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!

SC19’s HPC Impact Showcase Chair: AI + HPC a ‘Speed Train’

November 16, 2019

This year’s chair of the HPC Impact Showcase at the SC19 conference in Denver is Lori Diachin, who has spent her career at the spearhead of HPC. Currently deputy director for the U.S. Department of Energy’s (DOE) Read more…

By Doug Black

Microsoft Azure Adds Graphcore’s IPU

November 15, 2019

Graphcore, the U.K. AI chip developer, is expanding collaboration with Microsoft to offer its intelligent processing units on the Azure cloud, making Microsoft the first large public cloud vendor to offer the IPU designe Read more…

By George Leopold

At SC19: What Is UrgentHPC and Why Is It Needed?

November 14, 2019

The UrgentHPC workshop, taking place Sunday (Nov. 17) at SC19, is focused on using HPC and real-time data for urgent decision making in response to disasters such as wildfires, flooding, health emergencies, and accidents. We chat with organizer Nick Brown, research fellow at EPCC, University of Edinburgh, to learn more. Read more…

By Tiffany Trader

China’s Tencent Server Design Will Use AMD Rome

November 13, 2019

Tencent, the Chinese cloud giant, said it would use AMD’s newest Epyc processor in its internally-designed server. The design win adds further momentum to AMD’s bid to erode rival Intel Corp.’s dominance of the glo Read more…

By George Leopold

NCSA Industry Conference Recap – Part 1

November 13, 2019

Industry Program Director Brendan McGinty welcomed guests to the annual National Center for Supercomputing Applications (NCSA) Industry Conference, October 8-10, on the University of Illinois campus in Urbana (UIUC). One hundred seventy from 40 organizations attended the invitation-only, two-day event. Read more…

By Elizabeth Leake, STEM-Trek

AWS Solution Channel

Making High Performance Computing Affordable and Accessible for Small and Medium Businesses with HPC on AWS

High performance computing (HPC) brings a powerful set of tools to a broad range of industries, helping to drive innovation and boost revenue in finance, genomics, oil and gas extraction, and other fields. Read more…

IBM Accelerated Insights

Data Management – The Key to a Successful AI Project

 

Five characteristics of an awesome AI data infrastructure

[Attend the IBM LSF & HPC User Group Meeting at SC19 in Denver on November 19!]

AI is powered by data

While neural networks seem to get all the glory, data is the unsung hero of AI projects – data lies at the heart of everything from model training to tuning to selection to validation. Read more…

Cray, Fujitsu Both Bringing Fujitsu A64FX-based Supercomputers to Market in 2020

November 12, 2019

The number of top-tier HPC systems makers has shrunk due to a steady march of M&A activity, but there is increased diversity and choice of processing components with Intel Xeon, AMD Epyc, IBM Power, and Arm server ch Read more…

By Tiffany Trader

SC19’s HPC Impact Showcase Chair: AI + HPC a ‘Speed Train’

November 16, 2019

This year’s chair of the HPC Impact Showcase at the SC19 conference in Denver is Lori Diachin, who has spent her career at the spearhead of HPC. Currently Read more…

By Doug Black

Cray, Fujitsu Both Bringing Fujitsu A64FX-based Supercomputers to Market in 2020

November 12, 2019

The number of top-tier HPC systems makers has shrunk due to a steady march of M&A activity, but there is increased diversity and choice of processing compon Read more…

By Tiffany Trader

Intel AI Summit: New ‘Keem Bay’ Edge VPU, AI Product Roadmap

November 12, 2019

At its AI Summit today in San Francisco, Intel touted a raft of AI training and inference hardware for deployments ranging from cloud to edge and designed to support organizations at various points of their AI journeys. The company revealed its Movidius Myriad Vision Processing Unit (VPU)... Read more…

By Doug Black

IBM Adds Support for Ion Trap Quantum Technology to Qiskit

November 11, 2019

After years of percolating in the shadow of quantum computing research based on superconducting semiconductors – think IBM, Rigetti, Google, and D-Wave (quant Read more…

By John Russell

Tackling HPC’s Memory and I/O Bottlenecks with On-Node, Non-Volatile RAM

November 8, 2019

On-node, non-volatile memory (NVRAM) is a game-changing technology that can remove many I/O and memory bottlenecks and provide a key enabler for exascale. That’s the conclusion drawn by the scientists and researchers of Europe’s NEXTGenIO project, an initiative funded by the European Commission’s Horizon 2020 program to explore this new... Read more…

By Jan Rowell

MLPerf Releases First Inference Benchmark Results; Nvidia Touts its Showing

November 6, 2019

MLPerf.org, the young AI-benchmarking consortium, today issued the first round of results for its inference test suite. Among organizations with submissions wer Read more…

By John Russell

Azure Cloud First with AMD Epyc Rome Processors

November 6, 2019

At Ignite 2019 this week, Microsoft's Azure cloud team and AMD announced an expansion of their partnership that began in 2017 when Azure debuted Epyc-backed instances for storage workloads. The fourth-generation Azure D-series and E-series virtual machines previewed at the Rome launch in August are now generally available. Read more…

