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Even with the pandemic raging, natural disasters are having a busy 2020: tornadoes ravaged Nashville a few months ago; the chances of a new big one have dramatically risen in Californias fault zones; and meteorologists are anticipating a stronger-than-usual hurricane season for the U.S. More than ever, understanding and anticipating these events is crucial and now, two teams of researchers have announced that they have used supercomputers to run the higher-resolution-ever simulations of tornadoes and earthquakes.

While researchers have understood the basics of tornado formation for some time, the particulars are difficult to work out so difficult, in fact, that the National Weather Service has a 70 percent false alarm rate for tornado warnings. Leigh Orf, an atmospheric scientist with the University of Wisconsin-Madisons Space Science and Engineering Center is on a quest to change that using the most detailed tornado simulations ever produced.

Using a piece of software he developed, Orf has simulating and visualizing fully resolved tornadoes and their parent supercells for a decade. To run these powerful simulations, Orf has used a variety of supercomputers most recently, Frontera at the Texas Advanced Computing Center (TACC). Frontera delivers 23.5 Linpack petaflops of computing power, placing it 8th on the most recent Top500 list of the worlds most powerful publicly ranked supercomputers. With Frontera, Orf has been able to run simulations at high spatial and temporal resolutions ten meters and a fifth of a second, respectively.

It is only with this level of granularity that some features become evident, Orf said in an interview with TACCs Aaron Dubrow. We need to throw a lot of computational power to get it right and resolve salient features. Ultimately, the goal is prediction, but the truth is, we still dont understand some basic things about how supercell thunderstorms really work. Its really hard to answer questions like, will this supercell that just formed produce a tornado, and if so, will it be especially violent?

Orfs research has, to date, produced a variety of insights into the tornadogenesis process. When studying a deadly tornado event in Oklahoma, for instance, Orf found several characteristic features that might help explain how the tornadoes formed. In these simulations, theres a lot of spinning going on that you wouldnt see with the naked eye, he said. That spinning is sometimes in the form of vortex sheets rolling up, or misocyclones, what you might call mini tornadoes, that arent quite tornado strength that spin along different boundaries in the storm. Similarly, his simulations revealed that certain types of currents serve as driving forces for tornado intensity.

Now, with his allocation on Frontera, Orf is looking to re-simulate storms in a variety of conditions to see how minor variable changes might impact the formation or intensity of tornadoes. Very small changes early on in the simulation can lead to very big changes in the simulation down the road, he said. This is an intrinsic predictability issue in our field. Were doing some of the frontier work to try to tease out these variables.

While Orf is looking to the sky, a team at Lawrence Livermore National Laboratory (LLNL) is looking to the ground. Using code developed at LLNL, the researchers simulated a magnitude 7.0 earthquake on the Hayward Fault, which runs along the San Francisco Bay Area. The new simulations ran at double the resolution of previous iterations, capturing seismic waves as short as 50 meters across the entire fault zone. These simulations, too, required extraordinary computing power: in this case, LLNLs Sierra system, which delivers 94.6 Linpack petaflops, placing it third on the most recent Top500 list. The Sierra-based simulations were run during Sierras open science period in 2018, before it switched to classified work. The team also made use of LLNLs Lassen system (an unclassified machine with similar architecture to Sierra), which delivers 18.2 Linpack petaflops and placed 14th.

The [Institutional Center of Excellence] prepared computer codes at LLNL to run efficiently on Sierra and Lassen prior to their arrival so they could immediately take advantage of those capabilities when they came online, and this earthquake simulation and other science-based projects are achieving exactly what they were meant to do, said Chris Clouse, associate program director for computational physics at LLNL, in an interview with LLNLs Anne Stark.

We used a recently developed empirical model to correct ground motions for the effects of soft soils not included in the Sierra calculations, said Arthur Rodgers, a seismologist at LLNL. These improved the realism of the simulated shaking intensities and bring the results in closer agreement with expected values.

Now, with hurricane season beginning, eyes are turning to the wide range of weather and climate supercomputer centers many of which have recently received large installations or investments to see if the 2020 hurricane season can be more accurately anticipated.

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Supercomputers Just Hosted the Most Detailed Tornado and Earthquake Simulations Ever - HPCwire

LOS ALAMITOS, Calif., Aug. 12, 2020 /PRNewswire/ --The inaugural IEEE International Conference on Quantum Computing and Engineering (QCE20)a multidisciplinary event focusing on quantum technology, research, development, and trainingwill be conducted virtually, on 12-16 October 2020, instead of in-person in Colorado as originally scheduled. The decision was made out of an abundance of caution for the health and safety of participants amid the COVID-19 pandemic.

