Hi, I'm Bruce from Gloucester, Virginia. You're watching Trucker Josh on YouTube. Make sure you like his videos, subscribe Let's get him over a hundred thousand subscribers. Thanks Basil! It's another new day man. We're in Chester Chester, Iowa. We're just over the border from Minnesota. Minnesota's like right over there right over there And we are up at the ungodly hour Of what is it 6 a.m. Life of a trucker. Let's get some freight delivered We've got to deliver this lumber that we have behind us And we got to run back up to Minnesota pick up another new load Tarpet and then start making our way towards Alberta Canada. It's gonna be fun first We got to take care of the weasel You need to go outside? Right here. Oh that fresh air You know, I always say that I hate mornings, but it's not really true I love mornings. Fresh air first thing in the morning is the best thing All right, this guy is starting to lighten up a little bit there It's a good sign Let's get going. Railer's coming with me Right on, right on I need that It's only 20 minutes down the road here What we're gonna be unloading and they don't usually unload today. Today is Friday when I'm filming this Uh and they usually don't unload Fridays for some reason, but I called them and asked them really nicely and Well, here we are living the dream Proceed to the highlighted route. Oh, I intend to Karen I intend to but first we have to make sure that no other people are on the highlighted route first Okay, I think you're good. We'll deliver some lumber It's a nice small little town The message yesterday that I'm showing up this morning I'm just pretty sure the people who agreed to unload me aren't the actual people who are going to be unloading me this morning That should all be good Zach actually two minutes before and they told me to be there They told me to be there around 7 15. They'll get to me around 7 30 I'm gonna get there by the looks of it around like 7 13 7 12 The sun is starting to come up so that makes me happy We made it they acknowledged My entry into their little world here in that little yard their little their little lumber world And they told me to wait back here for a second There's a truck not this yellow one here, but there's one further down where that second tractor's coming from Uh, those are the last two bundles on that other tractor And uh, that other truck's gonna move out of the way.  We still have to go get reloaded, but that's gonna take a while that always takes a while the reload there Takes an average of six to eight hours It's a live load. It's you'll see you'll see. All right, so the tractor's getting out of the way This Semi truck's gonna come through here. Oh another guy is rushing into his spot back there.  He came in the exit.   Oh, yeah, they're telling him to move Buddy come on You think you'd get away with that? Try to sneak in front of everybody Sneaky sneaky Not sneaky enough To everybody right I'm just glad they actually stood up for me and said hey That guy's first. Yes. Thank you very much. I am first. I was here Just like that We're done The wonderful got back out of a little space here Get out of their way so they can get their next driver in here Maybe go find myself a shower That would be fantastic In and out of here in less than 20 minutes This guy's gonna Make room for me here Very nice of him I don't know why he unstrapped on the road the lineups on the other side in the yard, but here he is Thank you wave And it will be on our way Always say thank you When they said they didn't turn around this corner, I believe we'll be back in minnesota Minnesota line it's around this corner here somewhere There it is On All right, we're gonna turn left on shooting star wildflower scenic byway because that's What they called it right here What a magical magical bypass it is a byway. It's not a bypass.  We'll have plenty of time to get there's a two-day drive We'll have three days to get there. So I won't have to run so hard Okay, they said 124 Or 125 okay, so i'm gonna back in right beside that guy I guess He is right in my way Shoot So as soon as that guy Pulls out of there. I'm going in behind him I can't get into the door. There is another door on the other side of him over there I could get in there, but really tight and he said he's almost done and he's gonna move so I'll just wait whatever. This is really quick that I got in here. Usually I have to wait like Six to eight hours like I said till I can leave here But usually it's just a few hour wait just to get into the warehouse now. I got here I let him know I was here boom. They got me in the warehouse right away VIP Tell you what I don't know why it's so quick this time, but I'll take it I guess they're just ready for me and the timing was just perfect He usually does take them about two Maybe three hours to load the trailer It's glass and it's got to be put on there just perfectly They nail it right down onto the deck the straps are actually just for show and for dot requirements The load would actually stay there without a strap on it But you don't want to test that like that one guy did with the lumber you still got to strap it down and then So this guy's moving out of the way now Back her in Well, it's a super easy Nice, you see how they nail it's Right down to the deck nailed all on here at the a-frame These things aren't going anywhere, but we're still going to tie them down anyway Just to be sure they load them with these big cranes up here They're