Good morning guys welcome back to our Guinness World Record attempt to become the youngest pair to circumnavigate the globe by motorcycle Good morning bumblebee We're here at a campsite in the small village of Cossandere in the mountains of Turkey It doesn't really feel like the Turkey I had in my head. It's like gray and wet and green and green But I think that's gonna change because today we're heading south so let me show you guys the route for today So we are here and today we're heading south because our new mission here in Turkey is to reach our second point of interest Mount Nemrut all the way in the south So today we're gonna be taking this road south Towards the town of Erzincan and then this road to see how far we can get towards El Aziz We won't reach it today, but we try to get as far south as we can It's already 7.30 and we still have to pick up everything so better hit the road let's go We have still some oil dripping here from our suspension and we watched a video recently that you can actually Create your own little tool to clean it out. So this is what we did here Yeah, okay cool So now I'm trying to clean out the dust and the dirt definitely some dirt coming out Hopefully that will be good enough I actually just tried to plug in my phone here at the back It used to work all the time, but we had a lot of rain at one point and we forgot to close it I think it's totally broken now. So we're gonna check our tire pressure as well this morning Just to make sure it's in a good place. What is it normally on? So the front we're looking for 38 psi and we are at 35 and a half So we'll just give it a little bit more There you go. Easy as that. Ready? Take only photographs leave only footprints and a dry tent outline I just saw we didn't actually get the footage of the Cameras yesterday. So the one SD card is nearly full. I can see we have 13 minutes left on this SD card on this one Wow, yeah, you're right. We're gonna have to stop on the side of the road and like import this camera's footage. Okay Let's do it 354 miles to Mount Nemrut. Thank you so much. Thank you. Bye. Bye Google estimates eight hours and 41 minutes. So yeah, we'll see how far we can get today For us is probably more like 16 hours. I don't know why but we are so slow The day can pass like nothing, you know, and we haven't even gotten anywhere Well, like I always say Stopping is the whole point of moving around, isn't it? Yeah, but I think it's a couple thing as well We slow each other down.  Yeah, mine has a hole as well times in the night Yeah, our gear is pretty worn Yeah, it was our first time camping for ages. Yeah, I can't even remember the last time we camped Maybe in Russia. No, no, Kyrgyzstan So, yeah, we didn't camp since Kyrgyzstan our climate double mattress is just so so comfortable If you're a couple you have to invest in a double blow-up mattress for sure They are so so good and we camped for so long with two single mattresses And they're sliding around moving around and we both end up waking up in the morning in the middle with no mattress So this double mattress is just like oh was such a game-changer for our camping setup Yeah, just like the chairs the climate chairs. I love that. I love those chairs All right first fuel up in Turkey. It's time to find out the prices. How does it work? How does it work? How does it work? Do we do it ourselves? Does somebody help us? I have no idea. Everything's always so new. Oh, I think there's a fella coming. Somebody's coming, yeah Somebody's coming. Hello They have to write down the number plate apparently. It's interesting. So here it looks like they've got two diesels and one regular. Yes Perfect. And the regular is 95. Is that what it says? Yeah. There you go up to 95 octane So it's 39 lira per liter. Okay, that's quite a lot. 39 Yeah, one pound twenty one pound twenty per liter. So it's getting more expensive. Hey, yeah, that is more expensive for sure I mean in Kazakhstan we paid like 35 pence. Oh my god. I can't believe it's yeah, it's quadrupled since Kazakhstan Thank you so much perfect It's okay inside. Okay So I'm not 100% sure but I think I just read on the sign that this tunnel is 14 kilometers long How is that possible please? Is it actually going to be 14 kilometers long? I have no idea, but we just saw a sign that this road here is build 2023 So we are one of the first people actually riding it. It's crazy man If it is 14 kilometers long then this is going to be by far the longest tunnel we have ever ridden through The longest tunnel so far was that one in Columbia that was I think eight kilometers long Yeah, exactly and that was the longest tunnel in the Americas. Whoa. Look at this. This is so futuristic Okay, this is like a really modern tunnel really impressed good job Turkey 10 kilometers It was written up there. What still to go. I think so Here it is finally getting to the end whoa That actually was a 40 kilometer tunnel guys. We must have been so deep underground there. That's insane It's so insane So absolutely insane Well, we've reached the top of this pass 2,120 meters above sea level Whoa, look at this. Yeah, it looks crazy here. Wow That's a view.  That's great Whoa Okay, oh This is chicken. The chicken and this beef. Oh look at these ones.  Thank you Whoa What have you got there? It's like a whole kebab Rice salad and a giant bit of bread That looks so good. I think this is gonna be dinner as well as lunch for all German tourists Ausfahrt So now that we're completely stuffed after our luncheon Erzenkahn and back on the winding mountain roads It's time to tell you guys a little bit more about country number 30 on our round-the-world trip Turkey so Turkey is one of the world's earliest permanently settled regions and is home to important Neolithic sites like Gobekli Tepe and Mount Nemrut that we're going to be visiting tomorrow It's also been home to many important ancient civilizations like the Assyrians and the Persians and the Greeks but the modern history of Turkey has been dominated by the Ottoman Empire Which started around the 13th century and that lasted all the way up until the end of the First World War And it actually wasn't until 1923 that Turkey became a republic so the name Turkey first appeared in Western sources during the Crusades in the 11th century and Turkey just simply means land of the Turks and it's really interesting actually because the first Christian church in the world is located in Turkey in a place called Antakya and also Santa Claus St. Nicholas was actually born in Patara in Turkey in AD 300 but the modern-day population of Turkey is 85 million and Turkey is home to 19 UNESCO World Heritage sites And is the fourth most visited country in the world and I have one more interesting fact for you Turkey exports 75% of the world's hazelnuts Can you believe it? So there you go a few interesting facts about Turkey whilst we're climbing up and up and up Into this mountain region. We're just doing switchback after switchback after switchback Well, there you go views on the other side. Yeah, wow, Turkey is so beautiful It's so big as well and all over the place beautiful, you know You go from one mountain range to the next to the next to the next Well coming into a super crazy gorge here, look at this Awesome Wow, what a road look at this Absolute favorite road here. Yeah, this is one of the most spectacular roads. We've ridden in a long time Hello Who's your big mountain dog? Who's your big old mountain doggy? Whoa, look at the size of his face Hello Beautiful fresh mountain water. Oh, yes Hmm, it's cool is fresh beautiful Okay, whoa tunnel. It's got lights a Little windows here. Oh look, it's like more tunnels. Whoa, look at this. Oh, that is spectacular spectacular Wow Really crazy Wow 41,999.9 We just hit 42,000 miles on the road Just picking up some supplies Ekmek It's our first Turkish word we've learned Ekmek breads nice Thank you. Thank you. Can't go wrong with our dinner and breakfast Coming up to a giant reservoir now. We actually have to take a ferry to cross this reservoir here I think the reservoir is called Kebam Baraji and we read online that the ferry is actually quite quick just 15 minutes We will just go there now and check it out And see if we want to take the ferry today or perhaps tomorrow Because we saw an eye overlander as well that we might can wild camp here on the reservoir Which would be really cool. Yeah, that would actually be really really nice Hello Hi, hi, it's okay. Yeah, okay. Thank you. Yeah, okay Okay Hello Hello the ferry. What time? 27 minutes. How many times per day? 30 times And tomorrow What time the first? 6 6 a.m. Okay. Okay. Thank you. Thank you. What do you think? What should we do? I don't know. I Don't know now. It's 4 o'clock elastic elastic. Yes. Yes. Yes Yes, thank you. Thank you. Oh the ferry is probably just going like in a minute. I Think the ferry is just leaving actually They'd already cleared everything So we'll have a look and see where these camps are Okay, and then we'll decide whether we're gonna take the next ferry or if we're gonna stay on this side for tonight and have a camp This is an absolutely massive dog. Look how big this dog is Okay, so the camp on I overlander looks actually pretty good It's like just one kilometer on this little road running along the lake just here sounds actually really nice And this place is super beautiful So we decided that we're going to spend the night there and then take the ferry tomorrow morning Basically, I think we're just going this way Well, so just stunning area though. Hey look at this reservoir beautiful. Wow Oh Wow, that's pretty close there. Look at that. Yeah. Oh bash plate Okay, I think They're referring to here. I guess anywhere here Okay, not too bad. It's pretty good. Hey, so we can actually still see the ferry coming in just there Amazing view. Look at that. Yeah, I'm just cleaning off the oil It looks like that. It's still leaking and it doesn't look very good. You can see here as well a lot of oil so I think it's about three or four rides to Antalya and then we desperately have to Change the seal because if the oil is dripping on the brake pads It's not a good idea. So bumblebee was already due to get a service in Antalya anyway Plus possibly some new tires. So I guess we'll just have to add that to the list of things new fork seal We made it happy the life Yeah, this is a super awesome spot here We can just watch the ferries going to and fro and the landscape here and the water. It's just incredible Yeah at the moment very peaceful very quiet. I hope it will stay like this over the night So we will just chill out here for a little bit try to rest Pitch up the tent a little bit later and then tomorrow morning We will take the ferry over to the other side which we can see from here as well. It's not far It's not far actually like a 10-minute ferry. Yeah, super cool. And that's it from us today We hope you enjoyed the video if so, please give us a thumbs up subscribe to the channel share the video with your friends and family Comment below and we will see you next time So cool there's a castle over there on an island in the middle of the reservoir so cool


Lotus? No, Lotus is a symbol of my party. I must congratulate you, the way in which you have taken care of her, we feel proud. So, tell us your experience. So, we came on a Sunday and we stayed on Friday. So, which song do you like most? I like any Lochiva Phajans, Tamil Phajans also. I've been singing in over 12 Indian languages. Which are your most favourite? Sanskrit and Hindi. But also Indian languages. So, you are fond of Shiva? Yeah. Do you like Indian food? Yeah, I love it. What type of food do you enjoy? I just discovered the sweets. Yesterday, I saw your one small video, Home Number Shiva, I'm in India. Yes. You've been waiting for 6 years. Yeah. So, when first time I talked to her, he never told me that she wanted to come to India. Yeah, I was very shy. I'm still in. So, I never thought that this would happen. But, God is always with you. Yeah. And when he is doing a Phajans, God is always with you. Yes. People were very curious about you. Yeah.  Look at that YouTube.


