Google Charts Two-Dimensional Quantum Course

By Tiffany Trader

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. Delivering a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world’s foremost experts in quantum computing, emphasized that building a useful quantum device is not just about the number of qubits; getting to 50 or 1,000 or 1,000,000 qubits doesn’t mean anything without quality error-corrected qubits to start with.

Martinis compares focusing on merely the number of qubits to wanting to buy a high-performance computer and only specifying the number of cores. How to create quality qubits is something that the leaders in the quantum space at this nascent stage are still figuring out. Google — as well as IBM, Intel, Rigetti, and Yale – are advancing the superconducting qubit approach to quantum computing. Microsoft, Delft, and UC Santa Barbara are involved in topological quantum computing. Photonic quantum computing and trapped ions are other approaches.

The reason quality is difficult in the first place is that qubits – the processing unit of the quantum system – are fundamentally sensitive to small errors, much more so than the classical bit. Martinis explains with a coin on the table analogy:

“If you want to think about classical bits – you can think of that as a coin on a table; we can represent classical information as heads or tails. Classical information is inherently stable. You have this coin on the table, there’s a restoring force, there’s dissipation so even if there’s a little bit of noise it’s going to be stable at zero or one. In a quantum computer you can represent [a quantum bit] not as a coin on a table but a coin in free space, where say zero is up, and one is down and rotated 90 degrees is zero plus one; and in fact you can have any amount of zero and one and it can rotate in this way to change something called quantum phase. You see since it’s kind of an analog system, it can point in any direction. This means that any small change in this is going to give you an error.

“Error correction in quantum systems is a little bit similar to what you see in classical systems where you have clocked logic so you have a memory source, where you have a clock and every clock period you can compute through some arithmetic logic and then you sequence through this and the clock timing kind of takes care of all the different delays you have in the logic. Similar here, you have kind of repetition of the error correction, based on taking the qubit and encoding it in many other qubits and doing parity measurements to see if you’re having both bit-flip errors going like this or phase flip errors going like that.”

The important thing to remember says Martinis is that if you want to have small errors, exponentially small errors, of 10-9 or 10-12, you need a lot of qubits, i.e., quantity, and pretty low error rates of about one error in one-thousand operations, i.e., quality.

In Martinis’s view, quantum computing is “a two-dimensional horse race,” where the tension between quality and quantity means you can’t think in terms of either/or; you have to think about doing both of them at the same time. Progress of the field can thus be charted on a two-dimensional plot.

The first thing to note when assessing the progress in the field are the limiting error rate and the number of qubits for a single device, says Martinis. The chart depicts, for a single device, the worst error rate, the limiting error rate, and the number of qubits. Google is aiming for an error correction of 10-3 in about 103 qubits.

“What happens, “says Martinis, “is as that error rate goes up the number of qubits you have to have to do error correction properly goes up and blows up at the error correction threshold of about 1 percent. I call this the error correction gain. It’s like building transistors with gain; if you want to make something useful you have to have an error correction that’s low enough. If the error correction is not good enough, it doesn’t matter if you have a billion qubits, you are never going to be able to make them more accurate.”

Up to 50 qubits is classically simulatable, but if the error rate is high it gets easier but it is not useful. Pointing to the lower half of the chart, Martinis says “we want to be down here and making lots of qubits. It’s only once you get down here [below the threshold] that talking quantity by itself makes sense.”

One of the challenges of staying under that error correction threshold is that scaling qubits itself can impede error correction, due to undesired cross-talk between qubits. Martinis says that the UC Santa Barbara technology it is working with was designed to reduce cross-talk to produce a scalable technology. For flux cross-talk, fledgling efforts were at 30-40 percent cross-talk. “The initial UC Santa Barbara device was between 1 percent to .1 percent cross-talk and now it’s 10-5,” says Martinis, adding “we barely can measure it.”

The solid black dot on the chart (above) represents that UC Santa Barbara chip. It is 9 qubits and dips just beneath the error correction threshold. Now with its follow-on Bristlecone chip architecture, Google is working to scale the UCSB prototype to >50 qubits to show quantum supremacy, the point at which it would be longer feasible to classically simulate it. The Google team is focused on improving error correction with the expectation that near-term applications will then be feasible. Martinis says the next step is to move out to ~1,000 qubits with exponentially small errors. The end goal is to scale up to a million-or-so qubits with low error rates to solve real-world problems that are intractable on today’s best supercomputers.

The Bristlecone chip consists of 72 qubits, arranged in 2D array. The device has been made and is now undergoing testing to make sure it is operating correctly. Google uses its Qubit Speckle algorithm to validate its quantum supremacy experiments.