loading the trailer on the other side of me right now That's going to be super easy Super easy to tarp that All right, let's get her done That's the guy beside me. It's just about ready to tarp his load This is mine All wrapped up like a birthday present Ready to go to Calgary I just got to tie down my extra tarp at the back here yet Tighten that down don't want to lose that on the highway. It's like a hundred pound tarp Maybe not that heavy, but It's heavy Off we go. I think we got another six hours and some minutes six and a quarter hours available to us drive today yet Then I'm out of my 14 hours. So we'll make it quite a ways And we're in no rush like I was saying we got all the way till Tuesday. It's Friday And we got till Tuesday to go 2000 kilometers or like 1300 miles So We're gonna be taking her slow and easy Sipping the fuel just sipping the fuel That's the plan Going to bed early or stopping early not going to bed early, you know reading my book Sleeping in a little bit.  This is a heated warehouse. Look at mine Look how close this is like just barely barely Made it in You can't get much closer than that I gotta wash this truck. It's filthy.  Let's just you want to burn more fuel than me on the two lane I go the full speed limit though I keep up with traffic because Two lanes. I know how annoying it can be to be stuck behind someone going slow Well, that's a big heavy load that guy's got there. We've got about a half hour up the road You know what that means Stop and check on the load make sure the tarps are still tarping make sure the straps are still strapping Make sure the load is still Where it's supposed to be doing what it's supposed to do 11 kilometers Sitting very still I'm actually going to see how much parking they have here if they don't have if they have some parking for me I think I might go in and grab a shower right away We'll see this this parking lot. It's always a gong show But we'll go take a look we'll go do a loop and see what happens. I'm not going to grab fuel here You have arrived at your destination on the right side five j travel plus a number 576 I think it's on the left side there Karen any parking spots to the highlighted route Oh, yeah, there's some parking at the back there Hopefully there's no wait for a shower Spots here. They're all doing here. What are we all doing this light pole right in the middle of the parking lot is Dumb I'm just gonna say it.  Why would you put it right in the middle of a tight parking lot? I'm gonna back in right here beside mr. CFI. I think All right diesel. Here we are Hopefully no one backs into me these empty spots right beside me. So someone's probably gonna back in here that light pole Why why why see that white truck is over there? Those two spots him and the one beside it. It's impossible to get in there. I've tried Usually those two spots are open unless if it's like taken up by a bobtail that guy's bobtailing That's a good spot for a bobtail because a big full-sized unit can't get in those spots. That's perfect But yeah, hopefully no one backs into us Let's take a look at the app here and see if uh There's a shower available if there is I'm gonna wash myself off scrub myself down a little bit You guys are gonna wait in here though. I'm not weird Let's continue on How much time do we have left yet? Let's do it. I don't want to stay here. This place scares me Such a bad parking lot this guy came and parked in right beside me here So close to me didn't hit me So close I don't like parking in these parking spots or these parking lots My hood ripped off the apples. Yeah, maybe you're on clear water Maybe up there if the okay, but you just pulled out in front of me now. You're gonna stop in the driveway Don't wait now. You're gonna continue driving What are you doing? I'm gonna get great fuel. This load is just perfect streamlined Wind just slips around my truck and doesn't even feel the load behind me Here we are Clearwater, minnesota It's only 6 30. There better be parking Tell you what better be parking for me See I want to go off to the left here and check the parking lot to the left It's just Hopefully nobody's gonna be coming out because the driveway is very narrow They take my parking spot It's on the street That's right trucker josh sleeps on the street Nobody's here either good. This is the safest spot where uh Nobody'll hit me if you haven't noticed yet. I'm paranoid of people hitting me when I'm parked I have trust issues Come on let's go See when I park on the street here like this Nobody's gonna hit me nobody'll back into me And that's what I like


Hey everyone happy Monday today, September 26th I had to run out on Friday and was tied up all weekend So I did not have a chance to get out the weekly video update In its normal form. So just wanted to do touch base this morning just to kind of Give a few thoughts going into this week Obviously so two weeks ago with the CPI number coming out big move lower That was on a Wednesday, and then all of last week was pretty pretty red in as far as stocks go And so what what happened to our positions? Well, we got blown out of some of our Woodpecker butterflies right to the downside. So we had to close several of those out. We also took Some losses had to exit on our couple of our calendar trades our double calendars Because these are just these are just massive moves. So one thing I think is is important. Well a couple things one I've been getting a lot of questions about how do we