Building quantum applications with D-Wave's Leap
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00:00
BuildingQuantum computerJames Waddell Alexander IISource codeFeedbackSlide ruleQuantum computerCartesian coordinate systemPhysical systemOpen sourceData managementFeedbackSoftware bugComputing platformProduct (business)Revision controlSoftware developerMeeting/InterviewComputer animation
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SpacetimeFood energyMultiplication signDemo (music)Computing platformQuicksortQuantum computerPhysical systemSpacetimeSocial classQuadratic equationMereologyComputerHydraulic jumpClassical physicsBinary codeCorrespondence (mathematics)Food energyHill differential equationComputer animation
02:41
Computer networkVertex (graph theory)VortexRepresentation (politics)Workstation <Musikinstrument>BitJunction (traffic)Instance (computer science)SoftwareComputer configurationSlide ruleQuantum computerOpen sourceSet (mathematics)Maxima and minimaNumberPoint (geometry)Extension (kinesiology)QuicksortLibrary (computing)Graph (mathematics)CodeVertex coverUniform resource locatorQuadratic equationObject (grammar)Physical systemLength of stayRight angleComputer animation
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SoftwareSuite (music)Repository (publishing)Source codeBefehlsprozessorGraphics processing unitConstraint (mathematics)Mathematical optimizationRoyal NavyWaveQuadrilateralData modelStack (abstract data type)Set (mathematics)Quantum computerGreatest elementGraphics processing unitSoftwareKeyboard shortcutOperating systemBitComputerBefehlsprozessorQuicksortQuantum information scienceSoftware developerDifferent (Kate Ryan album)Social classAlgorithmVulnerability (computing)Level (video gaming)Execution unitCoprocessorMoment (mathematics)AdditionAbstractionQuadratic equation1 (number)Cartesian coordinate systemSlide ruleText editorMappingUniformer RaumComputer animation
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Physical systemComputer networkBinary fileData modelQuadratic equationVariable (mathematics)Junction (traffic)Pairwise comparisonMathematical optimizationMaxima and minimaHill differential equationCybersexInformation securitySoftwareQuantum computerLinear programmingVertex coverQuicksortComputerObject (grammar)Variety (linguistics)Set (mathematics)Transformation (genetics)CoprocessorState of matterCASE <Informatik>NP-hardBinary codeVariable (mathematics)Vector spaceMedical imagingMathematical optimizationWaveNumberDifferent (Kate Ryan album)Vertex (graph theory)AbstractionScripting languagePoint (geometry)Slide ruleType theoryFormal grammarHydraulic jumpNichtlineares GleichungssystemPhysical systemCartesian coordinate systemPairwise comparisonJunction (traffic)Computer programmingDifferential equationInteractive televisionDataflowSatelliteFunctional (mathematics)ProteinGraph (mathematics)Pattern recognitionQuadratic equationMappingReduction of orderUniformer Raum1 (number)Einbettung <Mathematik>Term (mathematics)MereologyQubitProof theoryFile formatClassical physicsComputer animation
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Physical systemQuadratic equationBitQuantum computerBinary codeMachine codeSequenceQuicksortRight angleCuboidThumbnailSlide ruleBlack boxKälteerzeugungHydraulic jumpPhysical systemServer (computing)Computer animation
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Physical systemPerfect groupQuicksortQuantum information scienceExecution unitThumbnailQuantum computerComputer animation
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Quantum computerProcess (computing)Execution unitVirtual machineBinary fileData modelQuadratic equationRule of inferenceComputer hardwareSlide ruleQuadratic equationMachine codeInteractive televisionMereologyIsing-ModellIterationFood energyPhysical systemVariable (mathematics)SummierbarkeitQuicksortRight angleLimit (category theory)CoprocessorImage resolutionRange (statistics)Computer hardwareVirtual machineRule of inferenceNichtlineares GleichungssystemQuantum computerSimilarity (geometry)Field (computer science)Hamiltonian (quantum mechanics)Computer programmingPhysicalismMathematicsComputer animation
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QubitGraph (mathematics)Acoustic couplerQubitExecution unitWhiteboardCoprocessorSlide ruleBitShape (magazine)TesselationImage resolutionAcoustic couplerTouchscreenQuicksortQuantum information scienceQuadratic equationConnected spaceGraph (mathematics)Square numberCorrespondence (mathematics)Zoom lensQuantum computerRight angleComputer animation