a Bologna, sono tornata a Bologna. Mi stiamo spiede, come vedi dalla foto. Perché volevo discutere delle cose scientifiche, ma magari qui non ci possiamo vedere a Bologna? Sì, assolutamente. Ti dico, io ho sto gesso che lo terrò parecchio un altro... Io ti vengo anche a trovare volentieri, se voglio di lavorare. Vediamo, ti scrivo e ci metti a accordo, sì, sì, perfetto. Vengo il microfono da qui un attimo. Sì. So the first speaker is Laurento Campos Venuti from USC, who is online, unfortunately, I think he will tell us. Or at least the picture, I think it's very clear. So, I think, Laurento, you can start as you want, I start the recording. Grazie, Elisa. Thank you very much. Thanks to the organizer for the very kind invitation. It's a pity I cannot be there with you to enjoy this conference, but unfortunately, as you can see, I broke my leg. This is what happened when you do these things on the left. But it's nonetheless very nice to participate at least remotely. So I'm going to talk about adubatic tiering for open systems and the possible way to use it to gain some acceleration and speed up. And given the title of the conference seems to be a very fitting title. May be even too fitting and you will wonder that I'm going to tell you about things that you are already very much familiar with. Well, I can't assure you that you'll be falling asleep, but I'll do my best to keep you entertained. So without further ado, this is the outline of my presentation. I'll just review the adubatic tiering for open and closed system. And I'll present the ideal boundary cancellation and do some other generalization and then report on implementing these new methods on D-Wave device. So let's start and as promised, I'm going to start very simple with the simplest and probably also the most important differential equation of quantum mechanics. So this is a first order differential equation with the linear, right? With this operator L, it's a linear operator, I'm going to assume actually I'm always going to be in a finite dimensional space. So this is a bounded operator acting on some vector, which here I indicated raw, which could be, for example, the density matrix of our system. And this generator L depends on time and on some other timescale tau, which I indicated here. Well, you know, you can find the solution of this, at least formally, of this differential equation. The linearity allows us to write this solution in a compact form. The interesting thing is that the solution is unique and it's under a very general condition. For example, one simple condition is that this integral converges, the same is true under a more general condition, but we won't need it. So that's a simple differential equation, but which nonetheless encompasses most of the physics we deal with in quantum mechanics. Adiabatic expansion is about being interested in the solution of this differential equation when the, with this generator L, varies slowly. What do you mean with that? I mean, as it is, varying slowly, it's not a really precise sentence. E in fact, here I'm quoting Arnold, he warned us that without further assumption, the adiabatic invariance is wrong. So just to make it more precise, I'm going to assume the following. So I'm going to assume that this generator L depends on this external timescale tau in this form. So this tau is basically the total annealing time. And of course when tau is very large, this implies that this generator L somehow gives you intuition that this generator L varies slowly. So you've changed the variable from time to this rescale time and then you see that the tau parameter becomes just a parameter of this differential equation and comes out in this form here. So now the problem is well defined and we are so we want to find the solution of this differential equation. So what happened to the solution of this differential equation when tau goes to infinity? Or in other words, what happens when epsilon, which is tau to the minus one goes to zero. Mathematician code is kind of perturbation expansion singular perturbation because you see if I set epsilon exactly equal to zero, then the left hand side here is zero. So this is no longer a differential equation. Actually this is going to tell us something about the adiabatic limit. So these are a little bit complicated perturbation series to develop. In this form, as I said, the setting is completely general. So this could I could be talking about Schrodinger equation, for example, or I could be talking about, you know, L could be a genuine L'Uvillian and rho could be a genuine density matrix and your favorite Lindblad master equation. So actually, which of these two settings should we consider? Well, actually, because of the third law of the dynamic, we can never achieve really zero temperature. So whenever we deal with physical systems, we should really be talking about open systems. So, for example, with an embedded description or something else. In this case, you know, the instantaneous steady state, which I indicate here with sigma, is most likely, you know, the thermal state like a Gibbs state, which I wrote here, and the rule of the game now are about not preparing ground state, but about preparing Gibbs state, for example. We still can be hard, classically, so this is still an important task. And of course, if the temperature is smaller than the gap of the Hamiltonian, the Gibbs state is close to the ground state, so we recover basically the zero temperature limit. In this setting, you can also ask a meaningful question. Let's say you have a speed up for the closed systems case, right? Then you can ask yourself, well, what happens when I, let's say, open the system, when I let my system interact with the back, will the speed up survive? Well, this is a very complicated question also because when you deal with, when you talk about speed up, it means you have in mind a classical algorithm, right? And so you are comparing your quantum adiabatic preparation with some yet to be specified classical algorithm. And it's complicated because, you know, if it's value, you don't really know if you're comparing the apple with apple. Nonetheless, we gave an argument in this paper that if there is a speed up, zero temperature, the speed that may survive when the temperature is of the order of the Hamiltonian gap. And the argument is in this paper. This is not necessarily a no go theorem in the sense that, you know, actually in some cases being open, so interaction with the environment may even help. So you can actually achieve speed ups where there was no speed up before. So this is not necessary. In fact, I will give a few examples of this behavior in the following, namely behavior where the open system case, so to speak, behaves better than the closed system case, which is a bit counterintuitive. So, good. So we want to generalize now the adiabatic theorem for open system. So in this case, so L will be our general luvillian that describe our system after we traced out the bath, right? And I'm going to assume these are sometimes called ergodic or primitive luvillian. So basically I'm going to assume that the steady state of this luvillian is unique. And is this state sigma s. So these are instantaneous eigenstates of the luvillian, okay? So basically they're eigenvector with zero eigenvalue of this l at time s at this riscate time. Instead, rho s, I integrate with rho s, the state, which is evolved under the dynamics, starting from the steady state at time zero. So basically we prepare, so ideally we prepare our thermal state at time zero, then we turn on our adiabatic algorithm and we end up with rho s. But the state we want to prepare is sigma, okay? So how does the adiabatic theorem work? So we have to generalize it to the standard adiabatic theorem that we learn in quantum mechanics courses to this setting. And quite surprisingly when I was dealing with this problem, it was a few years ago actually in 2016, but still adiabatic quantum computing was already very well developed. But there was no generalization to open systems at that time. So what do you do? You go to your office and I actually, I studied all the paper by Cato mainly and I was able to generalize the proof to the open system case. And this is the result. So basically for some standard assumption here with standard assumption, I refer to some regularity assumption that assure you that the differential equation has a unique solution. These are super general. Then there is a little bit more of a technical assumption. So I'm assuming that this L generates a contraction syndrome. Mainly the norm of this object here is smaller than one. So this is more general than unitaries. And actually even more general than uvillian that generates a completely positive map. And on the same time, it's easier to check. So this basically it's technical, but it's a physical assumption that assure that our uvillian produces physical states. And then comes another physical, very physical assumption. Namely, if the uvillian has a gap. Now the gap is a complex parameter no longer since L is not a mission. But nonetheless we require that there is a gap above the zero eigenvalue for all S in zero one. And then you can prove the following namely the distance between the exact state you end up with. So this row one at the end of the evolution to the state that you want to prepare this instantaneous steady state at the end of the evolution. So this distance is bounded by some constant divided by this total near time. So if you make the total in time large then this you converge to the instantaneous steady state. And actually you can work out also explicitly what this constant and you can derive many interesting things. As I said, this setting is more general than CP maps. So includes also, for example, right field master equation, for example, which do not generate necessarily positive states. But are nonetheless useful in some situations and include as a special case, of course, the unitary case. So, okay, good. So that's the standard, let's say adiabatic theorem in this most standard form. Can we do better? So namely, the question arises, can we make this error smaller, right? Instead of decreasing less one over tau, can we make this error smaller? So it was actually known for a long time that in the closed unitary or Hamiltonian case, if you have, if you engineer the schedule in a particular way, you're able to make this, what I call this adiabatic error. So this distance between the exact state and the state you want to prepare. Well, you can make this adiabatic distance decrease as an inverse power of this tau parameter, of this annealing parameter, to the power k plus 1. And k is the number of vanishing derivative of the Hamiltonian at the beginning and at the end. It's a bit of a, well, I don't know if you have, well, there are ways to give an intuitive picture of this requirement. Anyway, basically you require that the Hamiltonian starts, or the scalar starts in a flat way, right? And end up also in a flat way. So at the beginning, all the derivative is up to some power k or zero, and at the end, all the derivative to some power k or zero. Of course, if this is the case, you can make the error smaller, right? And so this would be highly beneficial. So can we generalize this to the open system setting? Well, again, you go back to the office and try to generalize, and the answer is yes, indeed you can. And here actually there is an interesting difference, one of these differences that was mentioned before between the closed and the open system. So here actually to obtain the same result, so this improved accelerated scaling with tau with the annealing parameter, you only need that the Ljouvelian has vanishing derivative at the end. I mean, the reason for this are several reasons, most of which are technical, but ultimately basically the reason is that the Ljouvelian generates an error of time because of this non, yeah. And so this is what makes zero and one different. Of course, in the unitary case, we could have started from the end and end up at zero, and this