D-Wave (AI)

Saturday 12th December 2015

D-Wave: The Quantum Computing Era Has Begun

Geordie Rose is a founder and CTO of D-Wave. He is known as a leading advocate for quantum computing and physics-based processor design

Founded in 1999, D-Wave Systems is the world’s first quantum computing company. Our mission is to integrate new discoveries in physics, engineering, manufacturing, and computer science into breakthrough approaches to computation that help solve some of the world’s most complex challenges.

Despite the incredible power of today’s supercomputers, there are many complex computing problems that can’t be addressed by conventional systems. Our need to better understand everything, from the universe to our own DNA, leads us to seek new approaches to answer the most difficult questions.

While we are only at the beginning of this journey, quantum computing has the potential to help solve some of the most complex technical, commercial, scientific, and national defense problems that organizations face. We expect that quantum computing will lead to breakthroughs in science, engineering, modeling and simulation, financial analysis, optimization, logistics, and national defense applications.

Today D-Wave is the recognized leader in the development, fabrication, and integration of superconducting quantum computers. Our systems are being used by world-class organizations and institutions including Lockheed-Martin, Google, NASA, and USC. D-Wave has been granted over 110 US patents and has published over 80 peer-reviewed papers in leading scientific journals.

In 2010 we released our first commercial system, the D-Wave One™ quantum computer. We have doubled the number of qubits each year, and in 2013 we shipped our 512-qubit D-Wave Two™ system. In 2015 we announced general availability of the 1000+ qubit D-Wave 2X™ system. – D-Wave

Saturday 12th December 2015

Rose’s Law for Quantum Computers Keeps Marching On

[Update in 2015: the hardware curve that is “Rose’s Law” (blue diamonds) remains on track. The software and performance/qubit (red stars, as applied to certain tasks) is catching up, and may lag by a couple years from the original prediction overlaid onto the graph] – Steve Jurvetson

When I first met Geordie Rose in 2002, I was struck by his ability to explain complex quantum physics and the “spooky” underpinnings of quantum computers. I had just read David Deutsch’s Fabric of Reality [1997] where he predicts the possibility of such computers, and so I invited Rose to one of our tech conferences.

We first invested [in D-Wave] in 2003 , and Geordie predicted that he would be able to demonstrate a two-bit quantum computer within 6 months.

There was a certain precision to his predictions. With one bit under his belt, and a second coming, he went on to suggest that the number of qubits in a scalable quantum computing architecture should double every year. It sounded a lot like Gordon Moore’s prediction back in 1965, when he extrapolated from just five data points on a log-scale.

So I called it “Rose’s Law” and that seemed to amuse him. Well, the decade that followed has been quite amazing.

So, how do we read the graph above?

Like Moore’s Law, a straight line describes an exponential. But unlike Moore’s Law, the computational power of the quantum computer should grow exponentially with the number of entangled qubits as well. It’s like Moore’s Law compounded. (D-Wave just put together an animated visual of each processor generation in this video, bringing us to the present day.)

And now, it gets mind bending. If we suspend disbelief for a moment, and use D-Wave’s early data on processing power scaling (more on that below), then the very near future should be the watershed moment, where quantum computers surpass conventional computers and never look back. Moore’s Law cannot catch up.

A year later, it outperforms all computers on Earth combined.

Double qubits again the following year, and it outperforms the universe. What the???? you may ask… Meaning, it could solve certain problems that could not be solved by any non-quantum computer, even if the entire mass and energy of the universe was at its disposal and molded into the best possible computer. It is a completely different way to compute — as David Deutsch posits — harnessing the refractive echoes of many trillions of parallel universes to perform a computation.

First the caveat (the text in white letters on the graph).  D-Wave has not built a general-purpose quantum computer. Think of it as an application-specific processor, tuned to perform one task — solving discrete optimization problems.

This happens to map to many real world applications, from finance to molecular modeling to machine learning, but it is not going to change our current personal computing tasks. In the near term, assume it will apply to scientific supercomputing tasks and commercial optimization tasks where a heuristic may suffice today, and perhaps it will be lurking in the shadows of an Internet giant’s data center improving image recognition and other forms of near-AI magic. In most cases, the quantum computer would be an accelerating coprocessor to a classical compute cluster.

There is also the question of the programming model. Until recently, programming a quantum computer was more difficult than machine coding an Intel processor. Imagine having to worry about everything from analog gate voltages to algorithmic transforms of programming logic to something native to quantum computing (Shor and Grover and some bright minds have made the occasional mathematical breakthrough on that front).

With the application-specific quantum processor, D-Wave has made it all much easier, and with their forthcoming Black Box overlay, programming moves to a higher level of abstraction, like training a neural network with little understanding of the inner workings required.

In any case, the possibility of a curve like this begs many philosophical and cosmological questions about our compounding capacity to compute… the beginning of infinity if you will.