how do we hedge a you know calendar spreads or you know what's the best way to hedge and the reality is The calendar spreads that we do they are defined risk and we have a very specific way of getting out of those When they're losing trades just because you have two weeks of Massive, you know, I mean these are massive moves. I mean these are beyond the expected move. So You have to you just you have to take a perspective that if this happens you will take losses You can't you can't plan to hedge for that because 80% of the time it's not gonna happen 80% of the time it's gonna stay within that expected range And so you really just have to look at this as These are these are the 20% that you're going to take losses on And and you know, I know I beat this I beat this over and over and over again But it comes down to position size and so I think taking the mindset of all the market conditions are bad So I'm not going to trade this strategy right now. I think that's a mistake I think that you need to take a perspective of hey, we got hit it Pushed out of our range. That's not normal. That's not that doesn't fall within our 80% probability that we usually Book profits on this falls in the probability of the 20% that we that we lose on and that's that's just how it is That's that's part of trading, you know Trying to change what you're doing every time you take a loss and trying to figure out how you could have avoided that loss While while it's good to always be continuing to grow and tweak your strategy I think it's a mistake to constantly change your strategy every time you take a you know, go through a drawdown or Or have a situation like this which just happens some of the time, you know, that's just and we never know in advance when that's gonna happen now With would I say? Reducing position size is an okay thing to do the answer to that is always always always always so, you know having the mindset of you know, just it's the market conditions are bad So I'm gonna stop trading. I don't I think it's a mistake But I think reducing position size in any situation is a good thing to kind of get that Get the flow back in the strategy get the flow back in the market get your confidence back in the strategy or whatever It is that you might be thinking of either consciously or subconsciously reducing position size is always a good thing until you kind of Get to understand or a better perspective or whatever it is And then you can kind of scale back up to your normal position size. I think all I think that's always a good option Obviously so Monday, we're starting out S&Ps are down another 20 pre-market. We've got about 40 minutes 50 minutes until the till the market opens so we'll see what happens, but You know, I will I will not stop putting on our kind of our core positions here Just because we got we got pushed out So I think that was kind of the main the main theme of what I wanted to talk about and then as far as hedges go, you know one thing that I used to do a lot of is Just carry always a certain amount of short delta And I've talked about this in the community several times but I've gotten some some additional questions about it recently and Just to just to help you understand as far as how how I'm managing my my overall different accounts is I am I am I am just Exclusively using NTT as my directional bias. Okay, so let me bring up a NTT chart. So I I've been I've been very short overall in my directional bias now Friday after that big drop I I essentially cut out of all of my all of my short positions that I had futures NTT portfolio positions You know anything else that that was kind of short delta. I pretty much cut out of and And so I'm I'm long right now now We're still in the downtrend her trend candles are still red And I just said I'm using NTT for my directional bias But when when you get a flush like that I also just wanted to take profits on those and I don't mind being a little bit long here I mean what what we know about bear markets is you can see if you can have some very Serious kind of rip your face off rallies, right? And so I don't mind being long here. Will it will it keep going down? Maybe it will will we have a massive bounce today? Maybe we will we don't know but but the bottom line is it's it's more about just kind of taking profits If we do get a bounce, I'll kind of start reloading my shorts But but for now I'm just I'm a little bit biased And that's really just with Delta neutral positions that have pushed to the downside And so now I've got long Delta because I need a bounce back to kind of get back to center. So Anyway, hopefully that helps. We'll be back on track with kind of our standard full go through every position every trade Weekly update next week, but I just wanted at least touch base this morning And kind of give you some initial thoughts that I've been thinking about hope that helps talk to you soon