would be a valid dynamical evolution. This is no longer a case here because the inverse of our generator wouldn't generate quantum state, wouldn't generate simply maps. So there is this, that's nice, okay, good. So maybe we can use this to achieve better convergence. And so you can ask, okay, this is true in general, but can I ever achieve this? So can I engineer a situation where I do have this vanishing derivative at the end of the Ljouvelian? So because when you deal with Ljouvelian, Ljouvelian has, and here I'm, for example, I'm mentioning a realistic Ljouvelian, for example, this is a very common Ljouvelian derived in Tamino, Bash and others in some time ago. It's also called adiabatic master equation. It's essentially, well, it's a Ljouvelian in time-dependent, time-dependent Davis 4, okay, where this A operator here are the system-bath operators, so they keep memory of the bath and the gamma also as the bath correlation function, essentially. So there is some information about the bath, which is hidden in this, well, not in this A operator here. So the question is, can we engineer such a Ljouvelian to have vanishing derivative at the end? Of course, we, without controlling the bath, right, because this is by definition something we cannot control. And the answer is yes, hopefully, so it's good. Essentially, it suffers to, if you engineer the system Hamiltonian, which is the Hamiltonian here, H of S, to have vanishing derivative at the end, this will imply that the Ljouvelian, the overall Ljouvelian has vanishing derivative at the end, and so, as the consequence, we will have this accelerated convergence to the steady state, to the state we want to prepare. So that's good. So then you can also ask if this result is solid, so now, because here I derive it for a particular Ljouvelian of this special form, and if you are familiar with master equation, you know that there are gazillions of different master equations. Basically, everybody has his own preferred master equation. So is the result somehow solid and doesn't, oh, here, of course, this tells us that, you know, it allows us to have the same accuracy with a smaller and in time. And as I said, the question is, is the result solid? Here I'm recalling just schematically the approximation, which I employed to derive the Limblad master equation. So small system bath coupling, you assume that the full density matrix factorizes, then you have the board mark of approximation. And after this step, basically, you end up with the Redfield master equation. You can also do this in the time-dependent setting, and you derive a sort of adgebatic Redfield master equation. And then if you apply a rotating wave approximation, you get the Limblad master equation, which is what I just showed before. And so the Redfield numerics on the adgebatic Redfield master equation show that actually the previous result, so this accelerated scaling of the adgebatic error, still works also for the Redfield. So that's good. And then, of course, I mean, the result always holds in a more general setting. If we assume unitary Hamiltonian evolution for the system plus the bath, and where the bath is unknown, and then we trace out. But in this case, we must assume that the system Hamiltonian has vanished in derivative also at the beginning, as I was showing before. By the way, just let me mention in passing that one of the benefit of using master equation is that first we can discard the bath to a large extent. And also, implicitly, we are assuming that the bath is infinite, which is something that actually we use many times, because if the bath is finite, then you have Poincaré recurrences and all sorts of somehow pathological behaviors. So it's good, at least mathematically, conceptually, to have the bath sent to infinity. But that's a somehow another, an aside remark. So that's a theorem, so you should trust it. But if you don't, you know, here are some mnemonics. So here, as I said, is this schedule, which is engineered to be linear at the end, or quadratic, or cubic, or and so on. So in such a way that it has K vanishing derivative only at the end. And well, these are simulation for the single cubic, but I also have simulation for several cubic, which I'm not showing here. And indeed, as you can see, the results matches very well the theory. Well, namely, because, I mean, you can also ask the theorem in itself is a bound, right? But here, we, this plot actually showed that the bound is basically captured the correct scaling for large and near time. And it also tells you that it is a sensible way to decrease the dramatic error, because, you know, if you set yourself at a certain total time, this changing schedule can allow you to move several order of magnets to have several order, maybe smaller adibatic error. So that's nice. Then you may want to, you wonder, okay, if this seems interesting, can we implement it on, for example, on a D-Wave device? And before going to the actual experiment, I'm going to recall a little bit of theory. So, as you all know probably, so the D-Wave machine prepares this system Hamiltonian, time-dependent, which is made up of this transverse field Hx, multiplied by this schedule function usually called A. A starts large and goes to zero, as time goes by. And then you have the longitudinal term Hz, which includes the, those grounds that is the solution, encode the solution of our computational problem multiplied by this schedule function B. And whereas B starts from zero, and then progressively increases as time increases. So this is what D-Wave machine does, essentially. And now, let's say we model, we try to get some insight to how the machine works. And we model the D-Wave with the adibatic master equation that I showed before. And if you talk to experimentalists long enough, well, you realize that the system bath coupling is predominantly longitudinal. So, meaning this A operator that I wrote before are basically Pauli, sigma, z. Okay? And then we immediately face a problem because at the end of the evolution, when the A parameter is zero, the system bath coupling commutes with the Hamiltonian. Because at the end of the evolution, the Hamiltonian is just along z, right? So the bath operator, the system bath operator commutes with the Hamiltonian at the end, and actually you can show that this implies that at the end of the evolution, the zero eigenvalue, so the steady state manifold is at least D for the general, where D is the dimension of the Hilbert space. So it's a huge degeneracy. So presumably before that point, the L'Uvillian is ergodic, so there is a unique steady state. So which means that the gap closes at the end, the L'Uvillian gap. So, this is actually bad news because we cannot use the adiabatic theorem that I showed before. Sorry, I'm seeing now a question. Yes, there is a question in the chat. I don't know if you want to keep it for the end or else now. Do you have two questions? Yes, yes, yes. So maybe I should read the question. I have two questions. Could an analogous relation on the trace norm between the state and the instantaneous thermal state be derived at any S? Yes, of course, there's not a special about one. Here I'm showing this adiabatic error for the last, for the S equal one point, but you can derive, of course, you can put yourself at any S, if you like. And the second question is, is the Markovianity assumption strictly required? It's a good question, not entirely, in the sense that as I showed some degree of Markovianity, it's maybe, I mean, we have to agree what you mean with Markovianity somehow. But as I mentioned before, so these results are valid for Redfield master equation, which are not less Markovian than the, yeah, sure, no memory Markovianity, no memory effect. Yeah, let's say they work when the generator is time local in this way. So if you have full memory effect, then the generator is, I take it back, actually, because the Markovian, sorry, the Redfield master equation has memory effect. So then yes, the answer is yes. So, okay. So as I said, here you see that the form of the noise of the system bus coupling, so now seems to be a bad news because, you know, we cannot use the, because we didn't get close and so we cannot use the standard divisive theorem. So what do you do? Oh, sorry, I think you, I moved it. Did you hear me? We hear you nicely, but. Okay, okay, I disappear for a second. Okay, so as I said, you cannot use the standard adiabatic theorem because the gap closes. So what do you do? You go back to your office again and try to generalize the results to the case where the gap closes. And first I consider the case where the gap closes at the end, at the end of the evolution.  So, namely, you make the schedule flat in such a way that the uvillian is vanishing very to the end. So they get close at the end and at the same point you enforce boundary cancellation there. And then you find out that the adiabatic error is now bounded by some constant over tau to some power eta. And now this power eta as this special form. Where alpha is the exponent that takes away the way the gap closes at s equal to 1, okay? And you can know that this eta as a function of k is always larger than 1 over alpha plus 1, which is the case without boundary cancellation with k is zero. And then, you know, if you make the schedule infinitely flat, so you have infinitely many derivatives, which are zero, the most you can get is this eta becomes 1 over alpha. So you see, this is somehow bad news, so not entirely unexpected. But it tells you that the boundary cancellation, in this case, is only mildly effective, it's not really powerful. And to be complete, actually, you would like to see what happens if the gap closes in the middle. And here, well, somehow I've been stuck on this problem for a long time and unfortunately I couldn't prove in full generality. But I have extensive numerics that show that actually, so now I assume the same thing. Standard assumption and so on. And now the L'Uvillian gap closes in the middle, but we perform boundary cancellation at the end. In this case, the adiabatic error scales with the inverse power of tau to the k plus 1. So this is the same as there was no gap closing at all, same for the gap case. This is actually very quite surprising because for the closed system case, the result is actually that the adiabatic error scales with the exponent, which is fraction, which is more than 1. And it is this 1 over alpha plus 1, basically. And all the benefits of boundary cancellation are gone. But somehow, for some, as I said, for some strange reason, in the open system case, you get this benefit. And here I have this one of the numerics that show this very well that this is the case. And, as I said, this is one of these examples where the open system case behaves better than the unitary case. I can almost prove it, but not in the full generality, which is I always must assume something more. So this is a little bit of work in progress. But anyway, actually, for D-Wave, we can never set ourselves in this setting. So this is just for completeness now. So there is another phenomenon, if you are familiar with the way you probably heard of it. And it's the fact that actually it's so-called freezing. And it's the fact that the dynamic essentially stops at freezing time, which is before the end of the year. E' even before the time where this A parameter goes to zero. And I can give an explanation of this phenomenon based on this adiabatic master equation, which I wrote before. If you set yourself in the energy again base, namely this N and M labels are instantaneous against state of the Hamiltonian. Then it is known that the diagonal element of the density matrix evolved with the Pauli master equation, which is now time dependent. Where this W rates, W at this form here for D-Wave, with the same assumption. And now, because of, again, because of the fact that the system bath operator sigma z, and they commute with the Hamiltonian at the end, you can show that the weights vanish at the end of the year. And in fact vanish with the large power. So this