Update on D-Wave One at USC

Here is a recent news piece describing some of what’s going on at the new USC center where the first publicly accessible D-Wave One is housed.

A Quantum Leap in Computing

USC recently opened the USC-Lockheed Martin Quantum Computing Center, which houses the world’s first commercial operational quantum processor. USC Dornsife researchers will play a key role in advancing the center’s groundbreaking research.

By Ambrosia Viramontes-Brody
January 3, 2012

Daniel Lidar, a professor of chemistry in USC Dornsife with a joint appointment in USC Viterbi, serves as the scientific and technical director of the recently founded USC-Lockheed Martin Quantum Computing Center. Photo by Ziva Santop/Steve Cohn Photography.

D-Wave makes HPCwire’s top 10 best stories of 2011

From the article:

Hit: Quantum Computing Goes Commercial

In May, D-Wave Systems sold the world’s first quantum computer. The buyer was Lockheed Martin Corporation, who did not disclose how they intend to use the machine. The system, named D-Wave One, employs a 128-qubit chip, called Rainier, and uses superconducting technology to generate “adiabatic quantum computing” (that some claim is not true quantum computing). The cost of the system was not disclosed, but undoubtedly this is one of those cases in which if you have to ask, you probably can’t afford it.

It still bothers me (marginally) that the claim that AQC is not “true quantum computing” still festers in the collective conscious. One of the things I’ve learned over the past ten years is that dogma is extremely difficult to dislodge. Opinions and beliefs have tremendous inertia — even ones that are wrong and/or harmful.

I think the gate model of quantum computing set back the field of actually building real quantum computers by 20 years or so. I can imagine a parallel universe where the ideas of experimental condensed matter physicists drove the underlying theory of quantum computation, instead of theoretical computer scientists and mathematicians. In this parallel universe, by now we’d likely have dozens of real working quantum computers of all sorts of types. The main problem with the gate model is that, while it is beautiful for theoretical computer scientists, it is astronomically horrible from the implementation side. Somehow we got into a situation where experimental physicists (ie implementers) bought the story that the gate model was “real” quantum computing and other ideas weren’t.

Someone (I think maybe it was Eric) had a classic line that I sometimes think about when this subject comes up. When questioned about whether what we’ve built was a “real” quantum computer, he said “How about we race our 25,000 Josephson junction superconducting adiabatic quantum computer against your powerpoint deck [editor's comment: powerpoint deck == most advanced gate model quantum computer ever built] and see who wins.”

The point is that no gate model quantum computer has ever been built. I have speculated for some time that no useful gate model quantum computer will *ever* be built, because of a long list of inter-related challenges that no-one — even though lots of smart people have tried — even has the faintest notion of how to solve.

CBC “on the go” radio interview

Here is a link to a radio interview I did last week on CBC’s On the Go. My bit starts at 1:00 although the turkey songs commercial that precedes it is arguably the best part of the bit.

Presentation at USC – Information Sciences Institute at opening of Lockheed Martin – USC Center for Quantum Computation

Here is the presentation I gave at the opening of the Lockheed Martin – USC Center for Quantum Computation, where the first D-Wave One is installed. It is a little long but gives a good overview of our perspective on quantum computation.

The presentation is linked to from an article written by Gully Burns about his take on the opening of the Center.

Vesuvius: A closer look – 512 qubit processor gallery

The next generation of D-Wave’s technology is called Vesuvius, and it’s going to be a very interesting processor. The testing and development of this new generation of quantum processor is going well. In the meantime, here are some beautiful images of Vesuvius!

Above: An entire wafer of Vesuvius processors after the full fabrication process has completed.

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Above: Photographing the wafer from a different angle allows more of the structure to be seen. Exercise for the reader: Estimate the number of qubits in this image

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Above: A slightly closer view of part of the wafer. The small scale of the structures (<1um) produces a diffraction grating effect (like you see on the underside of a CD) resulting in a beautiful spectrum of colours reflecting from the wafer surface.

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Above: A different angle of shot produces different colours and allows different areas of the circuitry to become visible.

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Above: A close-up image of a single Vesuvius processor on the wafer. The white square seen to the right of the image contains the main ‘fabric’ of 512 connected qubits.

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Above: An image of a processor wire-bonded to the chip carrier, ready to be installed into the computer system. The wires carry the signals to the quantum components and associated circuitry on the chip.