Hi everybody, we're back. This is Dave Vellante of Wikibon.org and this is SiliconAngle.tv's continuous coverage of Oracle Open World. We're live here at the Moscone Center in San Francisco and this is the spotlight on Oracle Backup and Recovery. We've been drilling down with experts and practitioners and analysts on the topic of backup and recovery. It's a complicated situation for a lot of practitioners out there in our audience.  Not only are you out of a job, your company is out of a lot of money and that's a bad situation. So backup and recovery is a very important topic and we're here with Darryl Smith who's the chief Oracle architect within EMC's IT organization. We've had a number of practitioners on from EMC's IT organization in the past, from their CIO, Sanjay Mershandani to a number of other folks and we really appreciate Darryl, you're taking your time out to come on theCUBE. Welcome. Thank you very much, I appreciate being here. Yeah, so as I said, this is a complicated topic for a lot of people. It's one that doesn't necessarily get a lot of the headlines. You haven't heard a ton about it in the keynotes and things like that. But so let's start with what you do at EMC IT. You're involved in backup and recovery and you're involved in Oracle. Talk about your role as the chief Oracle architect. Sure, well as you mentioned, I am the chief Oracle architect for EMC IT. I'm responsible for the successful run of all the databases and protecting them is a critical component. As you mentioned, if you lose your database, if you lose your data, you're out of business and that's a lot of pressure. And unfortunately in the same respect, nobody thinks about backup and recovery until you actually need it. And that's one of the big challenges. There's no heroes in backup and recovery. There's only villains. Only goats, right? So obviously EMC is a big Oracle shop. You guys have made no secret about that. It's one of the largest on the planet. And you basically run your business on Oracle. So tell us about protecting Oracle data. What's different about it? What's unique about it? What's so challenging about backup? Sure. So we have well over 800 databases approaching 900 databases that we're responsible for and need to backup. And that's pretty much about 100 terabytes of data to backup a day. So the biggest challenge is that's about 100 terabytes of data to backup a day, right? And that's growing all the time. And we talk about the explosion of data and that's in Oracle databases. It's just growing and growing and growing and we can't get rid of it. So it becomes more of a challenge as these backup windows start to get bigger and bigger and your backup infrastructure doesn't keep pace. So you're backing up 100 terabytes a day over 100 terabytes a day and you're doing an incremental backup? Is that right or you're doing daily fulls? We used to do incremental backups and that can really help you save on space on your tape drives. If you're still using tape drives, hope not. Or if you're using disk arrays to do your backups too, much faster response times. But the problem is with an incremental backup in order to recover that, recovery time is very important. In order to recover the incremental, I first have to recover my last full. Then I have to roll it forward with each of these increments and that adds time to recovery. So our goal is really to backup full whenever we can. And with the advent of data domain and the deduplication, I can now do full backups but in the same space the incremental backups would have taken. Okay, so your RPO is low for your Oracle apps. Zero. You have zero RPO. No data loss. That's the requirement. Okay, but you're also saying that your RTO is a main driver that affects your methodology. Correct. If you've got a mission critical system and when it's down you're losing revenue, how long can I take to recover that database? Not long. Well let's say it's 10 terabytes or 20 terabytes. Can I really afford to back up or to recover at let's say a terabyte an hour? That puts me at about a day. A day of lost revenue. We can't do that. So how do you achieve RPO zero? You have a three site data center or are you? So if you're talking about disaster recovery, that's a little bit of a different story but we do replication from our main data center which is in Massachusetts down to our secondary data center which is down in Durham, Massachusetts at Durham, North Carolina. Okay. And we do that through multiple ways. In some cases we do full database replication through our storage replication technologies like Recovery Point and SRDF. In other cases where we've got super mission critical databases, super large databases, we do a standard roll forward where we just replicate the redo logs and the archive logs and we just keep that database rolling forward. So we know it's always ready for us at a moment's notice. Okay so locally you can achieve RPO zero independent of a disaster, a disaster is a different situation. Okay. So locally we do that because in an Oracle database after you take your full backup you then back up all the archive redo logs, right? So all those transactions are saved in there so you can roll your database forward to any point in time. And use an R-man or what methodology is it? Absolutely, R-man is the only way to go, right? Yeah but there's still a lot of people who don't use R-man in the Oracle. Right, well I can remember back in the day even before R-man where you had to back up the data files and back up the archive logs individually and you had to basically put them out somewhere and then have some backup thing come along and take them later. It's a manual process. Oh you write a script, very error prone. Very error prone, especially if you really don't know what you're doing. With R-man it's very simple, you just tell R-man to recover until this point or recover until current and you just sit back and watch it do its work. Now how do the products that you're using, obviously using EMC products, right? Absolutely. How do they work and integrate