weights, which are the transition rates. So tell you how do you deplete level zero and go to level N or vice versa, jump from level N to level zero. Well, this transition rate vanish with the large power of A. So A to two to the Q, where Q is the hamming distance between the ground state zero and the excited state N. So essentially you see these terms here are all very small, even before A is zero. Because, as I said, this vanish with the power. In fact, we can basically define this freezing time to be the time where these rates are basically inverse of the, you know, typical timescale of the system, which we can set to be the total annihilata. And this also gives good results, this definition, with our numerical findings. In the paper that I'm going to cite later on, the explanation is a bit more complete, but basically this is the gist of the idea. So now, good. So now let's, let's, we have all the ingredients, so to speak, of the theory. So we, now we want to try to implement this boundary cancellation on D-WIF. So for, we have to avoid freezing, because after freezing the dynamic doesn't change. So it doesn't matter if you perform two boundary cancellations after freezing, because the system wouldn't see it. This is even true for, for, you know, for the matrix, you know. So basically you have to set yourself before, at a time where A is non zero, and it's before freezing, and then we engineer the schedule A and B in order to have approximately vanishing derivative at the end up toward the K. And this is achieved to some extent by the, some of the latest generation of D-WIF devices, the D-WIF 2000, which allows you to specify A and B to be piecewise continuous with the straight lines. So, and you have 11 points to engineer A and B. So if you have some limit, very limited engineering capability, but nonetheless you have some capability. And so basically we engineer the schedule with the capability that we have in order to be flat-tish somehow at this point where the A parameter is non zero. And finally we ramp to the final values of the, which are required by D-WIF after which we do measurement in the computational basis. So this is, for example, a physical schedule that we use. So this is a schedule that you see we stop at the value of A. This is the A schedule that doesn't go to zero. We stop here essentially. And this is for a value that approximately is quadratic. But of course, since this is, these are piecewise constant, it's not, the theorem is not satisfied exactly. But nonetheless, there is a result, which I haven't mentioned before, according to which, if you try to enforce boundary cancellation, but you only achieve it approximately, at least if your system is what you expect it to be, then the adiabatic error is still decreases. So the scaling maybe is not the one that you expect, but the adiabatic error is smaller. So there is theory that tells you that it's nonetheless good. So unfortunately, well, unfortunately, I mean, it's part of the beauty of physics that the horse is not really a sphere. So our D-WIF machine doesn't behave exactly as you would like it to be. Namely, there are several Hamiltonian programming error, also called integrated control errors. Namely, the Hamiltonian you are trying to, you end up not with the Hamiltonian that you're trying to program, and there are some noise on this. Then there are discretization error in the annealing schedule. There is a further phenomenon of anomalous heating, which is namely the fact that the temperature is actually not constant during the whole annealing procedure. Then there are marker of approximation in the, deriving the limblade and the form of the noise, which is unknown and so on and so forth. In particular, in all of this, basically, most of these error we have no control over, and we don't really know the extent to which they play and so on. The only thing we know is actually this discretization error in the annealing schedule. So the fact that we approximate a quadratic function with this piece, where is linear function with only 11 points. Actually, this numerically, we check numerically that this give rise to large errors. And here we show results, I don't know if you can see it, but basically here are the exponents that you obtain with this so-called, these are numerical results, but obtain with the d-wave piecewise linear beta schedule, which is implementing the boundary cancellation, but with only 11 points. And then you get this exponent 07, 1.49, 1.7. And if you use many more points, so this high precision, then you go from 1.5, let's say to 1.6, and from 1.7 to 2.6, or 2.7. So you see you, actually this discretization error, it shows that this discretization error are large. And finally, I can present the results in the paper where there's much more, but I don't have time to present. So here, basically you see the ground stage probability for different schedules, so schedule approximating the linear case, so basically they end up in a linear fashion, or so with k equals 0, or with k equals 1, or k equals 2. And the result are, you know, here the gap, at least from the theory, you, in principle, this, you read it now, it's gapped, so you would expect the exponent eta to be 1 plus k, and instead you get here, considering the smaller numbers. So, by the way, the top panels are just native standard, this is a problem of 8 qubits, a ferromagnetic chain, essentially, with different couplings, and they differ from where we employ the boundary cancellation, so basically the freezing is at a point where s is more or less 0.5, and we do see that if we approach the freezing time, boundary cancellation doesn't change, so here you see that these curves are basically all the same, so it doesn't really matter if you employ boundary cancellation because the system is already frozen. If you move a little bit from the freezing time, so you go a bit, you stop a bit before that, then you start to see that boundary cancellation become effective, and here you see that the best results are obtained for when s is roughly 0.45, and the lower panel are the same result, but now with quantum adiabatic correction, namely you substitute basically each qubit with 3 logical qubits, and you see that in this case you also see a good improvement on the exponent, and now the exponents are roughly half of the expected value, but I would say given all the sources of error that are there, including as I said the discretization error, in this case, let's play the big role, after all these results are not so bad, and in the hintside phase value actually they show you, since they are not consistent with the gapless form of this exponent that I showed before, basically these are proof that our rebellion is gapped, and I think I can stop here, my conclusion I showed that basically I generalize the adiabatic theorem to open system, and in some cases actually it's better behaved than the closed system version of the adiabatic case, and boundary cancellation on d-wave, it's somehow effective in the sense that it can increase the ground state population, and if you also use it in conjunction with the quantum annealing correction, then it makes results which are better than each alone, so thank you for your attention and if you have any questions ready to take questions. Thank you. So I think we can start from questions from here, or okay, Paolo. Hi Lorenzo, thanks for the talk, thanks for the talk, it was nice to hear all these old results and the new as well, so I've got a question for you, in your theorem you have this constant C, that well there's no K dependence there in the notation, but I believe there must be one because at least dimensionally, well if you have higher power over the time, well the constant should change dimensions accordingly, right? So my question is, do you guys have a general expression for the constant C as a function of the system gap, of the Liouvilleian gap? Is it like something like 1 over delta to the K power? So in other terms, how does this pre-factor changes with the system size? And by the way, Lorenzo, delta in general as you mentioned is a complex number, but of course here is a real one, are you guys taking the real part or the modulus of the gap? Okay, these are my questions, thank you. Yeah, very good question actually, so yes, the C, yes of course, this constant does depend on K, of course also because of the dimensional argument. And so the short answer is, in principle you may obtain it, it would require really a lot of work. Yeah, as I said, and the work would be, you know, for each different K you would have to do a lot of work. I don't know if you want to go through that pain. And by the way, you know, even for K equals zero, the value of this constant is, there's a lot of interest in this constant actually because it sets the adiabatic time scale in fact. And as of now, most of the times the adiabatic theorem will underestimate somehow or overestimate the adiabatic scale usually that you get from the theorem. So, I don't know, but the nice fact is that, for example, as given by this plot here, that you can see this constant K, this constant, sorry, C or K, which probably grows with K, doesn't grow too much in the sense that you do see sensible, you know, you do see that the fact that the adiabatic error diminishes. And finally, the gap, no, the gap is complex. What I wrote before was meant to be a complex number actually. It just depends on S, I mean, it depends on a real parameter, but then there are also other parameters which are now complex. So, and now you require, well, I mean, if the gap closes, it means that both the imaginary and the... Yes. Another question from here. And you showed that when the system bath coupling is... the bath couples to the sigma z, the system Hamiltonian and the interaction commutes, and in that case, the Liubidian gap closes at s equal 1. But you showed that adiabatic theorem holds when the Liubidian gap is scaled as 1 minus s to the power alpha. So, can I... Can we estimate the exponent alpha from the function of the unifying schedule? Yeah, good question. So, actually here in this case, alpha is the exponent which determines the speed at which the gap closes without boundary cancellation, because when you employ boundary cancellation, basically you are rescaling this s, right? So then it will depend on k. So this is the way the gap closes without boundary cancellation, where k is 0, if you want. And coming to this exponent alpha, there are some interesting facts. So in the unitary case, basically, most level crossing would be just exactly level crossing, so where this alpha would be 1, essentially. So that would be an x, right? Two levels that cross linearly. So, as I said, in the unitary closed system case, the most common value for alpha would be 1. For Liubidians, now alpha cannot be 1 because you cannot go lower than the zero eigenvalue. So alpha is... And there are some theorem that if the dependence of the schedule is analytic, which is basically what we are... Well, it's sufficiently smooth, let's say. Well, alpha should be an integer. So the most common guess, I would say, is that alpha is 2. In fact, this is the value that you observe for the single qubit adiabatic master equation where you see that alpha is 2. That's all like... So without further studying or assumption, this is the value I would imagine. Basically, you would need something else. You use some sort of symmetry, some other argument why alpha should be larger. But 2, I would say, is the most probable value. Okay, there is a time for another question here. Hi, I have a couple of questions. Firstly, how do you define the adiabatic error when you have degenerate ground states at the end of an ending process? Yeah, so basically, that's a good point. I mean, what is sigma 1? Very good question. So in fact, this is actually another... This allowed me to point out another fact where the open system case behaves better than the closed system one. Because basically, in that case, you should consider this sigma 1 actually as the limit of sigma s when