All right, thanks for staying with us down. The e-learning market is predicted to reach $375 billion by 2026. And like with most businesses, digital education is prospering and developing at an unprecedented rate as technology develops in Nigeria, opening the door for industry innovations and endless growth opportunities. Now, with the increase in demand for flexible and accessible learning solutions, digital education is set to play an integral role in shaping the future of education across the globe. So today we're asking, how can the business of digital education thrive in Nigeria? Now, please, let's hear what you have to say. Remember, if you can join the conversation, send us an SMS or WhatsApp to 08-1803-4663. So I want to bring our guest in a minute. I mean, you've explored a bit of digital learning. If you were to propose what would be an easy way to make it more receptive and a lot more enjoyable and all of that, what would be your things to consider? One of the major factors that I believe hinders e-learning or digital education in Nigeria, especially, is stable internet connection. Although now, with the advent of private network providers, the ETL, MTN, GLO, whatever, it's a lot easier because if you just pay for your own personal internet service, you can get this. But then if we were able to create a network that standardizes your amount of internet reception and whatever, it would be a lot easier for a lot of people. You don't have to have enough money to buy data before you can access e-learning platforms or websites. So that internet connection, I think, that's what's key, right? Yeah, that's what I would go for. All right, so let me bring in our guest, Fadal Al-Tazi, founded Nexford University in 2018 with a vision to enable greater social and economic mobility through high-quality affordable education. His vision is deemed from having built four startups employing over 500 people across 10 emerging markets. Fadal became an entrepreneur at 18 and has accumulated 20 years' experience founding digital marketing and SaaS startups, lean award-winning digital engagement and digital transformation projects. Cumulatively, his startups have raised over $35 million in growth capital and employ more than 500 people globally. Under New Group International, he launched and grew the social eyes from two to more than 120 people globally. He has worked with Fortune 500 global consumer brands such as Starbucks and government in the US, Europe and many regions. Fadal has lived in Cairo, Paris, Washington, D.C., Dubai and London. He holds a BB, rather, from the American University in Cairo and management degrees from George Washington and Middlesex University. And he's joined us live in studio. Hi, Fadal. I'm very impressive resume, I must say. So my son looked at you and when I said you had a startup at 18, do you want to walk us through that journey before we now delve into the business of digital education? What are you thinking at 18? It's questionable today in hindsight. I was in last year of high school. In hindsight, I think I was maybe lacking engagement. I was a bit bored in high school. The dot-com era was starting and I lived in Egypt at the time. Tourism is a big sector in Egypt. And I was thinking to myself, how come there's no website where you can go and check out what to do when you go to Egypt, which hotels to stay at, where to visit. It must be very difficult for a German guy or an American guy thinking of visiting Egypt, getting haggled on the roads. So I thought to myself, it would make sense to build a website that tourists can use to plan their trip before coming to Egypt. Awesome. And that was the business. That was the first business, yes. So how much did you earn from that business? Oh, zero.  Yeah, I thought back maybe about half of what we invested in it, but that's it.  But that was the start of our journey. Yes, yes. I mean, so we're having a conversation around digital education. Again, first of all, I want to say well done on NextFord. I know so many people that have gone through the program. And I mean, everybody says this is truly a breath of fresh air because up until NextFord, it seemed like most people that wanted to go abroad, I mean, that wanted to have a second degree would have to either travel abroad or they are stuck with some kind of unverified university certifications that they would do online and all of that. But I mean, this university came into Nigeria and it just showed that they were ready to be here. The presence, they came with food. I mean, I know since we started this partnership with NextFord, I've had like five people call me. So did they offer, what's it called, a PhD business? Did they offer the essays? Please just go on their website. I'm sure you'll be able to find a few things. And a lot of people have said, oh, I have, I've seen so many ads, but I was not sure. I mean, but when I saw them when they all said, I said, okay, yes, I think there's a credit. I mean, the brand was really, really aggressive. I mean, a lot of people actually brought into that vision. I mean, why did you choose digital education, first of all, and what truly was the end goal for you starting up, what's it called, NextFord? Yeah. So why I chose education in the first place is a desire to build a business that delivers social impact. So being an entrepreneur for a number of years, I just found myself a lot more interested in building companies that actually deliver positive impact in addition to financial rewards. So