Some new Rainier science

Here is a short break from the sparse coding mayhem. A recent paper by some interesting folks appeared today on the arxiv. They ran some experiments on the Rainier-based system at USC.

Here is some of what they found:

Our experiments have demonstrated that quantum annealing with more than one hundred qubits takes place in the D-Wave One device… the device has sufficient ground state quantum coherence to realise a quantum annealing of a transverse field Ising model.

Here is a link to the arxiv paper.

Quantum annealing with more than one hundred qubits

Quantum computing for learning: Shaping reality both figuratively and literally

I’d like to document in snippets and thought-trains a little more of the story behind how my co-workers and I are trying to apply quantum computing to the field of intelligence and learning. I honestly think that this is the most fascinating and cool job in the world. The field of Artificial Intelligence (AI) – after a period of slowdown – is now once again exploding with possibility. Big data, large-scale learning, deep networks, high performance computing, bio-inspired architectures… There have been so many advancements lately that it’s kind of hard to keep up! Similarly, the work being done on quantum information processing here at D-Wave is ushering in a new computational revolution. So being a multi-disciplinary type and somewhat masochistic, I find it exciting to explore just how far we can take the union of these two fields.

Approximately 5 years ago, while working on my PhD at University, I started to have an interest in whether or not quantum computing could be used to process information in a brain-like way. I’m trying to remember where this crazy obsession came from. I’d been interested in brains and artificial intelligence for a while, and done a bit of reading on neural nets. Probably because one of my friends was doing a PhD that involved robots and I always thought it sounded super-cool. But I think that it was thinking about Josephson junctions that really got me wondering. Josephson junctions are basically switches. But they kind of have unusual ways of switching (sometimes when you don’t want them to). And because of this, I always thought that Josephson junctions are a bit like neurons. So I started searching the literature to find ways in which researchers had used these little artificial neurons to compute. And surprisingly, I found very little. There were some papers about how networks of Josephson junctions could be used to simulate neurons, but no-one had actually built anything substantial. I wrote a bit about this in a couple of old posts (from Physics and Cake blog):

• Quantum Neural Networks 1: The Superconducting Neuron Model
• Josephson Junction Neurons

I’d read about the D-Wave architecture and I’d been following the company’s progress for some time. After reading a little about the promise of Josephson junction networks, and the pitfalls of the endeavour (mostly because making the circuits reproducible is extremely difficult), I then began wondering whether or not the D-Wave processor could be used in this way. It’s a network of qubits made from Josephson junctions after all, and they’re connected together so that they talk to each other. Yeah, kind of like neurons really. Isn’t that funny. And hey, those D-Wave types have spent 8 years getting that network of Josephson junctions to behave itself. Getting it to be programmable, addressable, robust, and scalable. Hmm, scalable…. I particularly like that last one. Brains are like, big. Lotsa connections. And also, I thought to myself (probably over tea and cake), if the neurons are qubits, doesn’t that mean you can put them in superposition and entangled states? What would that even mean? Boy, that sounds cool. Maybe they would process information differently, and maybe they could even learn faster if they could be in combinations of states at the same time and … could you build a small one and try it out?

The train of thought continued.

From quantum physics to quantum brains

That was before I joined D-Wave. Upon joining the company, I got to work applying some of my physics knowledge to helping build and test the processors themselves. However there was a little part of me that still wanted to actually find ways to use them. Not too long after I had joined the company there happened to be a competition run internally at D-Wave known as ‘Apps Day’, open to everyone in the company, where people were encouraged to try to write an app for the quantum computer. Each candidate got to give a short presentation describing their app, and there were prizes at stake.

I decided to try and write an app that would allow the quantum computer to learn how to play the board game Go. It was called QUAGGA, named after an extinct species of zebra. As with similar attempts involving the ill-fated zebra, I too might one day try to resurrect my genetically-inferior code. Of course this depends on whether or not I ever understand the rules of Go well enough to program it properly Anyway… back to Apps Day. There were several entries and I won a runner-up prize (my QUAGGA app idea was good even though I hadn’t actually finished coding it or run it on the hardware). But the experience got me excited and I wanted to find out more about how I could apply quantum processing to applications, especially those in the area of machine learning and AI.