s goes to 1 from the left. And that would be actually the... Assume that sigma is your thermal state at any s smaller than 1. This would mean that sigma 1 is the thermal state also at the limit. So basically, this behavior is not... I mean, in the open system setting, actually when you have a level crossing, sorry, in the closed system setting, if you have a level crossing, sorry, if you have a ground state, you have a level crossing with a ground state, the adiabatic theorem still works, but then you don't end up in the ground state anymore. You end up in an excited state. The closed system... Sorry, the open... The adiabatic theorem for open system is that... Guarantees that you always state in the... So to speak, in the ground state or in the steady state. And the second question is, you showed that if the gap closes in the middle of the annealing process, then your results are basically the same that you have for a gap system, right? So I was wondering how much of this is related to the fact that in open systems, the dynamics intrinsically tries to take the system towards the zero eigenvalue. So after the gap closes in the middle and then you let it evolve for the rest of the annealing time, what the effect of the gap closing in between is basically smeared out, because the dynamics itself is trying to take the system to the ground state. Yeah, absolutely. I mean, you can make up a lot of intuitive explanation and I think yours is a good one. When you actually have to prove this theorem, you have a series and actually it looks a bit of magic because there are terms in the series which diverge because the gap closes, so there are some terms that diverge. And this which should cause trouble, but for some reason they don't cause trouble. So that's the technical reason why... So you have to find a smart way to deal with those terms. Okay, I think... Thank you, Lorenzo, again for a very nice talk. Thank you, Lorenzo, again. And...


Hello and welcome. My name is Shannon Kemp and I'm the Executive Editor of DataVersity. We'd like to thank you for joining this DataVersity webinar. A new way of thinking about MDM sponsored today by Mark Logic. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we'll be collecting them by the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVersity. As always, we will send a follow-up email within two business days, containing links to the slides, the recording of the session, and additional information requested throughout the webinar. Now let me introduce to our speaker for today, Michael Dohm. Michael has worked at Mark Logic for eight years and currently serves as Health Care Solutions Director for Federal, State, and Local Government Health Care and Health Services. His recent focus and expertise is in solving big data challenges and groundbreaking work to find solutions to difficult enterprise integration problems by taking new and different approaches to those problems using Mark Logic. Mike holds a Bachelor's of Science degree in Mechanical Engineering from the Massachusetts Institute of Technology, aka MIT. And with that, we'll give the floor to Mike to get today's webinar started. Hello and welcome. Hello and thank you, Shannon. All right, good afternoon, and thank you for taking the time to join this DataVersity webinar today to hear about a new way of thinking about MDM. I hope it's going to be educational for you. I think it will be. As Shannon said, my name is Mike Dohm. I'm a Director at Mark Logic responsible for developing solutions that better solve the pains and challenges of our customers. I've been with Mark Logic for more than eight years and I've been working for companies dedicated to bringing together disparate data for nearly two decades. And Master Data Management is all about finding or creating a consistent and accurate view of the most important data to an enterprise. And if you don't know it already, after this webinar, you'll better understand how busting data silos and integrating them plays an important role in increasing the effectiveness of MDM. I'm currently focused on the healthcare domain, but I've spent time with many other domains including finance, law enforcement, defense, and the intelligence community. And we're seeing similar MDM opportunities in all of these domains where you integrate a lot of data. That makes sense. There's a lot of overlapping data and you've got to master it. In healthcare, we're seeing Master Data Management more than I would have expected or more frequently in more of our accounts. We're seeing it in master person indexes for hospitals and insurance companies. And in the state health and human services departments to integrate the many disparate and overlapping benefits eligibility systems. Okay, so what is Master Data Management? There's many definitions because it's a broad domain. It's practiced in many different ways. And I've listed just a few of the definitions from some of the well-known consultants and consulting companies, but there's plenty more that would have been equally appropriate to use. Master Data Management is many different things to many different people, so it's impossible to define in a sentence or two. Most implementations of it are different and they don't all require a full-blown solution with all aspects implemented. So choosing an MDM strategy is personal. It's not entirely unlike choosing a vehicle. The vehicle choices out there are many and varied. They don't all have the same number of seats, sizes of engines, varied tremendously. The purposes of them are different. So your spouse may have a different car from you. It's likely to have a different car for you because it serves a different purpose. MDM's no different. So what matters to you is what should guide you. So before you set out on an MDM project, please carefully consider what your goals and requirements are. Okay, so in addition to your MDM choice being personal, it's also complex. And not only is it complicated, but it's usually removed from the actual business of an organization and formed as an IT back-office function, where those who do the MDM implementation are often insulated from the face of change. And they're often completely unaware of the actual changes taking place in the business units they and the Master Data Management they're implementing are intended to benefit. On this slide, the items included around the center are implemented to varying degrees in different implementations, but there's usually some aspect of each in the successfully managed MDM project. Now, this isn't a comprehensive list, but these are the more common elements. So we start with auditing. It's important to know who did what and when for any create, update, delete, merge or unmerge operations, basically any change. But against the data that you're mastering, so auditing, automated and user actions is critical and you want to be aware of any of these changes to data regardless of where they're done within the system. Data flows, processes and workflows, two of these at once. You have source system updates and structure changes, ETL pipelines to move the data into the MDM repository, rules to dictate what can be merged and workflows to manually handle outliers that require human decisions. Now, this is obviously a complex set of processes that requires a great deal of effort to set up and it requires whole teams to coordinate things. On top of this, every transformation usually leaves some data behind and has the potential to be a point of data loss or data corruption if the transformation isn't perfect. Often the lineage of the data is lost. So where did this attribute of this entity come from? When was it extracted? What was the transform that was run on? What did it look like before? What if all that gets lost in an ETL process? Can you fix mistakes or figure out where in the process things went wrong? You really should be able to answer all of these questions. Now data governance refers to the overall management of the availability, usability, integrity, the security of your data employed in an enterprise. If you can't track all provenance and lineage metadata, can you confirm its integrity? Can you confirm where it came from? Is this metadata and data secured against changes from unauthorized users? Can you track if an authorized user made an unauthorized or highly suspect change? Data stewardship looks over all of this. It involves managing an oversight of all the data assets to help provide business users with high quality data that's easily accessible in a consistent way. Now many MDM solutions have a whole host of products that are all integrated together. And they have inherent integration code, different things that you have to learn. So wouldn't data stewardship be a little bit easier if you had a solution that was all in one unified product? Traditional lengthy life cycle of RDMS-based MDM. So with all this complexity, it's really not a surprise that it takes a long time with many different steps, especially in a relational-based approach. The activities listed here are fairly extensive, but most implementations actually require a significant, if not all, number of these. And this starts out with, the whole process starts out with simply identifying all the source systems and then the data in those source systems. And that data needs to be well understood so you create a data dictionary. You want to narrow this down to a list of unique data elements as many of the systems contain information on the same entities, hence the need to master them. And so you create a canonical list of entities and their attributes. You use it to create a canonical, ideally perfect data model. Next, you map the data sources and figure out what it will take to get the data out of the source systems and how often you need to do that. Transform it to fit the canonical model and load it into the MDM repository. This whole process is better known as ETL. Before you even run your ETL scripts, you need to test them. So these are just the steps that are required before you're even moving data into the MDM repository to begin the actual work of mastering the data. You still have the loading, testing, data disambiguation, cleansing, governance, more testing operations, et cetera, et cetera. So you can see with all these tasks, this takes a really long time. Long enough that some things are probably going to have changed from when you started. For example, the champions that you have could have been promoted. They could have moved on to another organization. They could have left the organization or simply lost interest along the way while you're getting to the point where you could do some actual mastering work. So this lengthy development time creates issues. And yet it's only one of the issues facing traditional MDM. There are many other strong forces working against it, and these are the headwinds referred to in the title. Now we just discussed the lengthy upfront modeling and ETL design processes, which are slow to show any progress. And because they're slow to show progress and the organization continues to move forward, the business goals and data often change while this is occurring. And the relational database approach is too brittle and slow to adapt to these changes. So as I mentioned earlier, MDM is usually owned by IT and disconnected from the impact to the enterprises, profits and loss to its goals. And this makes it even slower to react to outside changes. Most traditional MDM projects fail or only achieve part of their goals because of these reasons. Now, there are some MDM successes, and I'd be lying to you if I denied that. There are some excellent practitioners of MDM. They're not common, and they're able to manage all the moving parts and put in place processes to be aware of and accommodate ongoing changes. And although these people are rare, they can navigate the waters. One thing that is always true is that when they are successful, they're never successful for a low price. It's never cheap to do a successful MDM enterprise project. Now, there's also some custom