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.

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UVA Pioneers Study of Genetic Diseases With Mind-Bending Quantum Computing - University of Virginia

Dr. Michael M. Resch of the University of Stuttgart has professorships, degrees, doctorates and honorary doctorates from around the world, he has studied and taught in Europe and the U.S., but for all the work he has done in supercomputing for the past three-plus decades, he boils down his years in HPC to working with the same, if always improving, von Neumann architecture. Hes eager for the next new thing: quantum. Going to quantum computing, we have to throw away everything and we have to start anew, he says. This is a great time.

In This Update. From The HPC User Forum Steering Committee

By Steve Conway and Thomas Gerard

After the global pandemic forced Hyperion Research to cancel the April 2020 HPC User Forum planned for Princeton, New Jersey, we decided to reach out to the HPC community in another way by publishing a series of interviews with members of the HPC User Forum Steering Committee. Our hope is that these seasoned leaders perspectives on HPCs past, present and future will be interesting and beneficial to others. To conduct the interviews, Hyperion Research engaged insideHPC Media.

We welcome comments and questions addressed to Steve Conway, sconway@hyperionres.com or Earl Joseph, ejoseph@hyperionres.com.

This interview is with Michael M. Resch. Prof. Dr. Dr. h.c. mult. He is dean of the faculty for energy-process and biotechnology of the University of Stuttgart, director of the High Performance Computing Center Stuttgart (HLRS), the Department for High Performance Computing, and the Information Center (IZUS), all at the University of Stuttgart, Germany. He was an invited plenary speaker at SC07. He chairs the board of the German Gauss Center for Supercomputing (GCS) and serves on the advisory councils for Triangle Venture Capital Group and several foundations. He is on the advisory board of the Paderborn Center for Parallel Computing (PC2). He holds a degree in technical mathematics from the Technical University of Graz, Austria and a Ph.D. in engineering from the University of Stuttgart. He was an assistant professor of computer science at the University of Houston and was awarded honorary doctorates by the National Technical University of Donezk (Ukraine) and the Russian Academy of Science.

He was interviewed by Dan Olds, HPC and big data consultant at Orionx.net.

The HPC User Forum was established in 1999 to promote the health of the global HPC industry and address issues of common concern to users. More than 75 HPC User Forum meetings have been held in the Americas, Europe and the Asia-Pacific region since the organizations founding in 2000.

Olds: Hello, Im Dan Olds on behalf of Hyperion Research and insideHPC, and today Im talking to Michael Resch, who is an honorable professor at the HPC Center in Stuttgart, Germany. How are you, Michael?

Resch: I am fine, Dan. Thanks.

Olds: Very nice to talk to you. I guess lets start at the beginning. How did you get involved in HPC in the first place?

Resch: That started when I was a math student and I was invited to work as a student research assistant and, by accident, that was roughly the month when a new supercomputer was coming into the Technical University of Graz. So, I put my hands on that machine and I never went away again.

Olds: You sort of made that machine yours, I guess?

Resch: We were only three users. There were three user groups and I was the most important user of my user group because I did all the programming.

Olds: Fantastic, thats a way to make yourself indispensable, isnt it?

Resch: In a sense.

Olds: So, can you kind of summarize your HPC background over the years?

Resch: I started doing blood flow simulations, so I at first looked into this very traditional Navier-Stokes equation that was driving HPC for a long time. Then I moved on to groundwater flow simulations pollution of groundwater, tunnel construction work, and everything until after like five years I moved to the University of Stuttgart, where I started to work with supercomputers, more focusing on the programming side, the performance side, than on the hardware side. This is sort of my background in terms of experience.

In terms of education, I studied a mixture of mathematics, computer science and economics, and then did a Ph.D. in engineering, which was convenient if youre working in Navier-Stokes equations. So, I try to bring all of these things together to make an impact in HPC.

Olds: What are some of the biggest changes youve seen in HPC over your career?

Resch: Well, the biggest change is probably that when I started, as I said, there were three user groups. These were outstanding experts in their field, but supercomputing was nothing for the rest of the university. Today, everybody is using HPC. Thats probably the biggest change, that we are moving from something where you had one big system and a few experts around that system, and you moved to a larger number of systems and tens of thousands of experts working with them.

Olds: And, so, the systems have to get bigger, of course.

Resch: Well, certainly, they have to get bigger. And they have to get, I would say, more usable. Thats another feature, that now things are more hidden from the user, which makes it easier to use them. But at the same time, it takes away some of the performance. There is this combination of hiding things away from the user and then the massive parallelism that we saw, and thats the second most important thing that I think we saw in the last three decades. That has made it much more difficult to get high sustained performance.

Olds: Where do you see HPC headed in the future? Is there anything that has you particularly excited or concerned?