when my co-founder and I were thinking about education a few years ago, it was stemmed on this belief that lack of education is the root cause of most, if not all, world challenges. So we thought to ourselves, how can we start to address some of the challenges that exist in the sector? Of course, our initial thinking wasn't to build a university, to be clear, right? When people are thinking about building a startup, you don't really think of building a startup university. It's a concept that sort of doesn't exist. Is it a startup or is it a university? The two together is a bit unusual. But one thing led to another. We kept on researching and analyzing the industry more and more, and it became clear to us that disruption has to come from within in the sense that it's not enough to build technology and try to sell that technology to universities in order for them to address their core challenges. We would actually have to go direct the consumer and deliver an entire experience from A to Z. Because traditional universities are just, I hate to say it, but most of them are extremely bureaucratic and such deep-rooted, with such deep-rooted history and policies and resistance, administrative resistance. So to get them to change is extremely difficult. So it would have been easier for us to sell them technology to address the problems they have, but unfortunately that became clear to us that route would be very, very difficult. So ultimately, the desire was positive impact. And that stems from, like I said, the personal belief that lack of education is the root cause of most of all challenges. And personally, I think one's duty in life is to leave the world a slightly better place than you found it in whatever capacity. So that's what I'm doing. Absolutely. For, you said something about administrative policies being hindrances to students that plan to get a higher education of whatever form for themselves. I think I'm actually a personal, let's say, victim to this because when I was applying to universities, was it last year or early this year, it was so rough because I know that academically, extracurricularly, I'm capable of getting into the best universities in the world if I actually put my mind towards it. But then because of their own priorities that have sort of shifted from the fundamental thing, which is to provide education to other things, like giving people who have legacies and missions or giving scholarships to people because they come from disadvantaged backgrounds. It's like their priorities have shifted. So when I see an organization like Nextword, I am actually pushed to believe in the concept of education again because it's like all hope isn't lost with the world and all that. I appreciate that. I think hope isn't lost for sure. But to your point, I mean, when we were thinking about how to address the challenge, this was back in 2018. To your point, we surveyed students around the world, from India to China to America and Nigeria, Philippines all over the world. And we asked them one question. So we said, what is the primary reason you go to university? And then we took that same question and we asked it to administrators across universities. So we asked them, what is the primary reason you exist as a university? And so the vast majority of students told us is to get a job. But then the vast majority of administrators told us a very different answer. They said, you have to help people in life to shape the future of the world, to find research. So there are a whole range of reasons that we realize none of which are aligned to actual student interests. So if you accomplish all these things, still you're not better off as a graduate. So it showed us what we then end up as this disconnect between the buyer and the seller. The buyer is buying a service expecting it to deliver something, but the seller actually has no intent to deliver that. They intend to deliver something that they have your personal agendas. Yeah. That's interesting. So I mean, there's a business in this market, right? First of all, I don't know how much of the, the, what's it called? The business model was, you know, because I mean with next one, it's quite simple. Anybody can afford it. As long as you're able to plan, if you have a monthly income, you can just set something aside and just pay for your costs and all of that, right? Was that intentional? You know, was that the structure of the business that you wanted to be able to reach everyone, you know, with next? Yeah, 100%. There was a core pillar. So we have two pillars at next, but everything we do must adhere to these two pillars. You know, one is affordability and the second is a focus on business and technology related disciplines. So the idea was to deliver impact to the largest number of people possible. We could charge double the tuition and have half the number of people afford next third, but the business model is designed from scratch for the maximum affordability to the largest number of people in the world. And that's where the impact angle comes in. And again, it's one of our fundamental differences or disagreements with some of the traditional providers who have to say historically have been limited by size of classrooms. So, you know, they just had limited capacity so they had to adhere to that. In an online environment, it's a bit different so the pricing structure can be different as well. But yeah, 100%. I mean, we want to essentially render things like physical location, gender, race, ethnicity, all