solutions that are done within a part of the organization where they have control over their MDM, that data models, they can make schema changes to the database at will, they can generally be more nimble. The trouble with these is they're not enterprise-wide, and often they only meet the needs of the current project or campaign, and that's not true enterprise MDM. So I see one of the biggest problems with traditional MDM as the chase for the perfect model. Custom MDM solutions succeed when focused and not change to rigid schemas, but the results exist in project silos and don't benefit the enterprise as a whole. They have a smaller model to work with and to perfect. So there's the core problem with chasing perfection. MDM's goal is usually a truth-based model with a very rigid golden definition of entities and their attributes. In a large organization, agreeing on a single definition is not easy. I think a more reasonable and achievable goal is for a trust-based model. So remember, truth-based versus trust-based. In a trust-based model, you include all of the golden portion, but that's only a subset of the attributes that you have and those are the ones that you manage very closely. And you include this along with a flexible portion to capture and keep all the data. You expose all of this to your end users and let them decide on the other data that they want to use. Okay, so let's delve a little bit more into why the relational approach is problematic. Relational databases commonly require upfront data modeling that requires a very precise definition and near unanimous agreements before users can get access to any of it. You've got upfront data modeling that's expensive, time-consuming, and it requires an intimate knowledge of all data sources and data usage, detailed ETL mapping, coding, and testing for precise data modeling. Query patterns with the questions that users need to ask. They need to be known in advance so that appropriate indexing can be set up to deliver acceptable performance. And even with this, you still lose things like full-text search on unstructured data. And each of these steps is sequential. The waterfall model shown here takes a long time to field. By the time you deliver to the original business requirements, has your business moved on? Because of this, it's often easier to build new systems than modify existing ones. And this contributes to further data sprawl. So instead of slight modifications to existing systems, you end up with yet another database with different data definitions and a lot of data duplication. The relational approach is difficult to change, and as a result, the speed of IT can never quite match the speed of your business. This model assumes a static environment where data sources and access patterns don't change. That's how it works best. If the model is incorrect, if a data source was unaccounted for or its structure changes, the process needs to be revisited. Hence, number six, the restart process. If users ask questions of the data that the DBA didn't anticipate, you might need to add additional data structures or add additional indexes. In many cases, this means further schemas changes and a repeat of the modeling and ETL cycle. Now, I want you to think back to a slide with the Gantt-like chart of steps in the MDM life cycle. Do we really have to repeat all of these again? Unfortunately, yes. When things change, the answer is usually yes. Those business changes, new data sources, and existing data source changes coupled with relational databases requires a repeat. It should go quite a bit faster than the first time, but each of the activities will be impacted, and this cycle needs to be repeated over and over again. It's brittle and slow and costs a lot of money. All right, so if one ETL pipeline for a relational system is complicated, how about when there are nine separate ETL pipelines as depicted in this graphic? Or in a real enterprise system where there's many, many more than that? If you consider that each one of these will require you to rinse and repeat at some point in time, you're pretty much guaranteed that you'll always be working on data loading. So think of Sisyphus from that Greek or Roman story, pushing the rock up a hill every day and having it roll back every night in an endless cycle. It's basically the same here. Repeat things over and over knowing you may never quite get to where you want to be. It's frustrating to those who are implementing it. It's frustrating to those who are paying for it, and it's frustrating for the business users because MDM doesn't quite deliver on its promises. Even limited data consolidation projects require enormous planning and careful execution with multi-year timelines and multimillion-dollar price tags. We talked a lot about the ETL problems from relational to relational, but the source systems could be mainframes, modern architectures. Newer technologies like Hadoop MapReduce might be able to help with some sifting of data sets, but these generally don't have the enterprise security and reliability controls. So you'd end up with a trust level associated with these that's likely to be quite a bit lower. So people might not even use it. Mainframes might store the data hierarchically, usually will, so the ETL to fit it into the canonical MDM model will probably render it completely different or leave a whole lot of it behind. So the takeaway here is that the real world is messy and this makes data integration, which is at the heart of traditional MDM, very difficult. And one more time, traditional data integration using relational databases is at the core of most MDM systems and it's really with problems. It's too limited in scope. It takes too long to deploy. It's too expensive. It's too difficult to change. It doesn't deliver on modern user expectations of getting things fast, and traditional data integration is an expensive model that doesn't deliver within an acceptable cost and timeframe to your users. Complex because it's fixed schema-centric nature necessitates additional products in the stack. It's slow. Well, I think you all get by now why it's slow. It's brittle because the interdependencies of the numerous components make it difficult to make changes. Chains somewhere will break something else. And it's expensive because the time required to model, integrate, and maintain these components over time. Since so few of these work, so few MDM projects are successful, there's got to be a better way, a new approach, maybe with a more modern architecture. So you want a new way to think about it, a new way to do MDMs, a better way to go about it. You want to stop boiling the ocean. You want to overcome the headwinds. Stop chasing perfection. Align the solution more closely with your business. Adjust more quickly and achieve that progress incrementally. Progress that's actually tied to business drivers and events. And measure this progress in weeks and months, not years. Avoid creating duplicate data in the first place. You know, thereby increasing data quality and minimizing the need for fuzzy matches to find potential matches. Adjust more rapidly to changes. And why not also save all the data, all the breadcrumbs from the source data, including the lineage and provenance. What if MDM projects could also be business outcome driven? Instead of project successes and progress being measured in terms of technical milestones by the IT department that were supposedly met, but yet a couple of years later it still doesn't do what people actually want, you'd actually get what the enterprise needs. Believe it or not, it's possible to deliver on all of these. All of these are possible by fixing the architecture. Here at MarkLogic we do this with something called an operational data hub. An operational data hub enables you to take a new approach to MDM. So what is an operational data hub? This might be the first time you ever heard of it. An operational data hub is a hub and spoke approach to data integration where data is physically moved and re-indexed into MarkLogic. It's really not that different from what you're doing when you move data into an MDM repository. But to be a data hub as opposed to something that's a bit more common, a data lake, the system must support discovery, indexing and analytics, real-time access. What makes it operational is real-time access, supporting two-way data flows and supporting transactions that allow it to serve as a system of records to some of the applications that interface with it. In some cases, you have the operational data hub taking over one of the source systems functions and allowing, for example, a mainframe to be retired. With an ODH and operational data hub, you get 360-degree views of important entities. Transactional applications can directly integrate with it. It has all the advantages of a data lake in that it handles the three Vs of big data, volume, velocity and variety. An operational data hub is also characterized by a few other important things. In an operational data hub, data can be loaded as is without requiring it to adhere to a strict schema. A proper hub still harmonizes data, which has the same goal as mastering data. They're nearly synonymous. Data is in different formats with different field names in the different various styles, various source systems. But when it's ultimately served up, the records need to look at least similar to be processed as a group. So all MDM approaches must do some form of harmonizing, and we're not immune in a MDM implemented with an ODH. At the end of the day, well-structured documented APIs and data experts must return coherent data. However, you can take a different approach with an operational data hub. You can make that harmonization agile and progressive. Typically, key requirements are identified early, and the associated data elements are defined to be critical data elements are those that are harmonized first in what you call partial harmonization. And over time, you get additional requirements that drive harmonization of more and more data and the process of increasing the subset that is harmonized over time is progressive harmonization, or in this context, progressive mastering. And we particularly shine here, because Mark logic index is the structure of data from source systems in both harmonized and raw forms. So some search and processing can be done in the data lake style, but critical data is accessed and indexed in harmonized form. So I've spent a lot of time talking about the problems associated with traditional MDM, and I've introduced you to what may be a new architectural concept, the operational data hub. And the operational data hub is at the core of a new approach to MDM, the whole subject of today's webinar, that we're calling streamlined master data management. Now you still have varied source data systems, and they're still constantly evolving. New data sources continually get added to the enterprise, only now they can be easily added to the streamlined MDM system, where it gets harmonized and mastered in place. For each of the source systems, on the left side of the graphic, you see two-way arrows. They're often one-way into the hub, but it's also possible for there to be two-way communication with the hub updating the source system. There you see the streamlined MDM hub being used. On the right-hand side of the graphic, you see the same thing. You see the two-way arrows. But there you see the streamlined MDM hub being used at the point of customer engagement, not just as a back-office function. For example, you see a customer service representative speaking with a customer who just called them. Or you might see a benefits eligibility worker seeing a customer for the first time, wanting to check to see if they're receiving or have received in the past other benefits, benefits found in other systems. One of our other customers, a U.S. criminal history system, is built on a Markologic ODH and includes message documents that come from police departments, counties, agencies, and court systems across the state. These include new booking