Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

By John Russell

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-world applications are depends on whom you talk to and for what kinds of applications. Los Alamos National Lab, for example, has an active application development effort for its D-Wave system and LANL researcher Susan Mniszewski and colleagues have made progress on using the D-Wave machine for aspects of quantum molecular dynamics (QMD) simulations.

At CeBIT this week D-Wave and Volkswagen will discuss their pilot project to monitor and control taxi traffic in Beijing using a hybrid HPC-quantum system – this is on the heels of recent customer upgrade news from D-Wave (more below). Last week IBM announced expanded access to its five-qubit cloud-based quantum developer platform. In early March, researchers from the Google Quantum AI Lab published an excellent commentary in Nature examining real-world opportunities, challenges and timeframes for quantum computing more broadly. Google is also considering making its homegrown quantum capability available through the cloud.

As an overview, the Google commentary provides a great snapshot, noting soberly that challenges such as the lack of solid error correction and the small size (number of qubits) in today’s machines – whether “universal” digital machines like IBM’s or “analog” adiabatic annealing machines like D-Wave’s – have prompted many observers to declare useful quantum computing is still a decade way. Not so fast, says Google.

“This conservative view of quantum computing gives the impression that investors will benefit only in the long term. We contend that short-term returns are possible with the small devices that will emerge within the next five years, even though these will lack full error correction…Heuristic ‘hybrid’ methods that blend quantum and classical approaches could be the foundation for powerful future applications. The recent success of neural networks in machine learning is a good example,” write Masoud Mohseni, Peter Read, and John Martinis (a 2017 HPCwire Person to Watch) and colleagues (Nature, March 8, “Commercialize early quantum technologies”)

The D-Wave/VW project is a good example of a hybrid approach (details to follow) but first here’s a brief summary of recent quantum computing news:

  • IBM released a new API and upgraded simulator for modeling circuits up to 20 qubits on its 5-qubit platform. It also announced plans for a software developer kit by mid-year for building “simple” quantum applications. So far, says IBM, its quantum cloud has attracted about 40,000 users, including, for example, the Massachusetts Institute of Technology, which used the cloud service for its online quantum information science course. IBM also noted heavy use of the service by Chinese researchers. (See HPCwire coverage, IBM Touts Hybrid Approach to Quantum Computing)
  • D-Wave has been actively extending its development ecosystem (qbsolv (D-wave) and qmasm (LANL, et al.) and says researchers have recently been able to simulate a 20,000 qubit system on 1,000-qubit machine using qbsolv (more below). After announcing a 2,000-quibit machine in the fall, the company has begun deploying them. The first will be for a new customer, Temporal Defense System, and another is planned for the Google/NASA/USRA partnership which has a 1,000-qubit machine now. D-wave also just announced Virginia Tech and the Hume Center will begin using D-Wave systems for work on defense and intelligence applications.
  • Google’s commentary declares: “We anticipate that, within a few years, well-controlled quantum systems may be able to perform certain tasks much faster than conventional computers based on CMOS (complementary metal oxide–semiconductor) technology. Here we highlight three commercially viable uses for early quantum-computing devices: quantum simulation, quantum-assisted optimization and quantum sampling. Faster computing speeds in these areas would be commercially advantageous in sectors from artificial intelligence to finance and health care.”
D-Wave 2000Q System

Clearly there is a lot going on even at this stage of quantum computing’s development. There’s also been a good deal of wrangling over just what is a quantum computer and the differences between IBM’s “universal” digital approach – essentially a machine able to do anything computers do now – and D-Wave’s adiabatic annealing approach, which is currently intended to solve specific classes of optimization problems.

“They are different kinds of machines. No one has a universal quantum computer now, so you have to look at each case individually for its particular strengths and weaknesses,” explained Martinis to HPCwire. “The D-wave has minimal quantum coherence (it loses the information exchanged between qubits quite quickly), but makes up for it by having many qubits.”

“The IBM machine is small, but the qubits have quantum coherence enough to do some standard quantum algorithms. Right now it is not powerful, as you can run quantum simulations on classical computers quite easily. But by adding qubits the power will scale up quickly. It has the architecture of a universal machine and has enough quantum coherence to behave like one for very small problems,” Martinis said.

Noteworthy, Google has developed 9-qubit devices that have 3-5x more coherence than IBM, according to Martinis, but they are not on the cloud yet. “We are ready to scale up now, and plan to have this year a ‘quantum supremacy’ device that has to be checked with a supercomputer. We are thinking of offering cloud also, but are more or less waiting until we have a hardware device that gives you more power than a classical simulation.”

Quantum supremacy as described in the Google commentary is a term coined by theoretical physicist John Preskill to describe “the ability of a quantum processor to perform, in a short time, a well-defined mathematical task that even the largest classical supercomputers (such as China’s Sunway TaihuLight) would be unable to complete within any reasonable time frame. We predict that, in a few years, an experiment achieving quantum supremacy will be performed.”