Resch: [Laughs] Im always excited and concerned. Thats just normal. Thats what happens when you go into science and thats normal when you work with supercomputers. I see, basically, two things happening. The first thing is that people will merge everything that has to do with data and everything that has to do with simulation. I keep saying its data analytics, machine learning, artificial intelligence. Its sort of a development from raw data to very intelligent handling of data. And these data-intensive things start to merge with simulation, like we see people trying to understand what they did over the last 20 years by employing artificial intelligence to work its way through the data trying to find what we have already done and what should we do next, things like that.

The second thing that is exciting is quantum computing. Its exciting because its out of the ordinary, in a sense. You might say that over the last 32 years the only thing I did was work with improved technology and improved methods and improved algorithms or whatever, but I was still working in the same John von Neumann architecture concept. Going to quantum computing we have to throw away everything and we have to start anew. This is a great time. I keep telling my Ph.D. candidates, go for quantum computing. This is where you make an impact. This is where you have a wide-open field of things you can explore and this is what is going to make the job exciting for the next 10, 12, 15 years or so.

Olds: Thats fantastic and your enthusiasm for this really comes through. Your enthusiasm for HPC, for the new computing methods, and all that. And, thank you so much for taking the time.

Resch: It was a pleasure. Thank you.

Olds: Thank you, really appreciate it.

See more here:
The Hyperion-insideHPC Interviews: Dr. Michael Resch Talks about the Leap from von Neumann: 'I Tell My PhD Candidates: Go for Quantum' - insideHPC

WASHINGTON--(BUSINESS WIRE)--Microsoft Executive Vice President Jason Zander says the company has never been more busy partnering with the energy industry on cloud technologies and energy transition; the combination of COVID-19 and the oil market shock has condensed years of digital transformation into a two-month period; the companys return to its innovative roots and its goal to have removed all of the companys historic carbon emissions by 2050 in the latest edition of CERAWeek Conversations.

In a conversation with IHS Markit (NYSE: INFO) Vice Chairman Daniel Yergin, Zanderwho leads the companys cloud services business, Microsoft Azurediscusses Microsofts rapid and massive deployment of cloud-based apps that have powered work and commerce in the COVID-19 economy; how cloud technologies are optimizing business and vaccine research; the next frontiers of quantum computing and its potential to take problems that would take, literally, a thousand years, you might be able to solve in 10 seconds, and more.

The complete video is available at: http://www.ceraweek.com/conversations

Selected excerpts:Interview Recorded Thursday, July 16, 2020

(Edited slightly for brevity only)

Watch the complete video at: http://www.ceraweek.com/conversations

Weve already prepositioned in over 60 regions around the world hundreds of data center, millions and millions of server nodestheyre already there. If you can imagine COVID, if you had to go back and do a procurement exercise and figure out a place to put the equipment, and the supply chains were actually shut down for a while because of COVID. Thats why I say, even three to five years ago we as industries would have been pretty challenged to respond as quickly as we had.

Thats on the more tactical end of the spectrum. On the other end weve also done a lot of things around data sets and advanced data work. How do we find a cure? Weve done things like [protein] folding at home and making sure that those things could be hosted on the cloud. These are thingsthat will be used in the search of a vaccine for the virus. Those are wildly different spectrums from the tactical 'we need to manage and do logistics' to 'we need a search for things that are going to get us all back to basically normal.'

Theres also a whole bunch of stimulus packages and payment systems that are getting created and deployed. Weve had financial services companies that run on top of the cloud. They may have been doing a couple of hundred big transactions a day; weve had them do tens to hundreds of thousands a day when some of this kicked in.

The point is with the cloud I can just go to the cloud, provision it, use it, and eventually when things cool back down, I can just shut it off. I dont have to worry about having bought servers, find a place for them to live, hiring people to take care of them.

There was disruption in supply chain also. Many of us saw this at least in the Statesif you think even the food supply chain, every once in a while, youd see some hiccups. Theres a whole bunch of additional work that weve done around how do we do even better planning around that, making sure we can hit the right levels of scale in the future? God forbid we should have another one of these, but I think we can and should be responsible to make sure that weve got it figured out.

The policy and investment sideit has never been more important for us to collaborate with healthcare, universities, and with others. Weve kicked off a whole bunch of new partnerships and work that will benefit us in the future. This was a good wake up call for all of us in figuring out how to marshal and be able to respond even better in the future.

Weve had a lot of cases where people have been moving out of their own data centers and into ours. Let us basically take care of that part of the system. We can run it cheaply and efficiently. Im seeing a huge amount of data center accelerationfolks that really want to move even faster on getting their workloads removed. Thats true for oil and gas but its also true for the financial sector and retail.