to be no longer really inhibit one's ability to move forward in life. So these should no longer be barriers for your ability to move forward in your career. And this was not specific to emerging markets. Is this the general standard across anywhere next what is able to play? Our pricing is globally affordable. So we are, I would say, the first American university designed from scratch for a global audience. So in the US, our pricing will be extremely affordable as well, but it's designed for a global audience. The pricing changes by market. So what an ideal will pay is different from what a Chinese will pay or someone in the US. But in each market, pricing is localized in order to be locally affordable to that market. Awesome, awesome. All right, so we'll take a very short break now. When we come back from that break, we'll continue the conversation. Stay with us, we'll be right back. All right, thanks for staying with us now. If you're just tuned in, we're discussing the business of digital education with Fadel Altazi. I forgot the name correctly. Now please let us hear what you have to say. Remember, you can join the conversation, send us an SMS or what's up this week. 1-803-4663. I mean, this conversation is getting really interesting. So there's a business part of education, right? And what I hear you say is you literally can, you actually have the capacity to take on the entire market because you don't have any limitations like maybe structural limitations and all of that. All you just need to do is continue to expand the capacity of the number of students that your platform can accommodate. So what's the plan for next forward? So we have no physical restrictions as you indicated, but we still need to restore this appropriately because we maintain sort of a ratio between advisors and faculty and the students or the learners that we enroll. That way we ensure that learners get the support that they need and they get it rapidly, right? So we want to make sure that the experience that they have is an optimized experience. Having said that, yeah, we intend to enroll the tens of thousands of learners over the years. So I would say a much larger scale than any traditional or any physical university in Nigeria or even in other countries. So I would say over the coming five years, I would say we aim to enroll in at least 100,000 learners. So the business is designed for scale. For us, we achieve that scale through a combination of product expansion and geographic expansion. So as we start attracting a larger audience from a larger number of markets, as well as launching additional programs, be that undergraduate degrees or graduate or shorter programs, like we recently launched a number of shorter certificates, ranging from three to six month programs. So when you see geography now, how many locations are we looking at that we are currently operating from? Today I would say, let's not operate it from really because we're hungry for it. Yeah, you can do only the book. In terms of, like, you're a community. I would say today I believe we're at over 90 countries. There might be over 100, so many countries, yeah. Awesome, awesome. Alpha, you had a question. This is all quite crazy. The thing that you said at the beginning about you starting, this is at 18, it has been ringing in my head since you first said it. And I'm like, so it can't just be border magus For people that take those kinds of bold decisions, there has to be something literally inside you that drives that. It means that now, literally next year, I could say I want to become an entrepreneur and I'll begin to do everything that I need to to get to that stage. So I'd like to ask what exactly was your frame of mind? Like if you could inspire someone who was in a similar position as you to take that bold step to try to build something for themselves, how would you? Are you 17? I'm 17, yeah. I'll give you the answer you're probably not expecting. It might not be so inspirational. Honestly, if I were to look back, I don't think I would redo it the same way. Okay. I think for me at least I was really in a rush to start working. Frankly, I didn't need to necessarily, but I wouldn't rush to be more independent, to be intellectually stimulated. Starts your life in a real world. Yeah, I just really start applying. Like I remember I would go to college in a suit at one point the security guard would think I was a professor so they'd call me like doctor. And I remember one of our clients once called our office and then they told him that he's at university. So then the guy called me the next day. He said, I'm so sorry, I've sort of been calling you doctor father all this time. I didn't realize you were a professor. I was actually in class, but having said that, like I missed out obviously on college life. But maybe to help if folks insist on going down that journey or determined on going down the journey. I think the part that most founders miss is actually the self reflection part. So that would be the part that I would really advise to begin with. Self reflection in the sense of thinking about the why, like why do I wanna start this business? And really trying to understand what it entails to actually build a business from scratch. Cause unfortunately people spend too much time on Instagram and Twitter and so forth. And therefore they're inspired by the glamorous life that we see on Instagram of this company became a billion dollar company. This other company raised 100 million and so forth. But the reality is 99.9% of other companies are failing, right? Of startups in general. So we're not seeing these stories. And I think a large part of why many of them fail is that lack of thoughtful approach from the beginning. Self reflection part. Exactly. So self reflection thing, why do I wanna do this? And really making sure that I might prepare to go on this very, very tough journey. It's not a journey that's gonna be glamorous, at least not for the first few years. Yeah. Awesome. So I want to take you back to what Alpha talked about when we started the conversation on the hindrances, right? When it comes to detailed education. It's a huge problem, right? Data affordability, then also even the rich, right?  