The exciting QCE20 conference programfeatures over 270 hours of programming. Each day the QCE20 conference, also known as IEEE Quantum Week, will virtually deliver 9-10 parallel tracks ofworld-class keynotes, workforce-building tutorials, community-building workshops, technical paper presentations, innovative posters, and thought-provoking panels through a digital combination of pre-recorded and live-streamed sessions. Attendees will be able to participate in live Q&A sessions with keynote speakers and panelists, paper and poster authors, as well as tutorial and workshop speakers. Birds of a Feather, Networking, and Beautiful Coloradosessions spice up the program between technical sessions. The recorded QCE20 sessions will be available for on-demand until November 30.

"With our expansive technical program and lineup of incredible presentations from thought-leaders all over the globe, this is shaping up to be the quantum event of the year," said Hausi Mller, QCE20 General Chair, IEEE Quantum Initiative Co-Chair. "I encourage all professionals and enthusiasts to become a quantum computing champion by engaging and participating in the inaugural IEEE International Conference on Quantum Computing & Engineering (QCE20)."

Workshops and tutorials will be conducted according to their pre-determined schedule in a live, virtual format. The QCE20 tutorials program features 16 tutorials by leading experts aimed squarely at workforce development and training considerations, and 21 QCE20 workshopsprovide forums for group discussions on topics in quantum research, practice, education, and applications.

Ten outstanding keynote speakers will address quantum computing and engineering topics at the beginning and at the end of each conference day, providing insights to stimulate discussion for the networking sessions and exhibits.

QCE20 panel sessionswill explore various perspectives of quantum topics, including quantum education and training, quantum hardware and software, quantum engineering challenges, fault-tolerant quantum computers, quantum error correction, quantum intermediate language representation, hardware-software co-design, and hybrid quantum-classical computing platforms. Visit Enabling and Growing the Quantum Industryto view the newest addition to the lineup.

Over 20 QCE20 exhibitors and sponsors including Platinum sponsors IBM, Microsoft, and Honeywell, and Gold sponsors Quantropi and Zapatawill be featured Monday through Friday in virtual exhibit rooms offering numerous opportunities for networking.

QCE20 is co-sponsored by the IEEE Computer Society, IEEE Communications Society, IEEE Photonics Society, IEEE Council on Superconductivity,IEEE Electronics Packaging Society, IEEE Future Directions Quantum Initiative, and IEEETechnology and Engineering Management Society.

Register to be a part of the highly anticipated virtual IEEE Quantum Week 2020.

Visit qce.quantum.ieee.org for all program details, as well as sponsorship and exhibitor opportunities.

About the IEEE Computer SocietyThe IEEE Computer Society is the world's home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit http://www.computer.orgfor more information.

About the IEEE Communications Society The IEEE Communications Societypromotes technological innovation and fosters creation and sharing of information among the global technical community. The Society provides services to members for their technical and professional advancement and forums for technical exchanges among professionals in academia, industry, and public institutions.

About the IEEE Photonics SocietyTheIEEE Photonics Societyforms the hub of a vibrant technical community of more than 100,000 professionals dedicated to transforming breakthroughs in quantum physics into the devices, systems, and products to revolutionize our daily lives. From ubiquitous and inexpensive global communications via fiber optics, to lasers for medical and other applications, to flat-screen displays, to photovoltaic devices for solar energy, to LEDs for energy-efficient illumination, there are myriad examples of the Society's impact on the world around us.

About the IEEE Council on SuperconductivityThe IEEE Council on Superconductivityand its activities and programs cover the science and technology of superconductors and their applications, including materials and their applications for electronics, magnetics, and power systems, where the superconductor properties are central to the application.

About the IEEE Electronics Packaging SocietyThe IEEE Electronics Packaging Societyis the leading international forum for scientists and engineers engaged in the research, design, and development of revolutionary advances in microsystems packaging and manufacturing.

About the IEEE Future Directions Quantum InitiativeIEEE Quantumis an IEEE Future Directions initiative launched in 2019 that serves as IEEE's leading community for all projects and activities on quantum technologies. IEEE Quantum is supported by leadership and representation across IEEE Societies and OUs. The initiative addresses the current landscape of quantum technologies, identifies challenges and opportunities, leverages and collaborates with existing initiatives, and engages the quantum community at large.

About the IEEE Technology and Engineering Management SocietyIEEE TEMSencompasses the management sciences and practices required for defining, implementing, and managing engineering and technology.

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IEEE International Conference on Quantum Computing and Engineering (QCE20) Transitions to All-Virtual Event - Monterey County Weekly

by YK Sugi

Hi everyone!