Bo Ewald

For the moment, D-Wave is the only vendor offering near-production machines versus research machines, said Bo Ewald, the company’s ever-cheerful evangelist. He quickly agrees though that at least for now there aren’t any production-ready applications. Developing a quantum tool/software ecosystem is a driving focus at D-wave. The LANL app dev work, though impressive, still represents proto-application development. Nevertheless the ecosystem of tools is growing quickly.

“We have defined a software architecture that has several layers starting at the quantum machine instruction layer where if you want to program in machine language you are certainly welcome to do that; that is kind of the way people had to do it in the early days,” said Ewald.

“The next layer up is if you want to be able to create quantum machine instructions from C or C++ or Python. We have now libraries that run on host machines, regular HPC machines, so you can use those languages to generate programs that run on the D-Wave machine but the challenge that we have faced, that customers have faced, is that our machines had 500 qubits or 1,000 qubits and now 2,000; we know there are problems that are going to consume many more qubits than that,” he said.

For D-Wave systems, qbsolv helps address this problem. It allows a meta-description of the machine and the problem you want to solve as quadratic unconstrained binary optimization or QUBO. It’s an intermediate representation. D-Wave then extended this capability to what it calls virtual QUBOs likening it to virtual memory.

“You can create QUBOs or representations of problems which are much larger than the machine itself and then using combined classical computer and quantum computer techniques we could partition the problem and solve them in chunks and then kind of glue them back together after we solved the D-Wave part. We’ve done that now with the 1,000-qubit machine and run problems that have the equivalent of 20,000 qubits,” said Ewald, adding the new 2,000-qubit machines will handle problems of even greater size using this capability.

At LANL, researcher Scott Pakin has developed another tool – a quantum macro assembler for D-Wave systems (QMASM). Ewald said part of the goal of Pakin’s work was to determine, “if you could map gates onto the machine even though we are not a universal or a gate model. You can in fact model gates on our machine and he has started to [create] a library of gates (or gates, and gates, nand gates) and you can assemble those to become macros.”

Pakin said,My personal research interest has been in making the D-Wave easier to program. I’ve recently built something really nifty on top of QMASM: edif2qmasm, which is my answer to the question: Can one write classical-style code and run it on the D-Wave?

“For many difficult computational problems, solution verification is simple and fast. The idea behind edif2qmasm is that one can write an ordinary(-ish) program that reports if a proposed solution to a problem is in fact valid. This gets compiled for the D-Wave then run _backwards_, giving it ‘true’ for the proposed solution being valid and getting back a solution to the difficult computational problem.”

Pakin noted there are many examples on github to provide a feel for the power of this tool.

“For example, mult.v is a simple, one-line multiplier. Run it backwards, and it factors a number, which underlies modern data decryption. In a dozen or so lines of code, circsat.v evaluates a Boolean circuit. Run it backwards, and it tells you what inputs lead to an output of “true”, which used in areas of artificial intelligence, circuit design, and automatic theorem proving. map-color.v reports if a map is correctly colored with four colors such that no two adjacent regions have the same color. Run it backwards, and it _finds_ such a coloring.

“Although current-generation D-Wave systems are too limited to apply this approach to substantial problems, the trends in system scale and engineering precision indicate that some day we should be able to perform real work on this sort of system. And with the help of tools like edif2qmasm, programmers won’t need an advanced degree to figure out how to write code for it,” he explained.

The D-Wave/VW collaboration, just a year or so old, is one of the more interesting quantum computing proof-of-concept efforts because it tackles an optimization problem of the kind that is widespread in everyday life. As described by Ewald, VW CIO Martin Hoffman was making his yearly swing through Silicon Valley and stopped in at D-Wave and talk turned to the many optimization challenges big automakers face, such as supply logistics, vehicle delivery, and various machine learning tasks and doing a D-Wave project around one of them. Instead, said Ewald, VW eventually settled on a more driver-facing problem.

It turns out there are about 10,000 taxis in Beijing, said Ewald. Each has a GPS device and their positions are recorded every five seconds. Traffic congestion, of course, is a huge problem in Beijing. The idea was to explore if it was possible to create an application running on both traditional computer resources and D-Wave to help monitor and guide taxi movement more quickly and effectively.

“Ten thousand taxis on all of the streets in Beijing is way too big for our machine at this point, but they came to this same idea we talked about with qbsolve where you partition problems,” said Ewald. “On the traditional machines VW created a map and grid and subdivided the grid into quadrants and would find the quadrant that was the most red.” That’s red as in long cab waits.

The problem quadrant was then sent to D-Wave to be solved. “We would optimize the flow, basically minimize the wait time for all of the taxis within the quadrant, send that [solution] back to the traditional machine which would then send us the next most red, and we would try to turn it green,” said Ewald.