It's not even now. It's not this question of affordability. There are some places that you go to, it's not even available, right? So I mean, for someone that is in the business of making sure that people are online and are able to learn online, what are you doing in the regard of ensuring that some of these emerging markets have access to not just data, but at least affordable data that can ensure that they continue to learn without having to stop their education. Yeah. So a few years ago here in Nigeria, we did a partnership with MTN specifically for this purpose to subsidize data costs for learners while they're enrolled at Nexford. It's not live right now, but it's something that we're looking to revive with a number of telcos. We did a similar one in other markets like in Indonesia with a telecom company called Indosat. So we try to work on these partnerships with telecom companies to subsidize data costs for learners throughout their enrollment periods. But from a design perspective, more importantly, we try to design the product itself in a way that's quote unquote data friendly. So it doesn't consume. Not heavy. Yes, exactly. So I remember in the beginning, especially in the US when we were building our team and people were talking about virtual reality and augmented reality and all these bells and whistles. In reality, these are very data heavy applications. So we decided not to go down that route and build a product that's more data friendly. But the good news is we don't need to worry about it too much because the world's biggest companies are solving this problem. Yeah, they're catching up. Yeah, they're catching up and we're fortunate enough that- To just align. I can't believe you're like, we can ride the wave and we'll be fine. Just ride the wave. Yeah, from Google to Apple or to Amazon. If people aren't connected, these companies can survive. So thankfully, billions of dollars are being invested in connectivity. And this is only a matter of time that everyone's gonna be connected. Awesome, awesome. Also, you're in Nigeria, right? I hear there's a graduation happening this weekend. You wanna tell us a little bit about that? There is, there's a graduation happening tomorrow at Landmark. We hold a graduation here every year. It's predominantly focused on Nigerian graduates who aren't able to travel to the US either for economic reasons or visas are too difficult to get. So we're expecting, I'd say about a thousand people there. We have basically three events happening tomorrow. We have the graduation ceremony with a number of great speakers followed by a career fair where we have a number of employers who are actively hiring. We're gonna be there meeting our graduates. And then we have a number of masterclasses happening as well in the spirit of continuous education. So right after they graduate, they have to come back to masterclasses. Number of industry speakers are gonna be there. We actually also have a startup competition happening tomorrow with a number of our founder graduates who are gonna be pitching to VCEs and getting live feedback at the event. Awesome, awesome. I mean, because I was just gonna ask about, you know, outside of the learning because the traditional learning is you go to school and you're done, right? What was the added advantage? So what is the unique proposition that, you know, you're offering that would make someone say, you know, I think I'll go with this people. I mean, you've just answered it because the biggest, one of the biggest challenge that we have, I mean, I hear that the unemployment rate right now is really huge, right? I mean, if you have 70% of our population, our youthful population and a good number of that population, they are on, some of them are unemployed, some of them are unemployable, right?  So then we're also putting together, you know, a team of people that would come and say, you know, I am looking and I'm trying to hire, that's a complete value chain, you know, that's adding value to, you know, the learner. Why did you do that? Well, like you said, it's about adding value. Ultimately, we don't measure our success by enrollment, right, or by growth. We measure our success through the success of our graduates. So that is by far the number one priority and expert. We think of a metric called basic as ROI. So we target for our graduates to get a three to five X return on their own education investment within three to five years of graduation. So you're investing in this education to get a better job, to build your business, to get a promotion, to switch jobs. Millions of jobs are gonna disappear over the coming years as a result of technology and AI and so forth. So we wanna be the folks who are preparing people for the skills that they need and events like tomorrow are an example of how