with R-man and the whole Oracle environment? The entire EMC set of products are really designed to work with R-man. So we have backups of strangers where we offload them. So we take a storage clone, mount it up on a completely different server and back it up from there using R-man. But R-man understands that and understands that even though we're backing it up from this proxy server, the storage clone, the storage node, the archive logs that we back up from the production database are going in the same area and it knows they're the same. Networker adds an integration. That's another product from EMC so that when I back up through R-man it uses Networker as its virtual tape device to back it up to whatever virtual tape array you have. For us, that's now data domain because with the deduplication again, as I mentioned, I can do full backups all the time. The nice advantage to offloading this backup to a proxy host is that if I need that data back, I simply reverse the clone and I'm rolling it forward immediately. That helps me with my recovery time objective. Okay, so paint a picture of your backup architecture. What are the piece parts and help us understand what it all looks like? Sure, so today we've got databases that are backed up with these storage clones. We've got databases that are backed up straight through Networker to data domain. We've got some databases, like our databases service offering, where we just do daily exports, again to data domain. And so we use a combination of all three of those methods in order to back up each or 800 and some odd databases. Okay, and you mentioned before, I think off camera, that you were 100% virtualized, is that right? Well, in areas, so from a server virtualization perspective, we're at about 98% virtualized. When it comes to databases in general, we're around 84% virtualized. But our new system that we just launched, which is Propel, which is based off of SAP, running on Oracle databases, is 100% virtualized and it's running extremely well. Okay, so it's pretty high levels of virtualization. I think Joe Tucci several years ago in an endless meeting I asked him, when you're going to be over or close to 90% and said we're going to do it within five years and so you're tracking to that. For overall servers, we've hit it. Yeah, and servers, I was talking about the applications, which is really the hard part. Right? It is. And so what makes that hard? What are the barriers to getting that up? Do you have an intention of getting that up? Is it performance concerns? Is it the application heads? Is it process? What's the issue there? So from a database perspective, we were concerned about performance and we spent a lot of time trying to figure out how can we get our databases virtualized and achieve the same performance that we had, or better, than we had in physical world. And so we spent a lot of time, we worked with VMware engineering, we worked with a lot of performance experts and did a lot of benchmark in our labs and we've come up with a bunch of best practices which help you to implement that. And so now we're getting, as good in some cases, better performance, right? Moving to newer, faster servers. And so now it's a matter of resources. How many people can we do as quickly as possible to get to that virtualization? So where does the cloud fit in? I don't know if you had a chance to see any of the keynotes this week. Big dose of cloud. What's your take on cloud as a backup target and what are your thoughts there? So adding cloud in for backups, that's a problem. Your backup infrastructure typically is going to be held back by that movement to cloud, or I'm sorry, the cloud's going to be held back by your backups because your backups are defined to go to one place. And so if you start moving your cloud from here to there to somewhere else, how are your backups going to follow it, right? That's where we came up with DDBoost. So DDBoost is essentially abstracting or taking away from your hardened infrastructure. And so you can back, it doesn't matter where you are, you'll backup through DDBoost, full arm and integration to your backup target, regardless of where you move to. And so that's really helping us to accelerate into our cloud. Okay, so public cloud as part of the strategy or not yet? So we have very limited public clouds. We have essentially applications that are running outside of our data center. Some of it is in our business managed units. Some of it is, for instance, our sales, our sales contacts, I can probably say at salesforce.com, right? So we've migrated some of it out there. We have deep integration with them so we've got that same copy of the data internally so that we're able to very easily and quickly move anywhere we need to go. Going forward, we're looking at actually extending ourselves into other people's data centers because at end of quarter we have a huge surge, not just in infrastructure and people needing to get their work done, but that's when it becomes critical to get it backed up and critical to make sure that we can restore that very quickly. So that's an issue if you just don't want to buy the extra hardware and software assets if you can just do an end of quarter surge. Well, yes, and I'm sorry, no. So there's the over capacity that you can get for end of quarter surges, but there's also, as the business needs to be agile, we may not be able to get that infrastructure in quickly enough. So it may be a temporary surge out