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Above: A larger view of the bonded Vesuvius processor. More of the chip packaging is now also visible in the image.

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Above: The full chip packaging is visible, complete with wafer.

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Quantum computing and light switches

So as part of learning how to become a quantum ninja and program the D-Wave One, it is important to understand the problem that the machine is designed to solve. The D-Wave machine is designed to find the minimum value of a particular mathematical expression which I can write down in one line:

As people tend to be put off by mathematical equations in blogposts, I decided to augment it with a picture of a cute cat. However, unless you are very mathematically inclined (like kitty), it might not be intuitive what minimizing this expression actually means, why it is important, or how quantum computing helps. So I’m going to try to answer those three questions in this post.

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1.) What does the cat’s expression mean?

The machine is designed to solve discrete optimization problems. What is a discrete optimization problem? It is one where you are trying to find the best settings for a bunch of switches. Here’s a graphical example of what is going on. Let’s imagine that our switches are light switches which each have a ‘bias value’ (a number) associated with them, and they can each be set either ON or OFF:

The light switch game

The game that we must play is to set all the switches into the right configuration. What is the right configuration? It is the one where when we set each of the switches to either ON or OFF (where ON = +1 and OFF = -1) and then we add up all the switches’ bias values multiplied by their settings, we get the lowest answer. This is where the first term in the cat’s expression comes from. The bias values are called h’s and the switch settings are called s’s.

So depending upon which switches we set to +1 and which we set to -1, we will get a different score overall. You can try this game. Hopefully you’ll find it easy because there’s a simple rule to winning:

We find that if we set all the switches with positive biases to OFF and all the switches with negative biases to ON and add up the result then we get the lowest overall value. Easy, right? I can give you as many switches as I want with many different bias values and you just look at each one in turn and flip it either ON or OFF accordingly.

OK, let’s make it harder. So now imagine that many of the pairs of switches have an additional rule, one which involves considering PAIRS of switches in addition to just individual switches… we add a new bias value (called J) which we multiply by BOTH the switch settings that connect to it, and we add the resulting value we get from each pair of switches to our overall number too. Still, all we have to do is decide whether each switch should be ON or OFF subject to this new rule.

But now it is much, much harder to decide whether a switch should be ON or OFF, because its neighbours affect it. Even with the simple example shown with 2 switches in the figure above, you can’t just follow the rule of setting them to be the opposite sign to their bias value anymore (try it!). With a complex web of switches having many neighbours, it quickly becomes very frustrating to try and find the right combination to give you the lowest value overall.

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2.) It’s a math expression – who cares?

We didn’t build a machine to play a strange masochistic light switch game. The concept of finding a good configuration of binary variables (switches) in this way lies at the heart of many problems that are encountered in everyday applications. A few are shown in figure below (click to expand):

Even the idea of doing science itself is an optimization problem (you are trying to find the best ‘configuration’ of terms contributing to a scientific equation which matches our real world observations).

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3.) How does quantum mechanics help?

With a couple of switches you can just try every combination of ON’s and OFF’s, there are only four possibilities: [ON ON], [ON OFF], [OFF ON] or [OFF OFF]. But as you add more and more switches, the number of possible ways that the switches can be set grows exponentially:

You can start to see why the game isn’t much fun anymore. In fact it is even difficult for our most powerful supercomputers. Being able to store all those possible configurations in memory, and moving them around inside conventional processors to calculate if our guess is right takes a very, very long time. With only 500 switches, there isn’t enough time in the Universe to check all the configurations.

Quantum mechanics can give us a helping hand with this problem. The fundamental power of a quantum computer comes from the idea that you can put bits of information into a superposition of states. Which means that using a quantum computer, our light switches can be ON and OFF at the same time:

Now lets consider the same bunch of switches as before, but now held in a quantum computer’s memory:

Because all the light switches are on and off at the same time, we know that the correct answer (correct ON/OFF settings for each switch) is represented in there somewhere… it is just currently hidden from us.

What the D-Wave quantum computer allows you to do is take this ‘quantum representation’ of your switches and extract the configuration of ONs and OFFs with the lowest value.
Here’s how you do this:

You start with the system in its quantum superposition as described above, and you slowly adjust the quantum computer to turn off the quantum superposition effect. At the same time, you slowly turn up all those bias values (the h and J’s from earlier). As this is performed, you allow the switches to slowly drop out of the superposition and choose one classical state, either ON or OFF. At the end, each switch MUST have chosen to be either ON or OFF. The quantum mechanics working inside the computer helps the light switches settle into the right states to give the lowest overall value when you add them all up at the end. Even though there are 2^N possible configurations it could have ended up in, it finds the lowest one, winning the light switch game.