The other day, I visited D-Wave Systems in Vancouver, Canada. Its a company that makes cutting-edge quantum computers.

I got to learn a lot about quantum computers there, so Id like to share some of what I learned there with you in this article.

The goal of this article is to give you an accurate intuition of what a quantum computer is using a simple example.

This article will not require you to have prior knowledge of either quantum physics or computer science to be able to understand it.

Okay, lets get started.

Edit (Feb 26, 2019): I recently published a video about the same topic on my YouTube channel. I would recommend watching it (click here) before or after reading this article because I have added some additional, more nuanced arguments in the video.

Here is a one-sentence summary of what a quantum computer is:

There is a lot to unpack in this sentence, so let me walk you through what it is exactly using a simple example.

To explain what a quantum computer is, Ill need to first explain a little bit about regular (non-quantum) computers.

Now, a regular computer stores information in a series of 0s and 1s.

Different kinds of information, such as numbers, text, and images can be represented this way.

Each unit in this series of 0s and 1s is called a bit. So, a bit can be set to either 0 or 1.

A quantum computer does not use bits to store information. Instead, it uses something called qubits.

Each qubit can not only be set to 1 or 0, but it can also be set to 1 and 0. But what does that mean exactly?

Let me explain this with a simple example. This is going to be a somewhat artificial example. But its still going to be helpful in understanding how quantum computers work.

Now, suppose youre running a travel agency, and you need to move a group of people from one location to another.

To keep this simple, lets say that you need to move only 3 people for now Alice, Becky, and Chris.

And suppose that you have booked 2 taxis for this purpose, and you want to figure out who gets into which taxi.

Also, suppose here that youre given information about whos friends with who, and whos enemies with who.

Here, lets say that:

And suppose that your goal here is to divide this group of 3 people into the two taxis to achieve the following two objectives:

Okay, so this is the basic premise of this problem. Lets first think about how we would solve this problem using a regular computer.

To solve this problem with a regular, non-quantum computer, youll need first to figure out how to store the relevant information with bits.

Lets label the two taxis Taxi #1 and Taxi #0.

Then, you can represent who gets into which car with 3 bits.

For example, we can set the three bits to 0, 0, and 1 to represent:

Since there are two choices for each person, there are 2*2*2 = 8 ways to divide this group of people into two cars.

Heres a list of all possible configurations:

A | B | C0 | 0 | 00 | 0 | 10 | 1 | 00 | 1 | 11 | 0 | 01 | 0 | 11 | 1 | 01 | 1 | 1

Using 3 bits, you can represent any one of these combinations.

Now, using a regular computer, how would we determine which configuration is the best solution?

To do this, lets define how we can compute the score for each configuration. This score will represent the extent to which each solution achieves the two objectives I mentioned earlier:

Lets simply define our score as follows:

(the score of a given configuration) = (# friend pairs sharing the same car) - (# enemy pairs sharing the same car)

For example, suppose that Alice, Becky, and Chris all get into Taxi #1. With three bits, this can be expressed as 111.

In this case, there is only one friend pair sharing the same car Alice and Becky.

However, there are two enemy pairs sharing the same car Alice and Chris, and Becky and Chris.

So, the total score of this configuration is 1-2 = -1.

With all of this setup, we can finally go about solving this problem.

With a regular computer, to find the best configuration, youll need to essentially go through all configurations to see which one achieves the highest score.

So, you can think about constructing a table like this:

A | B | C | Score0 | 0 | 0 | -10 | 0 | 1 | 1 <- one of the best solutions0 | 1 | 0 | -10 | 1 | 1 | -11 | 0 | 0 | -11 | 0 | 1 | -11 | 1 | 0 | 1 <- the other best solution1 | 1 | 1 | -1

As you can see, there are two correct solutions here 001 and 110, both achieving the score of 1.

This problem is fairly simple. It quickly becomes too difficult to solve with a regular computer as we increase the number of people in this problem.

We saw that with 3 people, we need to go through 8 possible configurations.

What if there are 4 people? In that case, well need to go through 2*2*2*2 = 16 configurations.

With n people, well need to go through (2 to the power of n) configurations to find the best solution.

So, if there are 100 people, well need to go through:

This is simply impossible to solve with a regular computer.

How would we go about solving this problem with a quantum computer?

To think about that, lets go back to the case of dividing 3 people into two taxis.

As we saw earlier, there were 8 possible solutions to this problem:

A | B | C0 | 0 | 00 | 0 | 10 | 1 | 00 | 1 | 11 | 0 | 01 | 0 | 11 | 1 | 01 | 1 | 1

With a regular computer, using 3 bits, we were able to represent only one of these solutions at a time for example, 001.