documents, court appearance documents, and others. They use the ML system to collocate or match and group these documents, which can often be quite difficult as the data elements in the documents can be, you know, they don't always match up. For example, first name, even if it spelled the same column name, one might contain John, one might contain Jonathan, one might contain Jack. And so you want algorithms and the source matches that allow you to find those. So the date of birth field might be off in the month, the day, or the year by a single digit. That could be just due to data entry errors. So after we've merged things, the collimated record includes all of the original source information, data source counts for each matching data element, and a history of the changes to support unmerged operations. So they're doing master data management and they're keeping all the data and adding a whole lot of metadata. And to accomplish this, they're doing all of these things inside MarkLogic. It's basically MDM integrated with the business and aligned with its goals. Now I'm going to move into some of the features that allow us to do some of this. To use this new approach, you need some features that are unique or at least unavailable in relational database systems. First up, we have schema agnostic, so-called load as is. Because of it, you can minimize extract transform load and replace it with ELT or extract load and transform or harmonize. Only now you can partially harmonize to incrementally deliver results early on and maintain buy-in. You don't need to create a perfect data model. One of our customers actually made changes to their data model daily and even in production, they're still doing it on a quarterly basis. And that's possible because of the schema agnostic nature of the database. Now you still want to be able to identify, you still need to identify the data sources, but if you forget one and you need to add it later, because it was just created, you can easily add it because of the flexibility of this data model. So despite being schema agnostic, MarkLogic is actually structure-aware. It's got this universal index that indexes words, values, structure on everything that it sees. And it does this for all the loaded data without actually knowing that structure in advance. And this allows you to run queries that can find anything, even if that's something that you're looking for, happens to be in a data field where you didn't expect to find it. So search searches across every data field, query allows you to query any of the more specifically, within specific data fields. So being schema agnostic is a very key requirement. It's central to the success as it's going to drastically reduce the upfront modeling time. It really does make it easy to handle new data sources and accommodating data changes to existing ones. It doesn't take all the work out, but it does let you do it faster and in a manner that lets you keep up with the pace of change so you're not always far behind. So streamlined MDM needs to be something called contextual. And we talked a little bit about this. So for example, when a call center customer service rep is helping a caller with their new insurance application and discovers there's already a match in the system and they manually merge both or abandon the new one, this sort of contextual or operational MDM allows some of the important data quality improvement activities to be done in the context of the business activities that the entity supports and it prevents the need for downstream disconnected MDM to clean things up later. Well, there's still some of that, but it minimizes it. If you never make a mess, you don't have to clean it up. Same here. The apps interacting with the streamlined MDM database and you're avoiding the creation of data problems that would otherwise need to be cleaned up later. To accomplish this, we use things like field weighted fuzzy matching algorithms along with data merging capabilities that all run in MarkLogic. I'll touch on a few other use cases soon after I've covered some of the other features. All the data. So what does this mean? It means we don't get rid of potentially valuable data. Storage is cheap. So, saving money on storing costs is not a huge issue. Relational databases were designed when disk space was extremely expensive. So it went to great lengths to avoid using that disk space. Unless the data is absolutely needed, don't store it and don't store it twice for any reason. Again, this doesn't apply now or at least to nowhere near the extent when this technology was created. In streamlined MDM, we recommend you master a subset of each entity's data elements and attributes, but keep the rest. Keep all the source data. Keep it in its original format. Index the contents. Keep everything and allow it all to be queried. Just let your users decide on what data they want to include. You can even keep data that doesn't match the golden master data just in case for whatever reason. This allows you to fix mistakes, to reverse changes, to unmerge things that never should have been merged in the first place. And it supports the more complete trust-based model that isn't quite perfect, but provides more capabilities to more different users and lets them make their own decisions. Metadata Unlimited. So you want an MDM system that doesn't limit the metadata you can store about an entity. Being able to store virtually unlimited metadata for every data element and keeping a copy of all the source data in its original state and format, allowing data cleansing, merging errors to be corrected would be impossible with relational-based MDM. Further, maintaining the source information allows system and application users to see all the data and decide what's closest to the true copy. This extra data is stored as metadata. If every element or attribute of data in the same entity came from a different source system or from multiple source systems, you want to be able to capture that in metadata. You have bi-temporal data that you might want to keep, and that allows you to answer queries like, what did we know when at some point in the past? And to do this, you've got specialized indexes that are essentially metadata, four different timestamp values, the valid start date for a piece of data, the valid end date, the system start date, and the system end date, you know, more and more metadata. So you want to make sure that... you also want to make sure that no one can change the data that they shouldn't. So you want element-level data security, the security stored as metadata. You...hopefully I've beat the dead horse and you realize that unlimited metadata certainly has some benefits. Simplified architecture, the so-called KISS principle applies equally well here. Don't underestimate the value of a simplified architecture. Traditional NVM solutions are built on a wide range of technologies. These technologies include relational databases, ETL tools, search engines, app servers, content processing frameworks, and even data virtualization technologies. This makes for not only a complex architecture with multiple products and people to manage them, but it also makes the data integration tasks inherent in traditional MDM expensive, brittle, and inflexible. Things like achieving a consistent security model across all these is difficult. Managing backups, achieving high availability, and overall administration are difficult and expensive to implement and maintain. And having all these products can make auditing nearly impossible, since many of the things that are happening to data in the different products and not all of this is captured or transferred to the central MDM database. Streamlined MDM can be integrated into an operational data hub built on MarkLogic, which is a single integrated data platform with database, search, application services, security, and more, all in one QA platform. Getting everything in a single QA platform means that someone else wrote and maintains the code, test it to make sure it all works together, and this makes for a more stable product, and it lowers your TTO, your total costs. Okay, you want a data model that's as close to the real world as possible. Streamlined MDM benefits from a logical data model where your entities are modeled as hierarchical documents instead of being normalized or shredded and forced to fit into the columns and rows in multiple separate tables as in a relational system. Documents are ideal for handling varied and complex data. They're human readable. They closely map to the concepts and business models of the data, and they avoid the impedance mismatch problem that relational databases have with applications where you got a whole lot of data transformation going on to be able to use it. When it makes sense for an entity to be split across more than one document to so-called normalize it or to be related to another entity, semantic triples can store this relationship, and they can do so with greater context or even multiple contexts, and let me talk a little bit about what that means. In a relational system, a customer is related to a product through a many-to-many relationship, but the fact that there is a relationship doesn't tell you anything about how that customer and that product are actually related beyond the cardinality, and that's really not something that's important to your end-users. Now, the customer may have bought the product. They could have returned the product because they didn't like it. They could have returned it because it was defective or they could have just been confused as to how it works and called customer support to get answers. Now, these are just four of the many contexts between these two entities that I came up with off the top of my head while preparing for this webinar, and there's, of course, many, many more. All these situations can be captured through semantic triples that have the same subject and object but different predicates, so the same customer and product but different interactions between them. Same thing goes for a patient and provider or any two interacting entities in any domain. Each interaction has contextual differences, which can be captured with semantic principles. Similarly, semantics also gives you the flexibility to define master data beyond an object and to compose relevant domains based on attributes such as behavior. Forester calls this approach contextual MDM, and it allows you to support multiple domain models on one MDM system, even with the specific entities having mastered portions. Semantics can also enable you to use a hybrid registry approach. So you've got the oven spoke coupled with a federated or a oven spoke and a registry hybrid type of MDM approach. So the data can be left in the source systems, at least some of the source systems, and linked through semantic triples. So documents and triples combined as possible in MarkLogic is a very powerful combination. Now you need security. If you're going to bring all your enterprises most important data assets into one place, then you better make sure that it's secure for that reason alone. If you want to also allow applications to interact with that data in an operational environment, you're going to need robust data access controls that support all your data governance requirements. Ideally, you want element level data security, which happens to be extremely rare in a flexible schema agnostic database, but it's not on existence. I know somebody who has it. Okay, full auditing. We've touched on it. Lastly, and closely related to security, we've got streamlined MDM requires a system with full auditing. Who did what, when, and why to any piece of data at any time during its life cycle, even if that someone was in automated process or was trying to cover their actions. As I mentioned earlier, in a system of different integrated products, it can be quite difficult to trace what happened when changes were made outside the database. MarkLogic's single unified data platform, the features I mentioned as being critical to streamlined master