiber-optic block that plugs into the ion-trap chip, coupling light to optical waveguides fabricated in the chip itself. Through these waveguides, multiple wavelengths of light can be routed through the chip and released to hit the ions above it.

It's clear to many people in the field that the conventional approach, using free-space optics such as mirrors and lenses, will only go so far, says Jeremy Sage, an author on the paper and senior staff in Lincoln Laboratory's Quantum Information and Integrated Nanosystems Group. If the light instead is brought onto the chip, it can be directed around to the many locations where it needs to be. The integrated delivery of many wavelengths may lead to a very scalable and portable platform. We're showing for the first time that it can be done.

Multiple colors

Computing with trapped ions requires precisely controlling each ion independently. Free-space optics have worked well when controlling a few ions in a short one-dimensional chain. But hitting a single ion among a larger or two-dimensional cluster, without hitting its neighbors, is extremely difficult. When imagining a practical quantum computer requiring thousands of ions, this task of laser control seems impractical.

That looming problem led researchers to find another way. In 2016, Lincoln Laboratory and MIT researchers demonstrated a new chip with built-in optics. They focused a red laser onto the chip, where waveguides on the chip routed the light to a grating coupler, a kind of rumble strip to stop the light and direct it up to the ion.

Red light is crucial for doing a fundamental operation called a quantum gate, which the team performed in that first demonstration. But up to six different-colored lasers are needed to do everything required for quantum computation: prepare the ion, cool it down, read out its energy state, and perform quantum gates. With this latest chip, the team has extended their proof of principle to the rest of these required wavelengths, from violet to the near-infrared.

With these wavelengths, we were able to perform the fundamental set of operations that you need to be able to control trapped ions, says John Chiaverini, also an author on the paper. The one operation they didn't perform, a two-qubit gate, was demonstrated by a team at ETH Zrich by using a chip similar to the 2016 work, and is described in a paper in the same Nature issue. This work, paired together with ours, shows that you have all the things you need to start building larger trapped-ion arrays, Chiaverini adds.

Fiber optics

To make the leap from one to multiple wavelengths, the team engineered a method to bond a fiber-optic block directly to the side of the chip. The block consists of four optical fibers, each one specific to a certain range of wavelengths. These fibers line up with a corresponding waveguide patterned directly onto the chip.

Getting the fiber block array aligned to the waveguides on the chip and applying the epoxy felt like performing surgery. It was a very delicate process. We had about half a micronof tolerance and it needed to survive cooldown to4 kelvins, says Robert Niffenegger, who led the experiments and is first author on the paper.

On top of the waveguides sits a layer of glass. On top of the glass are metal electrodes, which produce electric fields that hold the ion in place; holes are cut out of the metal over the grating couplers where the light is released. The entire device was fabricated in the Microelectronics Laboratory at Lincoln Laboratory.

Designing waveguides that could deliver the light to the ions with low loss, avoiding absorption or scattering, was a challenge, as loss tends to increase with bluer wavelengths. It was a process of developing materials, patterning the waveguides, testing them, measuring performance, and trying again. We also had to make sure the materials of the waveguides worked not only with the necessary wavelengths of light, but also that they didn't interfere with the metal electrodes that trap the ion, Sage says.

Scalable and portable

The team is now looking forward to what they can do with this fully light-integrated chip. For one, make more, Niffenegger says. Tiling these chips into an array could bring together many more ions, each able to be controlled precisely, opening the door to more powerful quantum computers.

Daniel Slichter, a physicist at the National Institute of Standards and Technology who was not involved in this research, says, This readily scalable technology will enable complex systems with many laser beams for parallel operations, all automatically aligned and robust to vibrations and environmental conditions, and will in my view be crucial for realizing trapped ion quantum processors with thousands of qubits.

An advantage of this laser-integrated chip is that it's inherently resistant to vibrations. With external lasers, any vibration to the laser would cause it to miss the ion, as would any vibrations to the chip. Now that the laser beams and chip are coupled together, the effects of vibrations are effectively nullified.

This stability is important for the ions to sustain coherence, or to operate as qubits long enough to compute with them. It's also important if trapped-ion sensors are to become portable. Atomic clocks, for example, that are based on trapped ions could keep time much more precisely than today's standard, and could be used to improve the accuracy of GPS, which relies on the synchronization of atomic clocks carried on satellites.

We view this work as an example of bridging science and engineering, that delivers a true advantage to both academia and industry, Sage says. Bridging this gap is the goal of the MIT Center for Quantum Engineering, where Sage is a principal investigator.We need quantum technology to be robust, deliverable, and user-friendly, for people to use who aren't PhDs in quantum physics, Sage says.

Simultaneously, the team hopes that this device can help push academic research. We want other research institutes to use this platform so that they can focus on other challenges like programming and running algorithms with trapped ions on this platform, for example. We see it opening the door to further exploration of quantum physics, Chiaverini says.

More here:
Lighting up the ion trap - MIT News