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AlgorithmHybrid computerHelmholtz decompositionSoftware frameworkBuildingQuantum computerClassical physicsIndependence (probability theory)MereologySample (statistics)Set (mathematics)Food energyBefehlsprozessorDuality (mathematics)Process (computing)GoogolComputer-generated imageryDemo (music)Tabu searchQuantum computerHybrid computerSoftware developerMultiplication signBitAlgorithmQuicksortPoint (geometry)Line (geometry)CodeLoop (music)Type theoryDifferent (Kate Ryan album)Branch (computer science)Physical systemRevision controlSoftware frameworkHelmholtz decompositionWaveComputerRight angleVirtual machineVulnerability (computing)Task (computing)2 (number)Slide ruleSampling (statistics)Meta elementInstance (computer science)MereologyNumberQubitFerry CorstenGreatest elementComputer animation
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Quantum computerMultiplication signAlgorithmComputer simulationComputer programmingFreewareComputing platformReal numberHybrid computerData centerPoint (geometry)ComputerEnergy conversion efficiencyResultantFehlererkennungQuicksortInternet service providerCASE <Informatik>VirtualizationComputer scienceEndliche ModelltheorieType theoryPhysical lawCoprocessorArithmetic meanClassical physicsMusical ensembleSpacetimeService (economics)Information securityLevel (video gaming)CryptographyData miningLimit (category theory)PhysicalismTheoryMereologyQubitSatelliteNormal (geometry)KälteerzeugungRight angleDivisorCartesian coordinate systemCalculationPower (physics)Fraction (mathematics)Simulated annealingPhysical systemUniverse (mathematics)Logic gateOvalGodPoint cloudBit2 (number)VacuumWaveSimilarity (geometry)Moore's lawSet (mathematics)Meeting/Interview
Transcript: English(auto-generated)
00:06
Okay, so this is the 15 minute delayed and with an excellent improv intro To building quantum applications with d-waves leap. So if we go to the next slide So
00:21
I'm gonna skip over my intro But the really quick version is that I am the manager of applications development technologies and tools at d-wave So one of our mandates is writing the open source tools in Python that people use to access the d-wave system And to solve practical problems with our quantum computing products
00:41
so I also want to just say that you know, we're really invested in the Python open source ecosystem and Because of that pretty much everything I'm going to be talking about today is available in open source Stuff is available online and in our elite platform and we really really want feedback from you all So things like github issues bug reports pull requests That's all super valuable to us because it helps us prioritize because we really want to be
01:04
Make this accessible to you all next slide Normally I would do a quick demo with our online platform But I think I'm going to cut that in the interest of time so let's go on to the next one and instead and one more and
01:21
Talk about solving problems with binary quadratic models Which is the problem class that we at d-wave are interested in solving with our system. So I think that the The metaphor that we often use when we're talking about solving problems with our system is the so-called landscape metaphor which is
01:40
imagine that you are on a landscape and this this sort of height of the various parts of the landscape the hills and the valleys are defined by or in some sense represent the quality of your solution and The problem that we're trying to solve is to find the lowest Space on the landscape
02:01
And that one corresponds to the best quality solution So it's a metaphor that's useful. We tend to talk about like there is a large Energy Ridge between two solutions, which means you have to walk like way uphill except much much worse solutions as you as you sort of journey from one solution to another using a classical algorithm and
02:24
and the value that quantum computing brings is its ability to essentially I'm gonna use a metaphor here because this will get upset with me tunnel through these energy barriers to To sort of jump into other valleys in a way that a classical computer can't can't do
02:40
Next slide, please So to make this a little bit more concrete. I want to talk about a specific example that Helps sort of demonstrate how we think about solving problems with these binary quadratic models And it's a really good representation of how we solve problems on the quantum computer So imagine that I have a network of pipelines
03:01
You can sort of see it on the right-hand side there and these pipelines intersect at various junctions. So You can see that, you know the pipe between one and two and the pipe between two and three, you know They intersect at a junction two What we would like to do is to find a minimum set of junctions From which we can monitor every pipe in a segment. So if we jump to the next slide, please
03:24
So you can see here on the right-hand side an example of this this so-called minimum cover Which is that from each of those different locations? We can every single pipe is adjacent to at least one of those red Junctions and it's one thing it's important to note that there's also some some other options here
03:43