data management are all closely related. Full auditing requires a single unified platform and it requires metadata if you want to be able to easily query the actions and changes to your entities. Storing unlimited metadata that's easily searched is also closely tied to a document model that's schema agnostic. Being able to search in addition to query requires something that has search engine internals. So these are all related and important features and combined together, they deliver the capabilities you need for streamlined MDM. So let's cover a few of the real world examples. I'm just going to cover two and I know I've run over because of the original mistake. In each of the examples, you're going to see operational mastering with some fuzzy matching, some fuzzy search and field weighted algorithms and data merging to support the search for duplicates and eliminate them. This example is no different. On the right hand you have various applications that perform actions against the MDM operational hub. And on the left side, the source system with one or two-way access. This customer is a United States eastern state. It's engaged Mark Logic to create a single view of beneficiaries in the state system and they're doing this by integrating beneficiary data from their children's social services system, their resource eligibility system for temporary assistance for needy families and supplemental nutrition assistance program among other benefits. And the juvenile services system into one master record for beneficiary in a Mark Logic operation data. So the data source system read initially we're only receiving data from the hub through a REST API. But after it's gone to production, they're going to get added. So this is just yet another example of the flexible data model accommodating progressive incremental results to be delivered to your end users. So this solution allows all benefits programs to search and select existing person records, lets you avoid additional duplication and creation of already existing people. It includes data merging features that of course maintain enough information to be able to unmerge later, supports unlimited search, provides a 360-degree view of beneficiaries and helps to prevent people from being enrolled in overlapping benefits programs as commonly occurs in siloed benefits systems. This is kind of important. There's smaller budgets and that allows them to spend a little bit less money. HealthCare.gov, little operational mastery. Mark Logic is the primary product used for HealthCare.gov. It's run by CMS, the Center for Medicare and Medicaid Services. And I'm going to blitz through a little bit of this because we're really close on time. So you've got applications and information about consumers existing in many forms, coming in from multiple sources. People submit multiple applications. The family remembers, might submit applications with the same people on them. And Medicaid may transfer a person into the system from a state system. As a result, a person may be listed on many applications. So the system is able to, while live, to find all the different matches that it's 100% sure of and it's able to do this under a load of as high as 300,000 concurrent users. And it's able to direct the data, direct people to their existing data and applications as they log in and to merge applications that it's 100% certain of. Of course, it also presents potential matches where it's not 100% certain to a person for them to do a manual decision on merges. We talked about being used by customer service representatives and there's 10,000 of them, so that's a little more example of doing operational mastering under scale. Now, of course, mistakes can and will happen. So just like everywhere else, we keep the original information for the infrequent but necessary unmerged operations. So I hope you've learned from what I shared and I can see and can see that there are alternatives to the traditional approach to MDM. MarkLogic provides one, streamlined master data management to quickly summarize the high level reasons why it's ideal for such an approach. MarkLogic makes it easy to get any and all of your data in and have it be fully indexed. It's immediately queryable using queries of any complexity without needing to know those queries in advance. This combined with enterprise-ready features, such as it being 100% asset-compliant, common criteria certified enterprise-grade security that's good enough for the Intel community, and out-of-the-box continuity of operations supporting features make it perfect for meeting your MDM needs or complimenting your existing MDM tooling. So that's all I've got. I hope you learned something today and if you have any questions, I can take them now. So the question about regarding metadata. Is the metadata stored in the same database as the data instances and can this metadata database be integrated with other commercially available metadata repositories? So you can certainly, it is stored in the same database. It's all queryable together. So the data and the metadata are both first-class citizens in MarkLogic. There's certain kinds of metadata, document properties, for example, that need to be queried slightly differently. But it's all stored in MarkLogic. And the language that, well, you can access MarkLogic through JavaScript. You can access through REST API. You can access it through XQuery. XQuery and XML or JavaScript and JSON. Both of them allow you to easily export data sets. So just maybe the metadata to an external system. This is one way that we might integrate with your existing suite of MDM tools. If you already have an investment, I didn't really touch on it. But if you already have an investment, we can work closely with it and take advantage of some of the tooling that's associated with that. And I know we said, we typically end right at the top of the hour, but just to get a couple of questions in the recording if you have time, Mike, if that's okay. Sure. Can you explain the idea of golden portions again? Is this a core subset agreed by all or different for each project? Okay, so there's various patterns used in how you do your data modeling. And we have this one that we call the envelope pattern. The envelope pattern is this all-containing document that up at the top level of it, you have the stuff that everybody agreed upon. So it is exactly what you're asking. It's the core subset agreed by all. It's not really different for each project. The whole point of the bottom section of the envelope pattern, which contains all that source data and the previous versions of each of the data elements, is what gives you the flexibility to accommodate the different users. And just one last question I'll sneak in here is how is harmonization different than imposing a schema as in traditional relational database? So the beauty about harmonization is that you can load the data, all of the data into the streamlined MDM system without having to do any of the modeling up front. So you can incrementally identify the most important data elements. You don't have to get your model perfect. So really harmonizing is you're essentially modeling. You're moving towards a canonical model, but you're not held back by changes to that model. You do not have to deploy a new schema in order to transform in place inside the database some of the data. So you're basically just adding to the golden portion of the different records. And when you do so, you can continually deliver incremental results to your users. It's really quite powerful. As I said in the talk, we do not eliminate harmonization or ETL entirely, although you really don't need to do ETL, but extract load, play with it a little bit, and then do your transformations or your harmonization. Does that make sense? It makes sense to me. Well, Mike, thank you so much. It really was a fantastic presentation. I really appreciate you taking the time to speak with us today. And thanks to all our attendees for being so engaged in everything we do and for your understanding and patience today and asking so many great questions. I will send those over to Mike. And again, I will get the follow-up e-mail out to you by end of day Thursday with links to the slides and links to the recordings so you can prove at your leisure. Mike, thank you so much again for speaking. Thank you, Stan. Thank you for sponsoring. And I hope everyone has a great day. Thanks. Thank you all.


What's good QteGang? I'm Gérard and you're watching Artifacts, the cool stories behind the scenes of your favorite studio, Quantic Dream. And today's item is not here because today I'm about to tell you a secret. How to unlock 17 new storylines of Detroit become human. That's not true. I'm kidding. False, fake news. Nope. I want to reveal to you guys what makes Quantic Dream such a successful studio. What do you think it is? Is it the Motion Capture Studio? One of the best in Europe. Don't get me wrong. Magnificent. But there's more, there's more. Or is it the sound department where we're recorded a wall-winning schools made by work-loss artists. It could be it. It's pretty cool. But I feel like we're missing something. Is it because of our own in-house gym? Nope. I don't think so. I'm tired. Or is it the incredible sum of talented people who work here to develop top-of-the-line technologies and create our unique type of gameplay? Well, they're awesome. They're great. But we are still missing one thing. Because to make this work, to bring all those people together to create the magic, you need a special element, an X-Factor, a special source.  And it's not me at all. It's this, the first ball table, or a baby football table. Or as we say in French, un baby, allez on, on, on baby, on, on baby. Look at this beauty. A vintage piece of history that was actually gifted 33 years ago for the 18th birthday of David Cage, our founder. And he kept it all those years, even brought it with him when he moved to Paris to start as a musician in the early 90s. When he had such a tiny flat, they had to sleep underneath it. It shows the football table over a bed, guys. That's commitment. And now this is the central item of the lunch break room. If this room was New York, this would be Central Park. This is what we play to release pressure, to bond and to win. Because make no mistake, football is a game and it's about winning. And we even have our own in-house champions, Dajama, Sophie, Nicolas, Alex, Tony, Alexi, legends of this game. Without this and those late night games, Nomad Saul may not have ever existed. And if you don't believe me, even David Bowie, David Bowie played this very table. What more do you want? Do you want to see me play? Is that what you want? Is that what you want? Well, Tony, champion, please join me. Let's show them. See? I'm losing and I'm having fun. Losing is fun. See you next time, QtGang. Bye, Tony.


What's up everybody, Jayce here for JayceBee's CaseBrakes.com. We're doing a quick little 19-20 cup hockey six box case break Pikachu number 14 RMB filler, random number block for the Devils, Leafs and Jets. So again only for these teams in this specific break of Pikachu number 14 you get a piece of them for $125.99 and again every spot gets a zero, a random number from zero through nine and as an example there you go how it works right there and you get the first number on the left side of the dash and there you go. Good luck dice roller customer names from Sean down to Danny I got zero through nine. Roll three to one four times good luck one two three and four four times Mike tower down to Sean four times four times zero one two three four five seven nine one two three and four four times two down to zero so Mike you have the two spot Danny with nine Stephen Kendrick get one any one-on-one for these teams of yours Eric with five Danny with six Sean with three Danny with eight as well as seven willing with four and Sean any one-on-ones or sorry any redemption cards for these teams of yours so there you go coming up next guys in a separate video is Cup Hockey Pikachu number 14 good luck guys