About physicsandcake

A quantum computer programming physicist with a penchant for machine learning, future technologies, tea & cake.

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. quantum_learning_1But 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):

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. quantum_learning_2 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.

Embracing Disruption: Lessons from building the first quantum computer

Here’s an article written about disruptive technology in Canada featuring a case study of the impact D-Wave could have on future innovation in healthcare:

Embracing disruption: Lessons from building the first quantum computer

From the article:
“Many health experts believe that in the next five to ten years, quantum computing will radically improve the ability to understand, treat and cure diseases.”

It’s like the quantum computer is playing 20 questions…

I’ve been thinking about the BlackBox compiler recently and came up with a very interesting analogy to the way it works. There are actually lots of different ways to think about how BlackBox works, and we’ll post more of them over time, but here is a very high level and fun one.

The main way that you use BlackBox is to supply it with a classical function which computes the “goodness” of a given bitstring by returning a real number (the lower this number, the better the bitstring was).

Whatever your optimization problem is, you need to write a function that encodes your problem into a series of bits (x1, x2, x3…. xN) to be discovered, and which also computes how “good” a given bitstring (e.g. 0,1,1…0) is. When you pass such a function to Blackbox, the quantum compiler then repeatedly comes up with ideas for bitstrings, and using the information that your function supplies about how good its “guesses” are, it quickly converges on the best bitstring possible.

So using this approach the quantum processor behaves as a co-processor to a classical computing resource. The classical computing resources handles one part of the problem (computing the goodness of a given bitstring), and the quantum computer handles the other (suggesting bitstrings). I realized that this is described very nicely by the two computers playing 20 questions with one another.

Quantum computer 20 questions

The quantum computer suggests creative solutions to a problem, and then the classical computer is used to give feedback on how good the suggested solution is. Using this feedback, BlackBox will intelligently suggest a new solution. So in the example above, Blackbox knows NOT to make the next question “Is it a carrot?”

There is actually a deep philosophical point here. One of the pieces that is missing in the puzzle of artificial intelligence is how to make algorithms and programs more creative. I have always been an advocate of using quantum computing to power AI, but we now start to see concrete ways in which it could really start to address some of the elusive problems that crop up when trying to build intelligent machines.

At D-Wave, we have been starting some initial explorations in the areas of machine creativity and machine dreams, but it is early days and the pieces are only just starting to fall into place.

I was wondering if you could use the QC to actually play 20 questions for real. This is quite a fun application idea. If anyone has any suggestions for how to craft 20 questions into an objective function, let me know. My first two thoughts were to do something with Wordnet and NLTK. You could try either a pattern matching or a machine learning version of ‘mining’ wordnet for the right answer. This project would be a little Watson-esque in flavour.

“Inside the chip” – new video showing Rainier 128 processor

Here is a video showing how some of the parts of a D-Wave Rainier processor go together to create the fabric of the quantum computer.

The animation shows how the processor is made up of 128 qubits, 352 couplers and nearly 24,000 Josephson junctions. The qubits are arranged in a tiling pattern to allow them to connect to one another.


New tutorials on devPortal: WMIS and MCS

There are two new tutorials on the website, complete with code snippets! Click on the images to go to the tutorial pages on the developer portal:

Quantum computer tutorial quantum programming

This tutorial (above) describes how to solve Weighted Maximum Independent Set (WMIS) problems using the hardware. Finding the Maximum Independent Set of a bunch of connected variables can be very useful. At a high level, the MIS it gives us information about the largest number of ‘things’ that can be achieved from a set when lots of those ‘things’ have conflicting requirements. In the tutorial, an example is given of scheduling events for a sports team, but you can imagine all sorts of variants: Train timetabling to improve services, assigning patients to surgeons to maximize the throughput of vital operations and minimize waiting lists, adjusting variable speed limits on motorways to reduce traffic jams during periods of congestion, etc etc.

Quantum computer tutorial quantum programming

This tutorial (above) describes how to find Maximum Common Subgraphs given two graphs. The example given in this tutorial is in molecule matching. Looking for areas where sub-structures in molecules are very similar can give us information about how such molecules behave. This is just one simple example of MCS. You can also imagine the same technique being applied to social networks to look for matches between the structuring of social groups. This technique could be used for improving ad placement or even for detecting crime rings.

These two tutorials are closely linked – as finding the MCS involves finding the MIS as part of the process. There are also lots of interesting applications of both these methods in graph and number theory.

If anyone would like to implement WMIS or MCS to solve any of the problem ideas mentioned in this post, please feel free!

Physics World blog article featuring D-Wave

On Friday Hamish Johnston from Physics World visited D-Wave to have a look round and investigate the ‘inside’ of the D-Wave box. Read his report here!

Physics World Blog >> Inside the box at D-Wave

Quantum Computing PhysicsWorld Blog

Here are a few of the photos from his visit on Flickr:

Visit to D-Wave Systems Flickr set

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!

quantum computer quantum computing D-Wave Systems Vesuvius

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


quantum computer quantum computing D-Wave Systems Vesuvius

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 :)


quantum computer quantum computing D-Wave Systems Vesuvius

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.


quantum computer quantum computing D-Wave Systems Vesuvius

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


quantum computer quantum computing D-Wave Systems Vesuvius

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.


quantum computer quantum computing D-Wave Systems Vesuvius

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.


quantum computer quantum computing D-Wave Systems Vesuvius

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


quantum computer quantum computing D-Wave Systems Vesuvius

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


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:

quantum computing

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.

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

quantum computing

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.

quantum computing

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:

quantum computing

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.

quantum computing

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.

quantum computing

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):

quantum computing

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).

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:

quantum computing

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

quantum computing

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:

quantum computing

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.