we built community and how we add value to that community. And beyond graduation, it's important that we continue linking people with employers. It's important that we put people in front of, you know, coaches and mentors who can continue because, you know, education is lifelong today. So it's not about going to college for a year or two or three or four, you know, it's about learning how to learn. So we intentionally designed the master classes to happen tomorrow right after graduation as sort of a demonstration of that. You graduated, but that doesn't mean learning is finished, right? You need to continue learning for the rest of your life. Awesome, you're only coming, because I just wanted to touch on something. So for the startups, right, I put them in front of VCs. So how do you ensure that, you know, that that startup is sustained, you know? Are there like more things that you do with the startups outside of just putting them in front of VCs? Do you have like maybe structures that sets up a kind of what's it called monitoring process for the startups to continue to grow? Yeah, we don't right now. So I will pretty much ends when they graduate and when we have events like the one we're putting together tomorrow. However, in early next year, we're going to be launching a dedicated entrepreneurship program specifically. And that's going to have, I would say, an extension beyond graduation along the lines of like an accelerator slash incubator in partnership with a number of VCs. And we don't want to cross the line to pretend to be experts in all industries. So we're not well-equipped as an organization to incubate a startup. But we are well-equipped to prepare them with the skills that they need and put them on the path that then connects them with the next supporter throughout the startup journey. Awesome, awesome. Yeah. I thought you had a question. No, I don't know. You are just learning, all right. So I mean, if you have something to say to anyone listening now today, what would you say to that person? I mean, the person is thinking, should I, should I not, you know, and all of that. What would you say to that person? You know, I would say, unfortunately, Japan's sort of top of mind when I'm here, right? We hear of this issue every day and it's draining the country. Operators, did you just call Japan as Japan? Yes. Is Japan? Japan. Yeah, yeah, yeah. So I would say, you know, an investment in education is the best investment one can make in life. In investment in your own education. So whether it's next to anyone else, that's not the point. But the point is you need to forge your own future and by thinking, should I or should I not, as in like, should I, you know, invest in education? The answer is always yes. The form can differ and the time can differ and the program can differ. The second part of that I would say is, don't invest in education to be what I call a credential hoarder, where you just gather credentials to hang on your wall. What really matters is what have you actually learned and how you're gonna apply that. So that's what I would say, I mean, you must invest, it's not a question that one can ask, you must invest in educating yourself. But really think about why am I doing this again to my earlier point and how am I going to apply it? And to someone that is thinking of starting something in the line of educated people as well, what would you say to those people? APEC is a very, very challenging industry. If you're trying to sell two universities, it's a very, very long sales process. If you're trying to sell direct to consumer, you know, it has its advantages and its challenges. But I would say like, there's still tons and tons of opportunities to be addressed. I suppose if I had to choose one, I would say, you know, do really good research to make sure that what you're building solves a real problem. This is a challenge I see with a lot of founders. They build a product that they think people want, but doesn't actually solve a problem. So it's sort of nice to have. And very few people are willing to pay for things that are nice to have in an economic climate like Nigeria. So make sure that you're solving a real problem that people recognize they have, not that you think they have. I was actually, I'm taking this online course right now from Coursera, it's Google's UX design, user experience. And then literally what you just said now about you not projecting your own imaginations of what users experience as challenges to them. You actually have to gather their own problems, gather data to see whether they're actually going through the problems that you think they are going through before you try to solve them. Okay, so should I say one more final? No, I have a question. So yesterday we talked about the challenges in Nigeria, right? How, I mean, one of our uncles said that education is no longer relevant. And I'm sure you're going to strongly disagree with that. But she said education is no longer relevant that as far as she's concerned, let's fill up the skill gap and find a way to solve the problem. So we have a lot of problems in Nigeria, infrastructure problems, so many problems, right? And I strongly agree with you that education can be one of the biggest drivers to kind of solve this problem. So I mean, if you really look at the landscape of Nigeria, it's quite unique, right? We have a lot of things that are available, but at the same time we have a lot of challenges that do not make those things accessible, right? So if