into the public plan. So it's not just an expenditure issue, it's a speed, flexibility, we've got the agility. Agility is really the name of the game. So, Darrell, my last question for you is, talk to about what you would advise your fellow Oracle practitioners with regard to how they're architecting backup. What are the things that they should pay attention to, some of the gotchas that they should try to avoid? What's your best advice? So my best advice would be plan ahead. Your backup infrastructure's not going to keep up with you unless you make it. That's been our biggest problem. It's slowed down a lot of our plans because our infrastructure couldn't keep up. You've got to get to some sort of a de-dupe or compressed version on disk. Tapes really can't support you anymore, not with the size of the way the data's going. So you've got to plan ahead, you've got to get those guys on board before you can get launched off. Excellent. All right, Darrell Smith, thanks very much for coming on theCUBE. It was a pleasure meeting you. It's been fun. Take care. All right, keep it right there. We'll be right back with our next guest. We're live. This is theCUBE from Moscone in San Francisco, Oracle Open World. Keep it right there.


about the coding strategies to enhance parity quantum approximate optimization and by the way she's from Innsbruck University. Thank you so can you hear me clearly yeah good okay so yeah I will present you to you my work about decoding strategies to enhance parity quantum approximate optimization which I do during my PhD at the University in Innsbruck so here you can see our group so in the middle you see the head of our group Professor Wolfgang Lechner who is also involved in this project as well as our postdoc Glendon Bang which you can see here or just sitting right next to me and yeah as some people may know our group we work a lot with the LHSAT model so and to come to this I want to show you here shortly how to encode optimization problems so here you see Hamiltonian and the 2d qubit arrangement so the circles are the qubits and they're connected in line so the edges are the interactions where the interaction strength is j i j is where the problem is encoded and and on a physical device now one would have this problem to encode this long range interactions and this is actually now the main motivation behind the LHSAT model which implements these interactions as local fields model was introduced in 2015 by Wolfgang Lechner and Philipp Haukend Peter Zola so it's the LHSAT because it's just the first letters of those surnames and to show you how this works I want to demonstrate on this wall for a qubit example already see if they all the qubits are up and now in the LHSAT model I actually encode the parities between those qubits so if they're parallel they will get the value zero and if they're anti-parallel they get the value one in the LHSAT model and as I have six interactions I have here six qubits yeah each with a local field but as I have now I'm more qubits than on this logical model here on the left you need to implement constraints because now I can have configurations here in the physical model that do not translate back to the logical one so I want to show you this by flipping the top spin so here on top there's now a one now the question is how is the configuration on the left hand side in the logical model so I start with the decoding so we look at the first qubit here which tells me the parity between qubit zero and qubit one so it's supposed to be parallel so we can fix them on the left hand side to both up for example and then I look the second qubit it's also supposed to be parallel so it's also up but now the third qubit is supposed to be anti parallel to qubit zero so we've set it to down and if we go on now and look at the parity between qubit one and three which is supposed to be parallel in the physical model here but here you will see on the left they're already anti parallel the one is up and the one is down so we ran into a contradiction and yeah so we can't construct a valid logical state but as we can just throw this state away in the physical model it can give it an energy penalty because we are just interested in the ground state put is with the constraint so we have a constraint condition and the constraints you can see as this great triangle and this great diamond so the condition on this great diamond would be that the amount of zeros here of this four qubit should be even and as we have three so this is violated and yeah would get an energy shift to higher energies also if I would add one more qubit in the in the logical one this relates to like a full row in the let's add model so in fact the number of physical qubits square quadratically with the number of logical ones so you know also in the next slide so I see here the number of logical qubits on the x-axis and a number of possible states on the y-axis so we the green line corresponds now to the let's add model and the blue line to the logical one so we see that we have a great overhead in the physical model of this constrained violating states so states that never translate back to this logical one and of course we interested in the logical states because one of them is our ground state and this can actually now be a problem in QA OA which is short for quantum approximate optimization algorithm proposed by Farid Ali in 2014 just you can see here a schematic circuit how it works I mean we already heard