However, with a quantum computer, using 3 qubits, we can represent all 8 of these solutions at the same time.

There are debates as to what it means exactly, but heres the way I think about it.

First, examine the first qubit out of these 3 qubits. When you set it to both 0 and 1, its sort of like creating two parallel worlds. (Yes, its strange, but just follow along here.)

In one of those parallel worlds, the qubit is set to 0. In the other one, its set to 1.

Now, what if you set the second qubit to 0 and 1, too? Then, its sort of like creating 4 parallel worlds.

In the first world, the two qubits are set to 00. In the second one, they are 01. In the third one, they are 10. In the fourth one, they are 11.

Similarly, if you set all three qubits to both 0 and 1, youd be creating 8 parallel worlds 000, 001, 010, 011, 100, 101, 110, and 111.

This is a strange way to think, but it is one of the correct ways to interpret how the qubits behave in the real world.

Now, when you apply some sort of computation on these three qubits, you are actually applying the same computation in all of those 8 parallel worlds at the same time.

So, instead of going through each of those potential solutions sequentially, we can compute the scores of all solutions at the same time.

With this particular example, in theory, your quantum computer would be able to find one of the best solutions in a few milliseconds. Again, thats 001 or 110 as we saw earlier:

A | B | C | Score0 | 0 | 0 | -10 | 0 | 1 | 1 <- one of the best solutions0 | 1 | 0 | -10 | 1 | 1 | -11 | 0 | 0 | -11 | 0 | 1 | -11 | 1 | 0 | 1 <- the other best solution1 | 1 | 1 | -1

In reality, to solve this problem, you would need to give your quantum computer two things:

Given these two things, your quantum computer will spit out one of the best solutions in a few milliseconds. In this case, thats 001 or 110 with a score of 1.

Now, in theory, a quantum computer is able to find one of the best solutions every time it runs.

However, in reality, there are errors when running a quantum computer. So, instead of finding the best solution, it might find the second-best solution, the third best solution, and so on.

These errors become more prominent as the problem becomes more and more complex.

So, in practice, you will probably want to run the same operation on a quantum computer dozens of times or hundreds of times. Then pick the best result out of the many results you get.

Even with the errors I mentioned, the quantum computer does not have the same scaling issue a regular computer suffers from.

When there are 3 people we need to divide into two cars, the number of operations we need to perform on a quantum computer is 1. This is because a quantum computer computes the score of all configurations at the same time.

When there are 4 people, the number of operations is still 1.

When there are 100 people, the number of operations is still 1. With a single operation, a quantum computer computes the scores of all 2 ~= 10 = one million million million million million configurations at the same time.

As I mentioned earlier, in practice, its probably best to run your quantum computer dozens of times or hundreds of times and pick the best result out of the many results you get.

However, its still much better than running the same problem on a regular computer and having to repeat the same type of computation one million million million million million times.

Special thanks to everyone at D-Wave Systems for patiently explaining all of this to me.

D-Wave recently launched a cloud environment for interacting with a quantum computer.

If youre a developer and would like actually to try using a quantum computer, its probably the easiest way to do so.

Its called Leap, and its at https://cloud.dwavesys.com/leap. You can use it for free to solve thousands of problems, and they also have easy-to-follow tutorials on getting started with quantum computers once you sign up.

Footnote:

Read more:
What is a quantum computer? Explained with a simple example.

University of Virginia School of Medicinescientists are harnessing the mind-bending potential of quantum computers to help us understand genetic diseases even before quantum computers are a thing.

UVAs Stefan Bekiranov and colleagues have developed an algorithm to allow researchers to study genetic diseases using quantum computers once there are much more powerful quantum computers to run it. The algorithm, a complex set of operating instructions, will help advance quantum computing algorithm development and could advance the field of genetic research one day.

Quantum computers are still in their infancy. But when they come into their own, possibly within a decade, they may offer computing power on a scale unimaginable using traditional computers.

We developed and implemented a genetic sample classification algorithm that is fundamental to the field of machine learning on a quantum computer in a very natural way using the inherent strengths of quantum computers, Bekiranov said. This is certainly the first published quantum computer study funded by the National Institute of Mental Health and may be the first study using a so-called universal quantum computer funded by the National Institutes of Health.

Traditional computer programs are built on 1s and 0s, either-or. But quantum computers take advantage of a freaky fundamental of quantum physics: Something can be and not be at the same time. Rather than 1 or 0, the answer, from a quantum computers perspective, is both, simultaneously. That allows the computer to consider vastly more possibilities, all at once.