you were to look at the challenge that we're having, especially in the skill gap, right? How do we tailor? Because the educational structure around skills, it's a bit tricky, right? We don't have like proper, yeah. It's not standard. It's not standardized and all of that. And that way, when these graduates come out or when these people are in the workforce or in the market, they are not able to earn a decent living, you know? So I mean, we're talking about how someone was imported to come and tell, like put into locking stones on the ground. Yeah, I mean, when they're building in this country, for instance, I mean, we have a lot of infrastructural challenges. Why do we have to import? So there's a huge skill gap, right? And I believe that if we have an educational body that understands the skill gap and is able to close it. So imagine you're coming out as a graduate in plumbing, for instance, right? You come out as a graduate in plumbing. You will immediately, first of all, find something to do, immediate earning, and at the same time, your level of entry, market entry, in terms of earnings and all of that would definitely not be what somebody on the roadside is doing. So do you see what's called the edgy tech able to solve this major skill gap in Nigeria? I think 100% ed tech is gonna help address this challenge, which as you indicated, is a huge challenge. And ironically, despite unemployment being so high, you know, every executive of every large organization we work with in Nigeria, sites hire and qualify people as one of their top two challenges. Absolutely. So it sounds counterintuitive, but anyway, I tell this, outside of Africa, it's of course not, there's so much high unemployment, there's tons of people. But no, there is no unemployment, yes. So it's a huge, huge challenge. Honestly, I think the solution, it could take us a very long time to talk about, but it stems really in two things. I think one is cultural, there needs to be a cultural shift from looking at education as a transactional sort of document that I wanna hang on my wall. Because so long as you're looking at it as just a credential, I wanna hang on my wall, you're not really thinking about the value of that. What am I actually learning from this? What you can do with it. What am I gonna do with it? So therefore that impacts your choice. Like when you're buying a car, you're looking at, okay, is this car gonna take me from point A to point B? Absolutely. The car looks amazing, but it's not gonna take you to point B and you're not gonna buy it. Because you're buying it to go from point A to point B. So I think people need to think about what am I gonna learn here and choose based on that. And the second is unfortunately regulation. I think regulation is choking the sector here and in many other countries. So we need I think a massive amount of deregulation and letting a market economy sort of create supply and demand and therefore the high quality places will survive. The poor quality will not survive, right? If you know that by going to institution X, you will fail, why would you go there? Ultimately becomes redundant and it's canceled. But there are artificial factors in the regulatory system here in other markets that point people in specific directions where they need to go to one of maybe 10 institutions. Even though these institutions have no record of actually positive graduate outcomes. Oh, look, you have a comment. Quite interesting. Yeah. Go ahead. My comment says, OJNZ, you're partially welcome. When are you all coming back on the show? You were to advertise for just about a week then disappear. Why now? We really miss you all. Mr. Dimiji from Aja. We miss you guys too. Today was actually, today was a very interesting day because I think one of two of the Kauankas are supposed to be here. Unfortunately, it couldn't make it. Yes, so he bailed me out. I mean, it literally just happened to be in the studio. So that's the only reason why I'm here. All of my friends, they have gone to different places. But anyway, we're coming back, right? We'll definitely come back. But thank you so much for the help. For the help. For the help. I will go to Cairo because of you. I'll go and learn the language. But thank you so much. I think we had an amazing conversation. And in all honesty, I really, really am routine that we're able to solve a huge gap, and which is the fact that we start to change the numbers of unemployable young people. Yes, it's one thing for you to shout that there are no jobs or whatever. But people actually have jobs, but there are no skilled people to be able to take on those jobs. So it would really be nice to take the conversation further. I have some ideas for education in the media space. So we'll have the conversation behind this scene. But thank you so much. I think we had an amazing conversation. Alpha, thank you so much for taking our time to be with me. Now, before we go, I'll show you followers across all social media handers that wish you Africa. You can interact with us further, drop a comment, or more importantly, follow all our engagements on social media, like share and invite your families and friends to watch and follow the conversation. Now, if you missed our quote for today, here it is again. It says, where is my quote? Where is my quote? It's the second quote. It says, we can close the gap and improve what happens in the classroom by using educational technology that is the same high quality everywhere. We'll see you guys at 8 PM on Monday as we bring another great conversation to your screeners, Independence Month and Celebration Day. We'll see you guys on Monday. Enjoy.