In fact, we could for instance have a monitoring station at node one instead of node two. That's fine But the point is to find some minimum set the smallest number of locations from which we can monitor every junction It's also really and and you know You could sort of probably work this pipeline problem out on a piece of paper without too much issue
04:03
Although it's it's not trivial trivial But you know as you can sort of imagine that as this gets bigger this problem becomes increasingly difficult next slide, please But so here's what solving that problem with the ocean tools looks like so everyone here
04:20
I think should be I normally would say does everyone read Python, but I think here we can probably rest assured that people do So here is the Python code using our packages that solves this problem So at the top you can see we're importing Network X which some of you might be familiar with it's a it's a very popular graph library open source library in Python that Was developed by some folks out of Los Alamos National Labs, but is pretty widely used
04:46
and you can see right away that we're leveraging sort of Python's open source ecosystem because we have Implemented our own sort of extension to that package called D-Wave Network X which which extends some of the notions in that one And you can also see that we're importing the sort of some objects from our D-Wave system package
05:02
That lets us access the quantum computer Let's go to the next slide So before I I'm going to actually jump back to that in a moment, but I want to Talk a little bit about how we organize our software stack. So at the very bottom we have a set of compute resources So those are things like CPUs and GPUs just like normal
05:22
But what's new and different when you when you start talking about quantum computing is the addition of this quantum processing unit and I really want to dwell on this for a moment because It's it's it's important to think of this quantum processing unit as another coprocessor It's like a GPU and that you you're never going to use quantum QP used to sort of
05:43
You know One of the questions that keeps getting asked is can I play crisis on? Your quantum computer and the answer is no because you wouldn't necessarily you wouldn't have an operating system on a quantum computer You wouldn't use it for a text editing. You wouldn't use it for You know watching movies you might use it the way that use a GPU to accelerate some aspects of that
06:02
So you might be doing rendering and use the quantum computer to accelerate that but it's not in and of itself sort of a full-fledged Computer that can run, you know an operating system and a GUI and run a keyboard and all that fun stuff So starting at the bottom you have these CPUs and GPUs and and the QP use Sitting above that you have a set of samplers
06:22
So these are where this is the level that these binary quadratic models are solved at So this is sort of a set of algorithms slash packages that people can use to solve that problem class I was describing and then above that being a software developer and having a team of software developers We wanted a uniform abstraction level For for these different solvers because they all have sort of different needs and they're all have strengths and weaknesses
06:46
Finally sitting above that we have a bunch of mapping methods One of the ones I touched on was this network X package and then above that we have our applications So let's go to the next slide So so going, you know
07:00
We can now go back to that little ocean script that I described before and here you can actually see those abstraction layers in practice So at the top there, we have the sampler equals embedding composite D wave sampler So the D wave sampler is our access to the quantum processor It's the sort of it's the object that encodes the you know, the network calls and all of that to access the CPU
07:22
Around that we have this embedding composite. That's what gives us our uniform sampler API That's what allows us to solve sort of in some sense arbitrary problems on it And then we have our we use network X to specify a graph this is the same graph that I you know had in the picture before and then we use the Problem mapping layer this min vertex cover function where we pass in the graph and the sampler and it uses the sampler to solve
07:46
This problem so you can see that sort of abstraction layers in just one relatively short Python program if we could jump to the next slide So I'm gonna not dwell too long on this But I just want to sort of give you the mathematical formalism that underpins the problem that we're actually trying to solve here. So
08:08
Really the way that you should think about using our quantum computer and using classical solvers that solve the same type of problem is It is trying to find a vector V that minimizes this Equation and the things I want you to note about this equation is number one that this vector V is over binary variables
08:26
