Catching quantum mechanics in the act…

So today D-Wave’s latest paper has been published in Nature. You can take a look at the abstract here (and the paper if you have a subscription!). The paper is entitled ‘Quantum annealing with manufactured spins’

So what is this new publication all about?

Manufactured, coupled quantum spins

Manufactured, coupled quantum spins

Everyone knows that when you observe a quantum computation, you destroy it, right? So how are you supposed to know if your quantum computer is working correctly? That’s what this latest Nature article from the scientists at D-Wave addresses. We’ve known for some time that the D-Wave quantum computers are performing computations, and we know that the answers they are giving us are correct, (they agree with our predictions).
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But wouldn’t it be cool to be able to go further, to actually look INSIDE a quantum computer, with large numbers of qubits all interacting and computing, and catch the quantum mechanics in the act?

Not just a string of atoms

The first cool thing about this experiment is that the system under test this isn’t just the usual suspects found in quantum experiments – a string of atoms, a series of electron spins in a crystal, or a bunch of photons. It’s not a curiosity that scientists have found lurking in the natural world allowing them to observe some quantum mechanics. This is a processor! It is programmable – it actually solves problems, looks similar to the integrated circuits inside your laptop, and you can program it using Python! Anyway… I digress. What I mean to say is that it is very important to realise that these quantum effects are controllable. We’re no longer just looking at quantum systems – like atoms – and verifying their quantum nature. We’re taking those systems, and moulding and warping their energy levels, and controlling the way they interact with each other, so that we can use those quantum effects to help us compute.

Respecting the bigger picture

It’s fairly easy to isolate a single quantum bit and do some experiments on it to check that it is behaving quantum mechanically. It’s much harder to test that it’s STILL working quantum mechanically when it’s in the middle of an incredibly complex processor, connected to all kind of lines and electronics. It would be like designing a bridge that was able to support its own weight – but never considering what would happen when the bridge is used as it should be – with high volumes of traffic passing over it every day.
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That’s the second cool thing about this result – during the experiment, the processor is operated in the same way as it is operated during problem solving. We didn’t have to do anything particularly esoteric to the qubits in order to watch them. We’re simply lifting the lid off the black box so we can take a peek at the quantum mechanics of the computation as it happens during normal problem solving.
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In the experiment itself, this ‘black box’ is a subsection of the processor known as a unit cell. It is a fundamental block which is replicated and tiled together to form the larger processor. The unit cell tested contains 8 qubits, all linked together. There are 16 such unit cells in the current generation of D-Wave’s processors – known as the ‘Rainier’ architecture.

Quantum birdwatching

So how exactly do the scientists ‘watch’ the quantum mechanics? Well, the unit cell mentioned above is operated in the same way as it would be during a normal computation – running what is known as a quantum annealing algorithm. The difference is that at a certain point during the computation, the usually slow, careful annealing of the qubits is suddenly interrupted by a very fast signal. This signal causes the unit cell to ‘freeze’ in whatever state it was in at the time. If you repeat the computation lots of times, but each time apply your ‘freezing’ signal at a slightly different moment during the quantum computation, you can build up a series of ‘snapshots’, like stills on a movie reel. D-Wave scientists compiled all these snapshots to reveal exactly what is happening during the quantum computation.
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The next step is to check that these results really do agree with what quantum mechanics tells us. So a theoretical model of the unit cell was set up, based on the predictions of quantum physics, and the model fits very well indeed. Even more interesting, a second model was set up, which captured how CLASSICAL physics predicts the processor should behave. The results were striking – the classical model wasn’t even close! There’s no way these results can be explained using classical physics.
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This is a pretty awesome result for quantum computation in general. People have been worrying for a while that it may not be possible to ever build large scale quantum computing systems, that once we start putting those fragile qubits into a real processor environment that the quantum mechanics will be destroyed. The results from this latest paper reveal to us exactly the opposite – that quantum effects persist, and allow us to control them.
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Maybe quantum mechanics isn’t so spooky after all. In fact, I’d say that the future of building large scale processors that operate using quantum mechanics looks more promising than ever.

16 thoughts on “Catching quantum mechanics in the act…

  1. Hey Suzanne,

    What temperature does your quantum processor work at? Can it work at room temperature or has it got to be dunked in liquid helium first?

    Liquid helium reminds me of the big accident at CERN a few years back – I always imagine a bunch of physicists shouting “Run for your lives! Its a helium leak!” in really high pitched voices as the tunnels filled up with tonnes of gas…

    • Hi Tom,

      The processor works at a maximum temperature of 40mK. A dilution refrigerator is used to cool the processor and the incoming wiring. However, with our systems, the refrigerator is a Pulse-Tube Dilution Refrigerator (PTDR) rather than a so-called ‘wet’ dilution fridge – which means that the cooling power is provided by mechanical pumps that work using thermoacoustic effects:

      http://en.wikipedia.org/wiki/Pulse_tube_refrigerator

      rather than relying on a bath of cold liquid to cool the incoming gases in the refrigerator circuit – so you don’t need to dunk it in a bath of LHe4 as with older fridge designs.