Specifically, for oil and gas, one of the things that were trying to do in particular is bring this kind of cloud efficiency, this kind of AI, and especially help out with places where you are doing exploration. What these have in common is the ability to take software especially from the [independent software vendors] that work in the spacereservoir simulation, explorationand marry that to these cloud resources where I can spin things up and spin things down. I can take advantage of that technology that Ive got, and I am more efficient. I am not spending capex; I can perhaps do even more jobs than I was doing before. That allows me to go do that scale. If youre going to have less resources to do something, you of course want to increase your hit rate; increase your efficiency. Those are some of the core things that were seeing.

A lot of folks, especially in oil and gas, have some of the most sophisticated high-performance computing solutions that are out there today. What we want to be able to do with the cloud is to be able to enable you to do even more of those solutions in a much more efficient way. Weve got cases where people have been able to go from running one reservoir simulation job a day on premises [to] where they can actually go off to the cloud and since we have all of this scale and all of this equipment, you can spin up and do 100 in one day. If that is going to be part of how you drive your efficiency, then being able to subscribe to that and go up and down its helping you do that job much more efficiently than you used to and giving you a lot more flexibility.

Were investing in a $1 billion fund over the next four years for carbon removal technology. We also are announcing a Microsoft sustainability calculator for cloud customers. Basically, you can help get transparency into your Scope 1,2, and 3 carbon emissions to get control. You can think of us as we want to hit this goal, we want to do it ourselves, we want to figure out how we build technology to help us do that and then we want to share that technology with others. And then all along the way we want to partner with energy companies so that we can all be partnering together on this energy transition.

From a corporate perspective weve made pledges around being carbon negative, but then also working with our energy partners. The way that we look at this is youre going to have continued your requirements and improvements in standards of living around the entire planet. One of the core, critical aspects to that is energy. The world needs more energy, not less. There are absolutely the existing systems that we have out there that we need to continue to improve, but they are also a core part of how things operate.

What we want to do is have a very responsible program where were doing things like figuring out how to go carbon negative and figuring out ways that we as a company can go carbon negative. At the same time, taking those same techniques and allowing others to do the same and then partnering with energy companies around energy transformation. We still want the investments in renewables. We want to figure out how to be more efficient at the last mile when we think about the grid. I generally find that when you get that comprehensive answer back to our employees, they understand what we are doing and are generally supportive.

Coming up is a digital feedback loop where you get enough data thats coming through the system that you can actually start to be making smart decisions. Our expectation is well have an entire connected environment. Now we start thinking about smart cities, smart factories, hospitals, campuses, etc. Imagine having all of that level of data thats coming through and the ability to do smart work shedding or shaping of electrical usage, things where I can actually control brownout conditions and other things based on energy usage. Theres also the opportunity to be doing smart sharing of systems where we can do very efficient usage systemsintelligent edge and edge deployments are a core part of that.

How do we keep all the actual equipment that people are using safe? If you think about 5G and additional connectivity, were getting all this cool new technology thats there. You have to figure out a way in which youre leveraging silicon, youre leveraging software and the best in securityand were investing in all three.

The idea of being able to harness particle physics to do computing and be able to figure out things in minutes that would literally take centuries to go pull off otherwise in classical computing is kind of mind-blowing. Were actually working with a lot of the energy companies on figuring out how could quantum inspired algorithms make them more efficient today. As we get to full scale quantum computing then they would run natively in hardware and would be able to do even more amazing things. That one has just the potential to really, really change the world.

The meta point is problems that would take, literally, a thousand years, you might be able to solve in 10 seconds. Weve proven how that kind of technology can work. The quantum-inspired algorithms therefore allow us to take those same kind of techniques, but we can run them on the cloud today using some of the classic cloud computers that are there. Instead of taking 1,000 years, maybe its something that we can get done in 10 days, but in the future 10 seconds.

About CERAWeek Conversations:

CERAWeek Conversations features original interviews and discussion with energy industry leaders, government officials and policymakers, leaders from the technology, financial and industrial communitiesand energy technology innovators.

The series is produced by the team responsible for the worlds preeminent energy conference, CERAWeek by IHS Markit.

New installments will be added weekly at http://www.ceraweek.com/conversations.

Recent segments also include:

A complete video library is available at http://www.ceraweek.com/conversations.

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IHS Markit (NYSE: INFO) is a world leader in critical information, analytics and solutions for the major industries and markets that drive economies worldwide. The company delivers next-generation information, analytics and solutions to customers in business, finance and government, improving their operational efficiency and providing deep insights that lead to well-informed, confident decisions. IHS Markit has more than 50,000 business and government customers, including 80 percent of the Fortune Global 500 and the worlds leading financial institutions. Headquartered in London, IHS Markit is committed to sustainable, profitable growth.

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Microsoft Executive Vice President Jason Zander: Digital Transformation Accelerating Across the Energy Spectrum; Being Carbon Negative by 2030; The...