So, you know, yes, no Or negative 1 1 or 0 1 binary. I just mean to state And the reason that this is the case is because our quantum computer has a set of qubits and these qubits ultimately end their Computation in a classical state. That's why we can read them
08:42
They you know when you read them they collapse into a classical state and so Ultimately our solutions are classical even though our computation is quantum The other thing that I want to note is that sort of first term in the equation, which is this VI VGA AIJ This is the quadratic part of the problem. This is what makes these problems difficult
09:02
This is that sort of second-order interaction between the different antenna or the different pipes and the different networks Vertices in my pipe network. That's what makes the problem difficult It's not just pick the cheapest set of vertices It's picked the cheapest set of vertices subject to the requirement that each pipe have a sensor. Next slide, please
09:22
So going back to our our pipe problem You can sort of see that why this is this binary quadratic problem So first off we have binary variables should junction one have a sensor or no at the binary variable We have pairwise interactions Each pipe must have a sensor
09:41
That's a sort of quadratic interaction and interaction between two different variables. And then we have a linear optimization I want to do it as cheaply as possible So this is this this pipeline problem was picked specifically because it is exactly already natively a binary quadratic Model, so let's jump on to the next slide, please
10:01
So I want to point out that these binary quadratic models are an NP hard problem Which I think a lot of you are familiar with what that means But just to belabor the point that means that if you can solve that problem faster than other NP hard than other NP hard problems Then there's a fast transformation from those other hard problems into yours And so what that means is that we are able to affect a huge variety of problems
10:23
Even ones that aren't sort of natively binary quadratic models. So we take a problem we reformulated at the binary quadratic model We solve it on the quantum computer. We take it back out. We undo that that reduction In some examples of things that we've looked at is formation of terrorist networks, we've looked at traffic flow optimization
10:40
We looked at satellite placement. We've looked at protein design. We've done image recognition just, you know, 200 or more Proof-of-concept and applications that have been run on the D-Wave system if we can go to the next slide so I've talked a little bit about the binary quadratic models, but that's not quite a quantum machine instruction
11:02
You have to get from this binary quadratic model into something that the quantum computer can understand. So next slide, please so here is the sort of You know the pretty pictures of our of our the inside of our quantum computer And so, you know if you walked into our lab in Burnaby and outside of Vancouver in Canada, you would see our
11:22
Systems look like these big black boxes just like you see on the left. This box is about the size of a you know midsize bathroom The you can see the sort of server racks there on the front those are you know a little bit shorter than a person And then inside the box there is a sequence of refrigerators that are designed to keep the processor as
11:43
isolated and cold as possible so Starting at sort of outside the box obviously is room temperature inside the box. It's like a refrigerator Inside that first canister you're at 50 Kelvin then 4 Kelvin then 1 Kelvin then 100 millikelvin and then 15 millikelvin So for reference, this is a colder than interstellar space. It's it's unbelievably cold
12:05
And this is all to keep a little chip That's about the size of your thumbnail which you can see in that right hand picture. Actually, let's jump to the next slide a Little chip that is about the size of your thumbnail cold and that's where the quantum that's the quantum processing unit this QPU and you can see a picture there that's even zoomed in on that last picture where that that sort of
12:25
Chip holder is is bigger than this picture. So that chip is really quite small. Yeah, perfect Thanks, you can see that it's being covered there by that little gold plate If you remove the gold plate and you look at the next slide, you can see that's that's where the chip is so Jumping to the next slide
12:42
So again, this is a fairly math heavy and now physics heavy equation This is the equation that defines behavior of our quantum computer This is called an Ising Hamiltonian and if you look over on the right hand side You see an equation that looks an awful lot like that binary quadratic model
13:02
I was describing you can see that there is a sum over a bunch of very binary variables and then on the right hand side You see that there is a sum over quadratic iteration interaction to those binary variables We call this part of the Ising Hamiltonian the quantum machine Instruction and this is the part that you are programming when you're programming the quantum computer