it a few times today so we start with an initial state and then we apply our unitaries and P times so the unitaries you can see here on the right we have a driver term and a problem one unitary containing the problem Hamiltonian and yeah if would be would go to infinity we would reach the exact solution but yeah as we can't do that we get an approximate one then we can perform our measurements where the outcome depends on our first guess of this parameters better in gamma and with this we can evaluate our energy and hand it to the a classical optimizer which six to your minimize the energy so the optimizer can then return a new set of parameters to our quantum circuit and I repeat this till I reach a reach a come against criteria and now as I said here start with an initial state and in general this is a equal to a position of all the states but as I said in a physical model we will have a lot of this constraint violating states and as we can't apply this unitary infinite amount of time we will always have a high probability to measure this those states yeah which will never contribute anything to the solution so we will have a low performance and that's why we need a solution which I want to present to you now so here you see the adapted circuit like in particular always on qo and the let's set model we have one more unitary and this is because we split up our problem Hamiltonian into the local field term and into the constraint term but actually the new stuff now happens here at the measurement because here we also decode and we decode a physical state into logical state so we can hand a logical energy to the optimizer so we so to say good rid of our unphysical states but as I told you here we it's not easy to translate them back because we ran here between qubit and free already in this contradiction but actually before we read out qubit one free they actually had already a logical state and if we now look at this red line so the edges between those qubits we see that they actually cover all the nodes so all the qubits without making a loop so as soon as I made the loop I ran into this contradiction and that's actually the definition of a spanning tree and if I look at other spanning trees here we see that we can always decode a state so on the bottom you see the logical state where the arrows denote the spanning tree and on the top you see the physical model where the marked qubits translate and correspond to these interactions to the trees so I basically can just take those three qubits which corresponds to these lines and decode the state and in fact here for example on this first example on the left a fixed qubit number zero to up and look at the parities to yeah the other qubit so I can determine okay is the other qubit should it be parallel or not and like this I can decode and in a complete graph so in an ultra connected graph as I have here they're in total n to the power n minus two spanning trees possible so here I show four but as I have four qubits they're in total 16 so that would be other 12 spanning trees for the decoding also want you to see if we focus now again on this four big examples on the left that the decoded states they are not always the same so you see in the first one the third qubit number three has a downspin and the others two in the middle they always have upspins and this is because my physical state violates a constraint so if I have a constraint way violating state all these spanning trees could return and a different logical state but if all the constraint would be fulfilled all the spanning trees would give me the same logical state and yeah if this information I can show you how we construct now a new objective function on the bottom you see this physical state we want to decode and now we just consider physical qubits that correspond to a spanning tree in the logical model so we for example we take those three qubits and then we can construct our logical state like this but of course we can take more of them so for example we also take yeah a second one so we have two spanning trees and two logical states and then we just take the average energies of those states and to calculate them energy like this so in these examples we would just add those two energies and divide it by two and now I already want to show you our results so you can see here that on the x-axis we have the number of logical qubits and if I use if I have six logical qubits I also take six spanning trees for the decoding and on the y-axis see on the left plot we have the residual energy which is calculated like this so the measured energy minus the ground state energy and then the maximum minus the ground state energy like this and low energy value so the values go then from zero to one and a low value is preferable also on the right plot where I have one minus the success probability and the blue line you can see here corresponds to the parity QOA so you can see it really and rises fast to the value of one which is bad but with the decoding which is the orange line actually get really good results so we could improve it it's also better than the the green line which is the swap approach and I call it swap because in a logical model and it's done to implement this long range interactions with swap gates so I have to bring them together so I would swap those qubits so they are next to each other and then implement this interaction in this case it doesn't matter because I have an ideal circuit but now I