Martinis reports that progress on quantum algorithms is also advancing. One of the most compelling applications for quantum computers is quantum chemistry. It’s a natural application for quantum computing, says Martinis. The algorithm though is exponentially hard. In 2011, Microsoft’s quantum computing group documented an O(n11) quantum chemistry algorithm, which would take the age of the universe to run. Work has since progressed and recently the Google theory group showed an algorithm that is Õ(N2.67) for the exact solution and O(N) for the approximate. “[The exact implementation] would take about 100 logical qubits, requiring a million physical qubits,” Martinis notes. “It’s beyond what we can do now, but now the numbers are reasonable so we can think about doing it.”

In closing, Martinis points out that the metrics for assessing the progress of quantum computing in addition to quality and quantity also include speed and connectivity. In different technologies, there can be a factor of 105 or so different speeds. For networking, he says you need at least 2D nearest neighbor corrections to do the error correction properly. Referring to the chart on Google’s key metrics (at left), Martinis says the company isn’t ready to talk about Bristlecone’s error-correction or speed yet but it anticipates good numbers and hopes to show quantum supremacy “very soon.”

Link to slides: https://www.hpcuserforum.com/presentations/tuscon2018/QCOverview_Google_UFTucson2018.pdf

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!

GDPR’s Impact on Scientific Research Uncertain

May 24, 2018

Amid the angst over preparations—or lack thereof—for new European Union data protections entering into force at week’s end is the equally worrisome issue of the rules’ impact on scientific research. Among the Read more…

By George Leopold

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Francisco, one would be tempted to dismiss its claims of inventing Read more…

By John Russell

HPE Extreme Performance Solutions

HPC and AI Convergence is Accelerating New Levels of Intelligence

Data analytics is the most valuable tool in the digital marketplace – so much so that organizations are employing high performance computing (HPC) capabilities to rapidly collect, share, and analyze endless streams of data. Read more…

IBM Accelerated Insights

Mastering the Big Data Challenge in Cognitive Healthcare

Patrick Chain, genomics researcher at Los Alamos National Laboratory, posed a question in a recent blog: What if a nurse could swipe a patient’s saliva and run a quick genetic test to determine if the patient’s sore throat was caused by a cold virus or a bacterial infection? Read more…

Silicon Startup Raises ‘Prodigy’ for Hyperscale/AI Workloads

May 23, 2018

There's another silicon startup coming onto the HPC/hyperscale scene with some intriguing and bold claims. Silicon Valley-based Tachyum Inc., which has been emerging from stealth over the last year and a half, is unveili Read more…

By Tiffany Trader

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

Silicon Startup Raises ‘Prodigy’ for Hyperscale/AI Workloads

May 23, 2018

There's another silicon startup coming onto the HPC/hyperscale scene with some intriguing and bold claims. Silicon Valley-based Tachyum Inc., which has been eme Read more…

By Tiffany Trader

Japan Meteorological Agency Takes Delivery of Pair of Crays

May 21, 2018

Cray has supplied two identical Cray XC50 supercomputers to the Japan Meteorological Agency (JMA) in northwestern Tokyo. Boasting more than 18 petaflops combine Read more…

By Tiffany Trader

ASC18: Final Results Revealed & Wrapped Up

May 17, 2018

It was an exciting week at ASC18 in Nanyang, China. The student teams braved extreme heat, extremely difficult applications, and extreme competition in order to cross the cluster competition finish line. The gala awards ceremony took place on Wednesday. The auditorium was packed with student teams, various dignitaries, the media, and other interested parties. So what happened? Read more…

By Dan Olds

Spring Meetings Underscore Quantum Computing’s Rise

May 17, 2018

The month of April 2018 saw four very important and interesting meetings to discuss the state of quantum computing technologies, their potential impacts, and th Read more…

By Alex R. Larzelere

Quantum Network Hub Opens in Japan

May 17, 2018

Following on the launch of its Q Commercial quantum network last December with 12 industrial and academic partners, the official Japanese hub at Keio University is now open to facilitate the exploration of quantum applications important to science and business. The news comes a week after IBM announced that North Carolina State University was the first U.S. university to join its Q Network. Read more…

By Tiffany Trader

Democratizing HPC: OSC Releases Version 1.3 of OnDemand

May 16, 2018

Making HPC resources readily available and easier to use for scientists who may have less HPC expertise is an ongoing challenge. Open OnDemand is a project by t Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

CFO Steps down in Executive Shuffle at Supermicro

January 31, 2018

Supermicro yesterday announced senior management shuffling including prominent departures, the completion of an audit linked to its delayed Nasdaq filings, and Read more…

By John Russell

Deep Learning Portends ‘Sea Change’ for Oil and Gas Sector

February 1, 2018

The billowing compute and data demands that spurred the oil and gas industry to be the largest commercial users of high-performance computing are now propelling Read more…

By Tiffany Trader

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Sympo Read more…

By Staff

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This