13:23
next slide and Next slide. This is just the binary quadratic model which shows you the similarity and so there's a reasonable question Which is you know, how do you get a binary quadratic model into a quantum machine instruction? Because you know Those are not quite the same thing and and the answer is is that a quantum machine instruction is a binary quadratic model plus some rules
13:45
Those rules are first off your variables must be spin So that's negative 1 1 before I said they could be binary they could be anything now They must be either negative 1 or 1 and the reason for that is that our system is essentially running on little magnetic fields And so, you know we use you know, your magnetic field is either basically pointing down or up
14:02
Corresponding to spin values. We also have an energy range associated with our variables And and there's a sort of limited Resolution that our processor has and so that can be a problem and finally you have to be hardware Structured which is to say you must be shaped like the quantum computer. Let's go to the next slide
14:24
So if you zoom in even further on that chip picture You can maybe make out depending on the resolution of your screen You see this kind of a checkered board shape on the chip those checkered board shapes each square in that checker checker board corresponds to 8 qubits on our Quantum processing unit and you can see on the left the picture of the connectivity of our graph
14:44
So each of those 8 qubit tiles is connected to the tiles to the sort of north south east and west of it And so each of these qubits is connected to six other qubits next slide Now that's true on our current 2000q processor
15:00
But in the fall we're coming out with our advantage processor Which has quite a bit more connectivity and has a lot more qubits So on the right hand side, you can see that it has a 5000 qubit processor With 40,000 couplers and on the left hand side is our current technology with 2000 qubits and 6000 couplers. So ultimately When you're getting your binary quadratic model You need to shape it like one of these
15:21
One of these processors and how you actually do that is a little bit beyond the scope of this talk But suffice to say we have tricks to do that. Let's advance two slides, please The next one, please Okay. So before we move on to time for questions, I want to talk a little bit about hybrid algorithm development
15:43
So hybrid algorithms are combining the best of classical and quantum computing. So next slide, please So we have in Python we have our D wave hybrid framework, which is a hybrid asynchronous decomposition sampler framework It uses Python code to generate new
16:02
hybrid algorithms in a very sort of plug-and-play kind of way so you can create Brand new never-before-seen algorithms with just a couple lines of Python and I'm going to be talking a little bit about Different types of hybrid algorithms and in them I'm going to have a little snippet of Python code to sort of show you what that looks like. So next slide, please
16:22
So the motivation for this package is that Normally, these algorithms are quite complicated to specify So on the left hand side, you can see sort of the pseudocode for a particular Hybrid algorithm called Cubysol and then on the middle You can see that this takes you many many many lines of C and C++ code And then on the right hand side you can see an example that that sort of loop racing branches that is actual Python code
16:46
That is used to implement this algorithm with only, you know, six lines of Python and on the bottom You can sort of see how this algorithm is organized The basic idea is that you're running a problem both on a classical sampler on your local system While the problem is being run remotely on the quantum computer in Burnaby, you then take this the problem
17:05
You determine which had the better solution and then you decide whether you want to keep going And you can either go back around the loop again or exit. So next slide, please So I want to talk about a couple different types of hybrid algorithms because I think it's useful to Have a sense of what one could do with hybrid
17:22
So the first one is decomposition and this is obviously most often what people think about when they think about hybrids So the idea here is I want to grab a sub problem from the quantum computer and I want to use it Sorry from the larger problem and then I want to solve that sub problem on the quantum computer because quantum computers have at this point
17:42
A relatively small number of qubits and not all problems can fit directly on it. Next slide, please The next type is pre-processing So the idea here is I'm going to use a classical algorithm to pre-process my problem before submitting it to the quantum computer Next slide, please The next one is post-processing