introduce a deep polarizing noise to our two qubit gates so now it matters how many synods I use so because one swap gate consists of three synod gates here and in the LHZ model I have to implement my constraints with synod gates so a four qubit constraint would be implemented with six synods a three qubit constraint with four so I have more synod gates in my parity embedding and but still as you can see this orange line so the parity with the decoding already has a higher success probability that already with 10% noise it's as good as the one with the swap approach with zero noise but this is done with six logical qubits so 15 in the LHZ so exactly the size you can see here so I wanted to make a different comparison with modular parity QAOA so and this was actually published I think about two weeks ago from some people of our group and there you actually start from a constraint fulfilling state and then you apply a driver that only introduces transitions to other constraint fulfilling states so that I never leave my constraint fulfilling subspace to non-constrained fulfilling ones and they do this by introducing so-called driver lines so here we see the LHZ model and one line here that is driver line the purple one goes through all the qubits with the number of five and the orange one goes through all the qubits with four in it so that the lines and the driver term is just the sum of those lines like this our model is equivalent to the swap approach so this would all be equivalent that's why here with zero noise this bar here starts at the same point but as soon as I apply the noise we see the difference again I have more she-knots in the blue and approach in the parity then in the in the swap approach but I see when I use the decoding I'm very stable to noise so if this I can summarize it now I showed you how to do the decoding and to yeah construct the new objective function so we could improve parity QAOA with it and we also show better performance than the swap model at least with small system sizes but we are more also more stable and to noise if you use a modular parity QAOA so now I'm open for questions things actually have a question in slide number 10 can you please go to there this one yes so in the last place you are taking this EL alpha beta gamma and why did you come up with this formula what is the objective and because now we have because we have several logical state now that corresponds to one and physically one so then was the question which energy should we take so we could also take the minimum energy but as we just look at the small problem size we thought it's not such a nice comparison as well that's why decided first on this on the average and I actually also have another question that I actually had from yesterday why would somebody choose a parity qubit instead of a normal qubit and this is because as I am headed in the beginning that implementing these interactions especially this if qubits are far away and is not possible in a physical device so we are limited to the connectivity of this of the physical device and with a parity qubit I just need local fields and the qubits with the constraints I just can implement see not with with the neighbors so I'm very independent of the hardware use thank you yes so you explain how to how to get the energies from the many decoded states you get but how do you get the states you want out of the many decoded states and I can decide on the spanning trees I can take random ones I just have to fix an amount and if I have those as I said it on the spanning trees I have to decode them and get my collection so I just have to decide how which spanning trees they want and how many yes so how did you decide like which spanning tree you take as your solution and what I first did I have to like look at the performance for example on yeah how does it depend on how many spanning trees I take and then I chose when I have yeah six logical qubits then I chose actually the spanning trees as you can see here in this big example but just for a bigger with more qubits so I decided to because then I cover all the physical qubits and yeah I chose those spanning trees which we call also like the logic lines you read out the logic lines of it yeah okay so as you increase the number of spanning trees you ground state probability increases correct so as you increase the number of spanning trees you actually consider you get that you have more probability to actually get the ground state and yes but it will yeah stagnate at some point are there any other questions yes could you comment once again what is your initial state is it some pure state of your no I have an always an equal superposition of all the basis stage it's like like in quantum but is it a pure state of your more complicated Hamiltonian or it is some no of my of my physical device yeah of my physical configuration yeah and how does it look like it's do you have some formula how it looks like allowing all your constraints or it should not be just xxx like in a standard qa because you have constraints right yes yes but here I also consider the the constraint violating states I take all of them just not in the last slide I showed yes yeah but with this model on priority I can't tell you exactly so you start from some state which yeah it's which is not the which fulfills all the constraints yes mm-hmm okay are there any more questions if not we can thank


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