According to Ewald, VW was able to relatively create the “hybrid” solutions quickly and “get what they say are pretty good results.” They have talked about then being able to extend this project to predict where traffic jams are going to be and give people perhaps 45 minute warnings that there’s the potential for a traffic jam at such and such intersection. The two companies have a press conference planned this week at CeBIT to showcase the project.

It’s good to emphasize that the VW/D-wave exercise is developmental – what Ewald labels as a proto application: “But just the fact that they were able to get it running is a great step forward in many ways in that we believe our machine will be used side by side with existing machines, much like GPUs were used in the early days on graphics. In this case VW has demonstrated quite clearly how our machine, our QPU if you will, can be used in helping accelerate the work being done on a traditional HPC machines.”

Image art, chip diagram: D-Wave

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Intel Speeds NAMD by 1.8x: Saves Xeon Processor Users Millions of Compute Hours

August 12, 2020

Potentially saving datacenters millions of CPU node hours, Intel and the University of Illinois at Urbana–Champaign (UIUC) have collaborated to develop AVX-512 optimizations for the NAMD scalable molecular dynamics cod Read more…

By Rob Farber

Intel’s Optane/DAOS Solution Tops Latest IO500

August 11, 2020

Intel’s persistent memory technology, Optane, and its DAOS (Distributed Asynchronous Object Storage) stack continue to impress and gain market traction. Yesterday, Intel reported an Optane and DAOS-based system finishe Read more…

By John Russell

Summit Now Offers Virtual Tours

August 10, 2020

Summit, the second most powerful publicly ranked supercomputer in the world, now has a virtual tour. The tour, implemented by 3D platform Matterport, allows users to virtually “walk” around the massive supercomputer Read more…

By Oliver Peckham

Supercomputer Simulations Examine Changes in Chesapeake Bay

August 8, 2020

The Chesapeake Bay, the largest estuary in the continental United States, weaves its way south from Maryland, collecting waters from West Virginia, Delaware, DC, Pennsylvania and New York along the way. Like many major e Read more…

By Oliver Peckham

Student Success from ‘Scratch’: CHPC’s Proof is in the Pudding

August 7, 2020

Happy Sithole, who directs the South African Centre for High Performance Computing (SA-CHPC), called the 13th annual CHPC National conference to order on December 1, 2019, at the Birchwood Conference Centre in Kempton Pa Read more…

By Elizabeth Leake

AWS Solution Channel

University of Adelaide Provides Seamless Bioinformatics Training Using AWS

The University of Adelaide, established in South Australia in 1874, maintains a rich history of scientific innovation. For more than 140 years, the institution and its researchers have had an impact all over the world—making vital contributions to the invention of X-ray crystallography, insulin, penicillin, and the Olympic torch. Read more…

Intel® HPC + AI Pavilion

Supercomputing the Pandemic: Scientific Community Tackles COVID-19 from Multiple Perspectives

Since their inception, supercomputers have taken on the biggest, most complex, and most data-intensive computing challenges—from confirming Einstein’s theories about gravitational waves to predicting the impacts of climate change. Read more…

New GE Simulations on Summit to Advance Offshore Wind Power

August 6, 2020

The wind energy sector is a frequent user of high-power simulations, with researchers aiming to optimize wind flows and energy production from the massive turbines. Now, researchers at GE are preparing to undertake a lar Read more…

By Oliver Peckham

Intel Speeds NAMD by 1.8x: Saves Xeon Processor Users Millions of Compute Hours

August 12, 2020

Potentially saving datacenters millions of CPU node hours, Intel and the University of Illinois at Urbana–Champaign (UIUC) have collaborated to develop AVX-51 Read more…

By Rob Farber

Intel’s Optane/DAOS Solution Tops Latest IO500

August 11, 2020

Intel’s persistent memory technology, Optane, and its DAOS (Distributed Asynchronous Object Storage) stack continue to impress and gain market traction. Yeste Read more…

By John Russell

Summit Now Offers Virtual Tours

August 10, 2020

Summit, the second most powerful publicly ranked supercomputer in the world, now has a virtual tour. The tour, implemented by 3D platform Matterport, allows use Read more…

By Oliver Peckham

Research: A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic

August 5, 2020

Within the past years, hardware vendors have started designing low precision special function units in response to the demand of the machine learning community Read more…

By Hartwig Anzt and Jack Dongarra

Implement Photonic Tensor Cores for Machine Learning?