      Hope that helps!!

  2. Congratulations Suzanne. You can’t please everyone though. I just heard that Scott Aaronson is asking on his reality TV show that you and Geordie show your birth certificates.

    • Hi Michael,

      It’s certainly not a dumb noob question! :)

      Yes, it does have applications in this area. There are several bioinformatics problems that can be mapped into the processor hardware. One example is shotgun sequencing, which you can cast as a constraint satisfaction problem (trying to align fragments of DNA all broken at different points is a good example of an optimization problem).

      Another idea, arguably more interesting, is using QC in combination with machine learning techniques to detect patterns between genetic information and phenotype traits.

      Given enough good quality training data, the hardware is good at matching ‘high-level’ labels, (such as ‘does this person have a particular disease’) with low level information such as the presence or absence of many different – subtly correlated – gene markers. As the D-Wave systems scale, and genetic information becomes more ubiquitous, this type of data mining and learning is certainly an area that looks very promising for quantum computing!

  3. Pingback: D-Wave’s latest Quantum computer paper has been published in Nature and uses freezeframe snapshots to prove quantum mechanical behavior - forex world | forex world

  4. I’ve always felt the same way about diversional analysis being supplicated to, unimagined as of yet
    parameters, scaleable in preassignable models built
    in largepart on the fields in the standard model of
    quantum mechanics. Keep up the good work and stay away from govt. funding.

    Respectfully, Al Eggstien

  5. I am really happy for you guys in terms of the feedback among people valuing the work you are doing. As a developer, it’s reassuring to see the scientific validity of your platform. Can’t wait for the API release date.

  6. Pingback: Inaudito, D-Wave Systems logra publicar un artículo en Nature « Francis (th)E mule Science's News

  7. Pingback: Shtetl-Optimized » Blog Archive » Quantum-Effect-Demonstrating Beef

  8. Congratulations on Quantum annealing with manufactured spins … it’s a terrifically interesting article.

    Among the many important questions this article encourages us to think about is (with apologies to Tolstoy) “How much state-space does a computer need?”

    That computations benefit from more-than-classical state-spaces is known; that realizing these benefits requires full access to Hilbert space is not known … and perhaps even is not the case (as the performance of DWAVE’s technology suggests).

    At last week’s Perimeter Institute conference Conceptual Foundations and Foils for Quantum Information Processing one of the speakers gave a talk “The Territory Around BQP: Results and Open Problems” that reviewed considerable material relevant to the question “How much state-space does a computer need?”

    In particular, beginning about minute 09:27 of the talk (video here) there’s a discussion of “Separable Mixed State Models” that (AFAICT) leaves the QIT gate theoretically open for the DWAVE device to exhibit more-than-classical computational capabilities.

    In the event that Scott Aaronson ever chose to debate *this* guy on the potentialities of DWAVE-type more-than-classical computational technologies … well … it’s evident that the winner of this quantum debate (IMHO) would be none other than … Scott Aaronson! :)

  9. I have a question about the nuts and bolts of your processor development, but first, an introduction:

    When you thermally anneal materials(ie: classical), all kinds of strange things can happen because of non-idealities: point defects, interdiffusion, interfaces, phase transitions, precipitation, etc. The list is really endless. It seems like the more ‘perfect’ you try and make the material, the more you issues you uncover, and unfortunately, they tend to become harder to understand.
    Yet, empirical research motivates a model for these deviations, and it becomes the road map for further improvement. Ideal from a scientific perspective(our model fits the data exactly!) rarely coincides with the application perspective (is it harder, better, faster, stronger?)

    ANYWAYS, I am probably preaching to the choir, but what I would like to know is how much effort have you (and will you) have to expend on understanding the non-idealities of large scale quantum annealing? Have you uncovered issues similar to point defects, precipitation, etc? Flux pinning and device variation seem like the obvious culprits to me. A simple experiment I can think of is mapping an identical problem onto the chip using different physical qubits and ensuring the solution is identical.

    In materials science, not achieving the theoretical limit doesn’t mean that you made something that is useless. In fact, I think that making something useful for society only accelerates the scientific development(funds! funds! funds!).

    • Nb Chef,

      You are right!

      As the saying goes, “In theory, theory and practice are the same. In practice, they are not.”

      You were absolutely correct to guess that “flux pinning and device variation” are our major headaches, and this is why each of our qubits have a handful of extra control “knobs” (in addition to ones one needs to set h’s and J’s of the actual problem) attached to it, and they can be programmed to deal with the flux offsets and natural parametric variations. We also need to measure all the relevant parameters (“calibrate the chip”) and set those knobs accordingly to achieve the results that we are getting.

      When you read that we have 128 qubits on the chip, and only 55 to 95 were used in, say, Google’s demo (http://googleresearch.blogspot.com/2009/12/machine-learning-with-quantum.html, what was the exact final count, again?), or some other application, keep in mind that those were the ones balanced enough to be useful for that particular application!

      And it is my job to ensure that all of the present qubits are useful, but I will take the fact that this architecture allows for “graceful degradation” for now…

      Sincerely,

      Paul B.

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