This afternoon session, so I know that the lunch was a little bit short, so probably it's people arriving But in order not to delay the whole program, so it's a pleasure to have Ludwig Matei with us, and please go ahead Thank you so much Thank you so much. I would like to thank the organizers for setting up this beautiful meeting sort of in this beautiful location first time for me here, and I'm blown away sort of how pretty this is and I would like to share with you some of our recent thoughts centered around Optimal control with regards to implementing quantum algorithms So here's a brief overview. I want to take you this as a starting point our study that was centered around sort of mitigating baron plateaus that occurs in this context and then go on to this This algorithm that we that we are proposing to use which is this pulse engineering via Projection of response functions pepper as we call it and briefly mentioned here I'll be maybe a bit more detailed or so and This year is our experimental collaboration with a group of Chris of Becker and Klaus Langstock and their team We are sort of we utilize these type of ideas in the context of implementing Quantum gates in the context of rootback tweezer system Then I would like to share with you some thoughts about our method called or car sort of exclusive or quantum algebra and Time permitting which I probably won't have a so I might say a few words about This kind of quantum simulation of dynamical RG in the context of the dynamic BKT transition This is done together with a team of Chris foot and which is really a joy to work with them there All right, so let's get started If this works if it lets me Okay, I need to say okay, so let's start out with this here as a as a starting point this is done together with Lucas perures who deserves a lot of credit for this nice work You can take sort of this and I should also say So Some of the slides that I've seen here so far absolutely gorgeous So kind of these are some of these are still in the phase of development or so They're not quite as pretty as what I've seen this morning. For example, that's partially this is my excuse now That's partially because the preprints on here pepper and orca. They just came out today and yesterday So I'm still sort of trying but I was just too excited to share this with you guys That's why kind of some of the things might look a little bit sort of in a phase of growth. Let's say Okay, so this is what one might Think of if one if you see a quantum circuit in this kind of paradigm of discrete quantum computing you sort of set up your Quantum algorithm composed of individual quantum gaze all the usual classics here had a mass and x-gaze and see not case and so on And this is a very important and conceptually important sort of way of Depicting this it is of conceptually transparent because you kind of break down this complicated unitary into these kind of components And it was also an environment where you can make statements more easily Let's say about scaling because if you now imagine you write down a sequence of quantum circuits with Increasing number of qubits then you can at least kind of sort of make a sort of well-defined sequence of that and analyze sort of the asymptotic behavior the complexity of this of this Of this algebra So there are sort of if you if you take this too literal those sort of you might end up sort of With some challenges like for example, you might have some idle time and sort of might be an experimentally not ideal Realizations and this slide is supposed to illustrate this if you take this here small two qubit circuit like it is shown here You imagine you have a Hamiltonian composed of Some parts of the Hamiltonian that you can control and you have some parameters That's your experimental friend can implement in the lab Then if you take this literally you might sort of end up with a sequential Realization like it is shown here you make some for examples on pi over two parts here You have some sort of interacting pulse here and another power to pulse or so But this really doesn't make full usage of the platform that might that you might be working with You are in the larger space and you might be able to implement something like this here naturally We just can just in parallel run all of your control protocols all of your control Operators here sort of at the same time and now you can ask the question Is there like a is there a better more efficient implement implementation of a desired or transformation in this larger space? So that is this large kind of multi-parameter optimization problem that occurs in this context So we use some like many others we use some Optimization algorithm which I only flash here.  