The Developer Portal

Keen-eyed readers may have noticed a new section on the D-Wave website entitled ‘developer portal’. Currently the devPortal is being tested within D-Wave, however we are hoping to open it up to many developers in a staged way within the next year.

We’ve been getting a fair amount of interest from developers around the world already, and we’re anxious to open up the portal so that everyone can have access to the tools needed to start programming quantum computers! However given that this way of programming is so new we are also cautious about carefully testing everything before doing so. In short, it is coming, but you will have to wait just a little longer to get access!

A few tutorials are already available for everyone on the portal. These are intended to give a simple background to programming the quantum systems in advance of the tools coming online. New tutorials will be added to this list over time. If you’d like to have a look you can find them here: DEVELOPER TUTORIALS

In the future we hope that we will be able to grow the community to include competitions and prizes, programming challenges, and large open source projects for people who are itching to make a contribution to the fun world of quantum computer programming.

Building it was fun, and hard. But the next step is even more fun, and likely much harder.

 

This really was a team effort. Nearly a decade of hard work by one of the most talented technology dream teams ever assembled.

Building quantum computing hardware was tough. But the task that is now before us surpasses this in difficulty. Now that we have the hardware issues solved, the next step is to harness these awesome new resources to create machines that mimic or surpass human capability in a growing number of dimensions.

Quantum computers use nature in a new way to solve the hard problems that allow machines to learn how to perform tasks better. I believe we are right on the cusp of a major transition in our understanding of, and relationship with, technology. The kinds of truly intelligent systems that we can attempt to build, using quantum computers to perform the hard tasks that currently bottleneck progress, will be disruptively different than anything we can build with conventional computers.

Fun with Puss n Boots

At SC11, the speaker before me was from Dreamworks, and he presented some very interesting facts about how much computing time is required to render their films.

One of the fascinating figures was that the total time spent rendering Puss n Boots was 70 million hours.

We can back of the envelope the amount of energy that was consumed to do this. Imagine an average processor consumes about 100 watts. Then in one hour, one processor consumes about 360 kiloJoules [kJ], and in 70 million hours, the total amount of energy consumed is around 25 TeraJoules [TJ].

So what does 25 TJ mean? Here are some ways to think about it:

1. The world’s biggest power plant is the Three Gorges plant in China. It generates 18,460 MW of power. Puss n Boots rendering = about 23 minutes consuming the full output of this power plant.

2. Assuming that the human brain consumes about 30 watts, and there are about 7 billion humans, the energy required to render Puss n Boots is about the same as about 20 minutes of all energy consumed by every human brain on the planet. [interestingly this is similar to the output of the three gorges dam...]

3. The total power draw of Burnaby (where D-Wave is) is about 120 GigaJoules per capita per year. With a population of about 200,000, the energy used to render Puss n Boots would power all of Burnaby for about 9 days.

4. A 1 kilogram brick, carried up to the 275m platform of the Eiffel Tower, acquires about 2.7 MJ of potential energy relative to the ground. Therefore if all the energy used to render Puss n Boots were converted to potential energy, it would be the same as about 9 million kilograms sitting at the top platform of the Eiffel Tower. This is about the same as the total mass of every person in Burnaby.

5. According to Google maps, it would take me about 90 days of walking 12 hours a day to walk across Canada. Assuming I burn about 5,000 calories a day, with 1 calorie = 4.184 kJ, the amount of energy required to walk across Canada is about 2 GJ. This means that I could walk across Canada 13,000 times — or equivalently walk 12 hours a day for  3,200 years — with the same energy budget as rendering Puss n Boots.

6. A banana contains about 100 calories ~ 420 kJ. If the total energy used to render Puss n Boots were converted into banana food energy, you’d need 60 million bananas. A banana weighs about 150 grams, so that’s about 9 million kilograms of bananas. If you could eat 500 bananas per day, it would take you 327 years to munch your way through that stack.

 

Slide deck from SC11 presentation

Yesterday I drove to Seattle and gave a presentation at Supercomputing 2011. Here is the slide deck:

I drove back to Vancouver the same day … four hours of serious gripping through sleet and snow where I could see about 3 feet in front of the windshield…