The challenge is that the technology is, to put it lightly, technically demanding. Many quantum computers have to be kept at near absolute zero, the equivalent of more than 450 degrees below zero Fahrenheit. Even then, the movement of molecules surrounding the quantum computing elements can mess up the calculations, so algorithms not only have to contain instructions for what to do, but for how to compensate when errors creep in.

Our goal was to develop a quantum classifier that we could implement on an actual IBM quantum computer. But the major quantum machine learning papers in the field were highly theoretical and required hardware that didnt exist. We finally found papers from Dr. Maria Schuld, who is a pioneer in developing implementable, near-term, quantum machine-learning algorithms. Our classifier builds on those developed by Dr. Schuld, Bekiranov said. Once we started testing the classifier on the IBM system, we quickly discovered its current limitations and could only implement a vastly oversimplified, or toy, problem successfully, for now.

The new algorithm essentially classifies genomic data. It can determine if a test sample comes from a disease or control sample exponentially faster than a conventional computer. For example, if they used all four building blocks of DNA (A, G, C or T) for the classification, a conventional computer would execute 3 billion operations to classify the sample. The new quantum algorithm would need only 32.

That will help scientists sort through the vast amount of data required for genetic research. But its also proof-of-concept of the usefulness of the technology for such research.

Bekiranov and collaborator Kunal Kathuria were able to create the algorithm because they were trained in quantum physics, a field that even scientists often find opaque. Such algorithms are more likely to emerge from physics or computer science departments than medical schools. (Both Bekiranov and Kathuria conducted the study in the School of MedicinesDepartment of Biochemistry and Molecular Genetics. Kathuria is currently at the Lieber Institute for Brain Development.)

Because of the researchers particular set of skills, officials at the National Institutes of Healths National Institute of Mental Health supported them in taking on the challenging project. Bekiranov and Kathuria hope what they have developed will be a great benefit to quantum computing and, eventually, human health.

Relatively small-scale quantum computers that can solve toy problems are in existence now, Bekiranov said. The challenges of developing a powerful universal quantum computer are immense. Along with steady progress, it will take multiple scientific breakthroughs. But time and again, experimental and theoretical physicists, working together, have risen to these challenges. If and when they develop a powerful universal quantum computer, I believe it will revolutionize computation and be regarded as one of greatest scientific and engineering achievements of humankind.

The scientists have published their findings in the scientific journalQuantum Machine Intelligence. The algorithm-development team consisted of Kathuria, Aakrosh Ratan, Michael McConnell and Bekiranov.

The work was supported by NIH grants 3U01MH106882-04S1, 5U01MH106882-05 and P30CA044579.

To keep up with the latest medical research news from UVA, subscribe to theMaking of Medicineblog.

Read this article:
UVA Pioneers Study of Genetic Diseases With Mind-Bending Quantum Computing - University of Virginia

Physicists at the University of Alberta have developed technology that can translate data from microwaves to optical lightan advance that has promising applications in the next generation of super-fast quantum computers and secure fibre-optic telecommunications.

Many quantum computer technologies work in the microwave regime, while many quantum communications channels, such as fibre and satellite, work with optical light, explained Lindsay LeBlanc, who holds the Canada Research Chair in Ultracold Gases for Quantum Simulation. We hope that this platform can be used in the future to transduce quantum signals between these two regimes.

The new technology works by introducing a strong interaction between microwave radiation and atomic gas. The microwaves are then modulated with an audio signal, encoding information into the microwave. This modulation is passed through the gas atoms, which are then probed with optical light to encode the signal into the light.

This transfer of information from the microwave domain to the optical domain is the key result, said LeBlanc. The wavelengths of these two carrier signals differ by a factor of 50,000. It is not easy to transduce the signal between these regimes, but this transfer proves this is possible.

LeBlanc and researchers in her lab, including graduate student Andrei Tretiakov and undergraduate student Timothy Lee, worked closely with physicist John P. Davisand his research group, including graduate student Clinton Potts, to develop the technology.

LeBlanc and Davis are part of Quanta, an NSERC CREATE program designed to train graduate students in emerging quantum technologies.

This idea arose by having talks and meeting within the Quanta groupand it turned out to work as well or better than we first expected, said LeBlanc.

This sort of discovery-led research can be very fruitful, and lead us to new possibilities.

Funding for the project was provided by Alberta Innovates.

The study, Atomic Microwave-to-Optical Signal Transduction via Magnetic-Field Coupling in a Resonant Microwave Cavity, was published in Applied Physics Letters.

Original post:
U of A physicists develop technology to transform information from microwaves to optical light - Folio - University of Alberta