18:01
So for instance, I want to use I want to take the quantum computer to see the classical algorithm this is actually a very good thing to do because the quantum computer is Very good at finding pretty good samples quickly Whereas classical computers can be are very good at finding very good samples slowly So if you can accelerate that by preceding the classical algorithm with the quantum computer, you can often get a lot of benefit
18:25
Next slide, please The next line is the so-called meta algorithm So the idea here is I am going to take a purely classical Algorithm and accelerate one part of it with a quantum computer So for instance if I'm trying to build a strong classifier out of a collection of weak classifiers
18:43
Which is a typical machine learning task I can use the quantum computer to help select my collection of weak classifiers to use to build that strong classifier next slide and Then the last one is it's not really a hybrid technique, but it is a useful technique which is
19:02
So-called racing. So the idea is is that the quantum computer because it lives remotely in a lab in Burnaby You know when you've submitted a problem to it It has to you know if you're submitting it from Europe say it might take a second to sort of make its way across the internet and get to the get to the lab in Burnaby and then it solves the problem on the burnaby in a couple microseconds and then it comes back over the wire
19:21
Over a second and so, you know System is sitting idle for that two seconds and there's probably a lot of computation that could be done in that time And so by using racing you can basically not you can use that time to do other useful things to solve your problem So anyways, that's the slightly rushed version of my talk. I wanted to open it up to questions
19:42
Although I'm not 100% sure how the questions are gonna get passed to me given our slightly messed up setup. But yeah This is actually going to be quite easy because I'm just gonna read them out So Okay, thank you for the talk. That was that was nice. Very intense. I
20:01
didn't I didn't really quite understand all that but You know just in theory the the top high-level things. I think I I do know So the first question is how far are we from commercial quantum computing as a service? So qcaas Yeah, I mean so we have I mean quantum computers are available in the cloud today
20:23
That's both d-wave and and other providers like IBM's gets get so in some sense and you know, they're all purchasable with With time so in the commercial sense, we've already we already have that I think implicit to your question is When are quantum computers going to be?
20:41
Valuable to customers. What are we gonna start seeing advantage with quantum computers? And you know, the answer I will give is that We are already seeing Commercial advantage using hybrid systems, so that's combining quantum and classical. So we're already starting to see cases where we're able to provide advantage to to customers your value now if the customer say was to you know
21:04
Hire a team of computer scientists and give them a year Could they come up with a classical algorithm that would would you know be able to replace the quantum computer almost certainly? This isn't sort of a scientific result, but it is nonetheless Promising that we've reached a point of Sophistication with quantum computers that we are able to affect real-world problems and and use them in real-world commercial applications
21:27
Do you think we will see Moore's law in in quantum computing? Yeah, yeah, I mean Just just to sort of give a picture The did quantum computer as I said has to pass through all these different refrigerators, right?
21:43
When we're programming the quantum computer you're you're sending signals down to that processor that's being kept at 15 millikelvin and those signals are Oh, sorry, I'm actually talking about the ends of more law first off We have already doubled we double our quantum computer about every two years
22:01
But I think that we're also going to run into similar limits that the classical computers are running into which is that there's a fundamental Limit which is that eventually, you know, we're gonna be able to keep doubling for quite a while But eventually we're gonna start running into you know physics limits Which is you can't you know program? One billion qubits at 15 millikelvin because the amount of heat you need to send down just to send those signals
22:21
So I both think that for a while we're gonna get that sort of end-over-end doubling every every so every couple years But I don't think it's gonna last forever just like classical computers and then we'll be talking about a new type of computer That no one has thought of yet, but I'm okay Excellent, so next question are there any resources where hobbyists can play around with programming on virtualized quantum computers and algorithms?