August 5, 2020

Researchers from George Washington University have reported an approach for building photonic tensor cores that leverages phase change photonic memory to implem Read more…

By John Russell

HPE Keeps Cray Brand Promise, Reveals HPE Cray Supercomputing Line

August 4, 2020

The HPC community, ever-affectionate toward Cray and its eponymous founder, can breathe a (virtual) sigh of relief. The Cray brand will live on, encompassing th Read more…

By Tiffany Trader

Machines, Connections, Data, and Especially People: OAC Acting Director Amy Friedlander Charts Office’s Blueprint for Innovation

August 3, 2020

The path to innovation in cyberinfrastructure (CI) will require continued focus on building HPC systems and secure connections between them, in addition to the Read more…

By Ken Chiacchia, Pittsburgh Supercomputing Center/XSEDE

Nvidia Said to Be Close on Arm Deal

August 3, 2020

GPU leader Nvidia Corp. is in talks to buy U.K. chip designer Arm from parent company Softbank, according to several reports over the weekend. If consummated Read more…

By George Leopold

Supercomputer Modeling Tests How COVID-19 Spreads in Grocery Stores

April 8, 2020

In the COVID-19 era, many people are treating simple activities like getting gas or groceries with caution as they try to heed social distancing mandates and protect their own health. Still, significant uncertainty surrounds the relative risk of different activities, and conflicting information is prevalent. A team of Finnish researchers set out to address some of these uncertainties by... Read more…

By Oliver Peckham

Supercomputer-Powered Research Uncovers Signs of ‘Bradykinin Storm’ That May Explain COVID-19 Symptoms

July 28, 2020

Doctors and medical researchers have struggled to pinpoint – let alone explain – the deluge of symptoms induced by COVID-19 infections in patients, and what Read more…

By Oliver Peckham

Nvidia Said to Be Close on Arm Deal

August 3, 2020

GPU leader Nvidia Corp. is in talks to buy U.K. chip designer Arm from parent company Softbank, according to several reports over the weekend. If consummated Read more…

By George Leopold

Intel’s 7nm Slip Raises Questions About Ponte Vecchio GPU, Aurora Supercomputer

July 30, 2020

During its second-quarter earnings call, Intel announced a one-year delay of its 7nm process technology, which it says it will create an approximate six-month shift for its CPU product timing relative to prior expectations. The primary issue is a defect mode in the 7nm process that resulted in yield degradation... Read more…

By Tiffany Trader

Supercomputer Simulations Reveal the Fate of the Neanderthals

May 25, 2020

For hundreds of thousands of years, neanderthals roamed the planet, eventually (almost 50,000 years ago) giving way to homo sapiens, which quickly became the do Read more…

By Oliver Peckham

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

June 9, 2020

Pittsburgh Supercomputing Center (PSC - a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award Read more…

By Tiffany Trader

HPE Keeps Cray Brand Promise, Reveals HPE Cray Supercomputing Line

August 4, 2020

The HPC community, ever-affectionate toward Cray and its eponymous founder, can breathe a (virtual) sigh of relief. The Cray brand will live on, encompassing th Read more…

By Tiffany Trader

Leading Solution Providers

Contributors

Nvidia’s Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

May 14, 2020

Nvidia's first Ampere-based graphics card, the A100 GPU, packs a whopping 54 billion transistors on 826mm2 of silicon, making it the world's largest seven-nanom Read more…

By Tiffany Trader

Australian Researchers Break All-Time Internet Speed Record

May 26, 2020

If you’ve been stuck at home for the last few months, you’ve probably become more attuned to the quality (or lack thereof) of your internet connection. Even Read more…

By Oliver Peckham

15 Slides on Programming Aurora and Exascale Systems

May 7, 2020

Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…

By John Russell

‘Billion Molecules Against COVID-19’ Challenge to Launch with Massive Supercomputing Support

April 22, 2020

Around the world, supercomputing centers have spun up and opened their doors for COVID-19 research in what may be the most unified supercomputing effort in hist Read more…

By Oliver Peckham

Joliot-Curie Supercomputer Used to Build First Full, High-Fidelity Aircraft Engine Simulation

July 14, 2020

When industrial designers plan the design of a new element of a vehicle’s propulsion or exterior, they typically use fluid dynamics to optimize airflow and in Read more…

By Oliver Peckham

John Martinis Reportedly Leaves Google Quantum Effort

April 21, 2020

John Martinis, who led Google’s quantum computing effort since establishing its quantum hardware group in 2014, has left Google after being moved into an advi Read more…

By John Russell

$100B Plan Submitted for Massive Remake and Expansion of NSF

May 27, 2020

Legislation to reshape, expand - and rename - the National Science Foundation has been submitted in both the U.S. House and Senate. The proposal, which seems to Read more…

By John Russell

Google Cloud Debuts 16-GPU Ampere A100 Instances

July 7, 2020

On the heels of the Nvidia’s Ampere A100 GPU launch in May, Google Cloud is announcing alpha availability of the A100 “Accelerator Optimized” VM A2 instance family on Google Compute Engine. The instances are powered by the HGX A100 16-GPU platform, which combines two HGX A100 8-GPU baseboards using... Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This