By Tiffany Trader

Nvidia Launches Credit Card-Sized 21 TOPS Jetson System for Edge Devices

November 6, 2019

Nvidia has launched a new addition to its Jetson product line: a credit card-sized (70x45mm) form factor delivering up to 21 trillion operations/second (TOPS) o Read more…

By Doug Black

Supercomputer-Powered AI Tackles a Key Fusion Energy Challenge

August 7, 2019

Fusion energy is the Holy Grail of the energy world: low-radioactivity, low-waste, zero-carbon, high-output nuclear power that can run on hydrogen or lithium. T Read more…

By Oliver Peckham

Using AI to Solve One of the Most Prevailing Problems in CFD

October 17, 2019

How can artificial intelligence (AI) and high-performance computing (HPC) solve mesh generation, one of the most commonly referenced problems in computational engineering? A new study has set out to answer this question and create an industry-first AI-mesh application... Read more…

By James Sharpe

Cray Wins NNSA-Livermore ‘El Capitan’ Exascale Contract

August 13, 2019

Cray has won the bid to build the first exascale supercomputer for the National Nuclear Security Administration (NNSA) and Lawrence Livermore National Laborator Read more…

By Tiffany Trader

DARPA Looks to Propel Parallelism

September 4, 2019

As Moore’s law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat. Read more…

By George Leopold

AMD Launches Epyc Rome, First 7nm CPU

August 8, 2019

From a gala event at the Palace of Fine Arts in San Francisco yesterday (Aug. 7), AMD launched its second-generation Epyc Rome x86 chips, based on its 7nm proce Read more…

By Tiffany Trader

D-Wave’s Path to 5000 Qubits; Google’s Quantum Supremacy Claim

September 24, 2019

On the heels of IBM’s quantum news last week come two more quantum items. D-Wave Systems today announced the name of its forthcoming 5000-qubit system, Advantage (yes the name choice isn’t serendipity), at its user conference being held this week in Newport, RI. Read more…

By John Russell

Ayar Labs to Demo Photonics Chiplet in FPGA Package at Hot Chips

August 19, 2019

Silicon startup Ayar Labs continues to gain momentum with its DARPA-backed optical chiplet technology that puts advanced electronics and optics on the same chip Read more…

By Tiffany Trader

Crystal Ball Gazing: IBM’s Vision for the Future of Computing

October 14, 2019

Dario Gil, IBM’s relatively new director of research, painted a intriguing portrait of the future of computing along with a rough idea of how IBM thinks we’ Read more…

By John Russell

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

Intel Confirms Retreat on Omni-Path

August 1, 2019

Intel Corp.’s plans to make a big splash in the network fabric market for linking HPC and other workloads has apparently belly-flopped. The chipmaker confirmed to us the outlines of an earlier report by the website CRN that it has jettisoned plans for a second-generation version of its Omni-Path interconnect... Read more…

By Staff report

Kubernetes, Containers and HPC

September 19, 2019

Software containers and Kubernetes are important tools for building, deploying, running and managing modern enterprise applications at scale and delivering enterprise software faster and more reliably to the end user — while using resources more efficiently and reducing costs. Read more…

By Daniel Gruber, Burak Yenier and Wolfgang Gentzsch, UberCloud

Dell Ramps Up HPC Testing of AMD Rome Processors

October 21, 2019

Dell Technologies is wading deeper into the AMD-based systems market with a growing evaluation program for the latest Epyc (Rome) microprocessors from AMD. In a Read more…

By John Russell

Rise of NIH’s Biowulf Mirrors the Rise of Computational Biology

July 29, 2019

The story of NIH’s supercomputer Biowulf is fascinating, important, and in many ways representative of the transformation of life sciences and biomedical res Read more…

By John Russell

Xilinx vs. Intel: FPGA Market Leaders Launch Server Accelerator Cards

August 6, 2019

The two FPGA market leaders, Intel and Xilinx, both announced new accelerator cards this week designed to handle specialized, compute-intensive workloads and un Read more…

By Doug Black

When Dense Matrix Representations Beat Sparse

September 9, 2019

In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance pen Read more…

By James Reinders

With the Help of HPC, Astronomers Prepare to Deflect a Real Asteroid

September 26, 2019

For years, NASA has been running simulations of asteroid impacts to understand the risks (and likelihoods) of asteroids colliding with Earth. Now, NASA and the European Space Agency (ESA) are preparing for the next, crucial step in planetary defense against asteroid impacts: physically deflecting a real asteroid. Read more…

By Oliver Peckham

Cerebras to Supply DOE with Wafer-Scale AI Supercomputing Technology

September 17, 2019

Cerebras Systems, which debuted its wafer-scale AI silicon at Hot Chips last month, has entered into a multi-year partnership with Argonne National Laboratory and Lawrence Livermore National Laboratory as part of a larger collaboration with the U.S. Department of Energy... 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