That’s why I moved from physics into applications development.

Since then the team I joined has been looking into applying quantum technology to various areas of machine learning, in a bid to unite two fields which I have a really strong feeling are made for each other. I’ve tried to analyse where this hunch originates from. The best way to describe it is that I really want to create models of machine intelligence and creativity that are bio-inspired. To do that I believe that you have to take inspiration from the mammalian brain, such as its highly parallel, hierarchical arrangement of substructures. And I just couldn’t help but keep thinking: D-Wave’s processors are highly parallel systems with qubits that can be in one of two states (similar to firing or not firing neurons) with connections between them that can be inhibitory or excitory. Moreover, like the brain, these systems, are INCREDIBLY energy efficient because they are designed to do parallel processing. Modern CPUs are not – hence why brain simulations and machine learning programs take so much energy and require huge computer clusters to run. I believe we need to explore many different hardware and software architectures if we want to get smarter about intelligent computing and closer to the way our own minds work. Quantum circuits are a great candidate in that hunt for cool brain-like processing in silicon.

So what on earth happened here? I’d actually found a link between my two areas of obsession interest and ended up working on some strange joint project that combined the best of both worlds. Could this be for real? I kept thinking that maybe I wanted to believe so badly I was just seeing the machine-learning messiah in a piece of quantum toast. However, even when I strive to be truly objective, I still find a lot of evidence that the results of this endeavour could be very fruitful.

Our deep and ever-increasing understanding of physics (including quantum mechanics) is allowing us to harness and shape the reality of the universe to create new types of brains. This is super-cool. However, the thing I find even cooler is that if you work hard enough at something, you may discover that several fascinating areas are related in a deeper way than you previously understood. Using this knowledge, you can shape the reality of your own life to create a new, hybrid project idea to work on; one which combines all the things you love doing.

Lockheed Martin piece about D-Wave technology

Here is a short piece describing what Lockheed Martin and scientists at USC think about D-Wave technology.

Here is a great quote from the piece.

It’s a game changer for the corporation, it’s a game changer for our customers, and ultimately it’s a game changer for humanity. Computationally this is the equivalent of the Wright brothers at Kitty Hawk.

- Greg Tallant, Research Engineering Manager, Flight Control & VMS Integration – FW, Advanced Development Programs, Lockheed Martin Aeronautics Company

D-Wave Ones in their native habitat

Here is a nice shot of two D-Wave One^TM systems being tested in the lab!

Videos from the NASA Quantum Future Technologies Conference

There are a bunch of cool videos available online from presentations at the recent NASA Quantum Future Technologies conference.

In case you don’t want to watch all the talks (there are a lot!), I’ll point out a few which are most relevant to this blog. If you have time though I’d recommend browsing the full list of talks, as there were lots of extremely interesting themes at the conference!

One of the cool things about this conference was just how much of a focus there was on Adiabatic Quantum Computing. Hopefully the talks will inspire even more people to view this form of quantum computing as scalable, robust and useful for problem-solving.

Click on the images to watch the talks via Adobe connect:

First is Geordie’s talk on Machine Learning using the D-Wave One system – presenting D-Wave’s latest experimental results on applying the quantum computing system to learning problems such as image compression, image recognition and object detection and tracking:

Hartmut Neven works at Google on image search and computer vision applications. His talk describes how the quantum computing technology at D-Wave is being applied to some challenging problems in this field:

Mohammad Amin from D-Wave describes how noise comes into play in the Adiabatic Quantum Computing model, and explains how the adverse effects of decoherence can be avoided, and do not disrupt the quantum computation when the system is designed correctly:

Frank Gaitan describes how the D-Wave One system can be used to explore a problem in graph theory called Ramsey numbers:

Sergio Boixo describes using the D-Wave One for Adiabatic Quantum Machine Learning and how this relates to the ‘clean energy project’ being performed at USC:

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.

IEEE talk at Johns Hopkins University

I gave a talk at Johns Hopkins University on Monday entitled ‘Why the world needs quantum computing’. The video was for a general audience (no specific quantum physics background required). Here is a link to the talk. The video is available for download from this site!