While it will be fascinating to see if the next three years play out like Rose’s prediction, for today, perhaps all we should say is that it’s not impossible. And what an interesting world this may be. – Steve Jurvetson, October 2012

 

Saturday 12th December 2015

The Watershed Moment: Quantum Computer Announcement from Google

Boom! Google just announced their watershed results in quantum computing using their D-Wave Two.

It is rare to see a 100,000,000x leap in computing power… at least in this universe! =)

From the D-Wave board meeting today, I learned that it cost Google $1m to run the massive computation on their classic computers. The SA and QMC (classic computers) data points cost $1m of energy, and the green curve totally choked on large problem sets (that’s why there are no green data points in the top right). The D-wave computer operating cost was well over 100x less.

Has there ever been a leap forward like this in human history? (in any thing, like computing, energy processing, transportation… I am guessing there have purely algorithmic advances of this magnitude, but having trouble thinking of a single advance of this scale) – Steve Jurvetson, December 2015

 

Tuesday 29th December 2015

Building The Quantum Dream Machine

John Martinis has been researching how quantum computers could work for 30 years. Now he could be on the verge of finally making a useful one.

With his new Google lab up and running, Martinis guesses that he can demonstrate a small but useful quantum computer in two or three years. “We often say to each other that we’re in the process of giving birth to the quantum computer industry,” he says.

The new computer would let a Google coder run calculations in a coffee break that would take a supercomputer of today millions of years.

The software that Google has developed on ordinary computers to drive cars or answer questions could become vastly more intelligent. And earlier-stage ideas bubbling up at Google and its parent company, such as robots that can serve as emergency responders or software that can converse at a human level, might become real.

As recently as last week the prospect of a quantum computer doing anything useful within a few years seemed remote. Researchers in government, academic, and corporate labs were far from combining enough qubits to make even a simple proof-of-principle machine.

A well-funded Canadian startup called D-Wave Systems sold a few of what it called “the world’s first commercial quantum computers” but spent years failing to convince experts that the machines actually were doing what a quantum computer should.

Then NASA summoned journalists to building N-258 at its Ames Research Center in Mountain View, California, which since 2013 has hosted a D-Wave computer bought by Google.

There Hartmut Neven, who leads the Quantum Artificial Intelligence lab Google established to experiment with the D-Wave machine, unveiled the first real evidence that it can offer the power proponents of quantum computing have promised.

In a carefully designed test, the superconducting chip inside D-Wave’s computer—known as a quantum annealer—had performed 100 million times faster than a conventional processor.

However, this kind of advantage needs to be available in practical computing tasks, not just contrived tests. “We need to make it easier to take a problem that comes up at an engineer’s desk and put it into the computer,” said Neven.

That’s where Martinis comes in. Neven doesn’t think D-Wave can get a version of its quantum annealer ready to serve Google’s engineers quickly enough, so he hired Martinis to do it.

“It became clear that we can’t just wait,” Neven says. “There’s a list of shortcomings that need to be overcome in order to arrive at a real technology.”

He says the qubits on D-Wave’s chip are too unreliable and aren’t wired together thickly enough. (D-Wave’s CEO, Vern Brownell, responds that he’s not worried about competition from Google.)

Google will be competing not only with whatever improvements D-Wave can make, but also with Microsoft and IBM, which have substantial quantum computing projects of their own.

But those companies are focused on designs much further from becoming practically useful. Indeed, a rough internal time line for Google’s project estimates that Martinis’s group can make a quantum annealer with 100 qubits as soon as 2017.

The difficulty of creating qubits that are stable enough is the reason we don’t have quantum computers yet. But Martinis has been working on that for more than 11 years and thinks he’s nearly there.

The coherence time of his qubits, or the length of time they can maintain a superposition, is tens of microseconds—about 10,000 times the figure for those on D-Wave’s chip.

Martinis aims to show off a complete universal quantum computer with about 100 qubits around the same time he delivers Google’s new quantum annealer, in about two years.

He thinks that once he can get his qubits reliable enough to put 100 of them on a universal quantum chip, the path to combining many more will open up. “This is something we understand pretty well,” he says. “It’s hard to get coherence but easy to scale up.”

Figuring out how Martinis’s chips can make Google’s software less stupid falls to Neven.

He thinks that the prodigious power of qubits will narrow the gap between machine learning and biological learning—and remake the field of artificial intelligence. “Machine learning will be transformed into quantum learning,” he says. That could mean software that can learn from messier data, or from less data, or even without explicit instruction.

Neven muses that this kind of computational muscle could be the key to giving computers capabilities today limited to humans. “People talk about whether we can make creative machines–the most creative systems we can build will be quantum AI systems,” he says.

Neven pictures rows of superconducting chips lined up in data centers for Google engineers to access over the Internet relatively soon.

“I would predict that in 10 years there’s nothing but quantum machine learning–you don’t do the conventional way anymore,” he says.

A smiling Martinis warily accepts that vision. “I like that, but it’s hard,” he says. “He can say that, but I have to build it.” – Tom Simonite

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