22:45
Oh, I can do better than that. You can play on a real quantum computer for free So if you go to our leap platform, you'll get one minute of free time on the quantum computer I know that other vendors in the space have similar programs so it's not just the wave that you can do that with and then ocean itself comes with a collection of
23:02
Simulators and classical algorithms that kind of emulate the quantum computer that you can use to play with them if you like But a minute of quantum computing time doesn't sound like a month a lot But since each problem only takes about five microseconds or 20 microseconds, it's actually quite a bit All right, nice So next question, is it possible to sort faster than n times log n with quantum computers?
23:26
Is it to sort? To sort faster than n log n. Well the Yeah, I Guess you need some more Q. But you what a QP use you call them, right? Yeah. I mean, so there's there's
23:42
Oh my god, I'm I'm choking on the name, but the very famous quantum computing sorting algorithm that's not occurring to me right now but And that's a that's a gate model algorithm that none of the quantum computers that exist today can run really because it requires error Correction or a universal quantum annealer and neither of those are available
24:01
Okay So next one. Can you mine cryptocurrencies using quantum computing? Yes, and it's something that we've been asked about before I would say that we have not yet reached I'm not using it to mine cryptocurrency. So I don't think we're quite at a point where it's it'll Save you money on the other hand
24:21
Quantum computers by their very nature because they're kept so cold You Can't put a lot of power down there I mean when I said before like oh you're sending a lot of heat I'm still talking about just tiny fractions of a lot And so so they are a very energy efficient way to do calculations. And so I do think that
24:40
If cryptocurrency sticks around and all that that that quantum computing will be quite useful for that just for that reason You know having to keep them cold and having to keep your data centers Powered is becoming quite a limiting factor Right, I have another question because there are no more questions in the Q&A Would it make sense to you know, build a quantum computing data center?
25:04
Let's call it on the moon because then you don't have to you have to you know, the the the heat problem You know kind of it doesn't go away, but it gets more manageable Yeah, it's a great question and we get asked it some fairly often, you know in terms of like Could you put it in a satellite? So
25:21
The problem is is that getting it to To Kelvin which is about the temperature of interstellar space and sort of the dark side of the moon It's actually the relatively easy part of the cooling. It's getting it down to 15 millikelvin That's the hard part. And so and on the moon there's less It's harder to dissipate heat
25:41
Than it is on earth. So it's actually easier to have it here on earth than it is on the moon. Okay. Yeah, it's a great question. I mean, yeah Well, I was just you know thinking maybe you know, you can do that what you said about the cryptocurrencies though I mean if if the quantum computers actually get to the point where you can do crypto mining using quantum computers
26:01
I think the the crypto currencies are basically going to collapse right because they will need a completely new trip crypto to Still be going to still fulfill their purpose, right? So yeah, I actually it's a question I don't I'm sure somebody is thinking about quantum secure cryptocurrency I don't I'm someone must really yeah, but I don't know much about it if it does exist, but I no doubt someone here is
26:27
Okay, let me see Okay, now they're discussing there Is it another question here is it kept it I suppose is the computer is it kept in a void to be cooled down
26:45
Yes, it's also in a vacuum. That's right and and you know that that That set of canisters is also to maintain that it's also kept in a faraday cage obviously to limit electromagnetic interference And I can also tell you that we have we are able to detect earthquakes with our quantum computer
27:04
Because it has to be kept very vibration free Do you have to put in any protection against cosmic rays? Uh, i'm not actually sure it's a good question probably but I don't know Okay Good let me see whether we have more questions. Otherwise, I think we are done
27:24
No, don't see anything All right. All right. Thank you everyone. Sorry for the rush talk Thank you very much. Let me give you your applause. So
27:40
Very nice