You have some type of loss function For example the fidelity Compared to the desired state that the system should have after this time propagation and you take some time Do some sort of gradient descent type of approach this all leads to some type of grape and grape like sort of Algorithms and some methodologies in this context Okay, so I'm being a little bit vague what exactly we did here because I want to draw the attention to a specific point If we recall that our Hamiltonian might be of the following form here. You have a couple of Time-dependent functions here these theta J's they could be rabi like pulses. They could be magnetic fields There could be all sorts of free parameters that you might have in your in your platform And now you want to make sort of you a usable set a finite usable set of parameters out of this in order to constrain this infinitely dimension a dimensional space here and So one typical setup might be you use this kind of stepwise parameterization You break up the time interval that you give yourself into these kind of step functions And then the the pre-factor of that step function that is now one of your parameters And this is how you might sort of represent this kind of functional space by a finite number of parameters And this you also has something to do with kind of variational quantum algorithms where you have a bit of a sequential realization of operations in time What we tried out sort of and what we were intrigued by is what if you go to Fourier space now? You have a temporally non-local Parametrization of these theta functions They all overlap like it is shown here and really sort of we're looking at this at this larger space That you go away from this kind of stepwise Parametrization to watch this temporally non-local representations like it is shown here Okay, so we gave then with this in mind sort of we gave ourselves a computational task This is I think by now sort of I've seen variations of this in the number of papers Which is the following point here So if you write yourself down some quantum ising model like it is shown here. It has a transverse field Magnetic field formulae of a B X and a B Y term So it has two components and it has the ising coupling between neighboring qubits then sort of these fields together here these Control functions together make up the space of thetas Those are your this contain your trainable parameters and then you give yourself the task to Find the minimal energy of the ground set energy of some random Hamiltonian here So if you pick your Hamiltonian you pick them out of this time interval and now you want to minimize this energy by taking this Hamiltonian propagating them over a Time interval of time one sort of and think of this youth theta zero as your estimate of the ground state of Your system, so that is the computational test that we sort of gave ourselves there and here without sort of Trying to map out Too much about scaling and so on sort of let's just see how these two ways of Parametrizing your theta functions how they compare so here for example We take four qubits and in each case we saw we show you here three Optimizations as a function of the learning iterations like it is shown here. This is here for four qubits This is for six qubits. This is the energy difference compared to the exact ground state energy and sort of the first impression So this really does more like a practitioner's type of sort of impression that one has is you can see that in the stepwise optimization You keep getting stuck in these local minima here Where as kind of in these temporarily non-local in these Fourier representation you see sort of an improved behavior Simply on this practical level here So you find sort of just without sort of mapping this out in a quantitative way You find this sort of improved convergence by using a Fourier parametrization of these type of of this Of the control fields of the control functions that you're using You can also kind of scale this as a number of the qubit number here So I should mention here sort of if you look at this space of theta you have to cotton strain something right because if you For example if you allow sort of yourself a finite time interval But allow yourself to that the energy scales of your control fields of your control functions have an infinite Amplitude you can realize can and as long as your universal You can sort of realize any unitary arbitrarily close So you need to in some sense constrain the maximum of the of the allowed space of functions that you are including It is also kind of an experimentally sort of relevant constrained Because you might have just a maximum of the Rabi frequency that you can implement You might have a maximum magnetic field that you are using and so on and so on So this leads then here to this theta max, which is a global maximum of all the control function combined And basically here the punch line is said like if you set this constraint Too small then the system doesn't really find good improvement or so and here I should say kind of here We are now looking for these baron plateaus And we're looking at the variance of one of the gradients of the of the energy With regards to one of the control parameters this you can also define in different ways You could define that the global variance if you like here We focus on this one quantity if you are sort of if you give yourself Too small of a global constraints on the control parameters Then you don't see sort of good convergence So you want to be sort of around here sort of where you allow yourself enough control of the system And what people refer to as baron plateaus is that as you scale up the number of qubits Here from 2 to 8 that you see here equally spaced magnitudes Indicating an exponential suppression of the gradient of the of your energy Of your loss function like of your error of your fidelity function if you like so of your loss function, right? And this seems to be reduced in our opinion. Yes You mean the the number Okay, so so basically so you so the steps to imagine is So this year was one of the magnetic fields So for example, if we go to this parameterization You pick out one of these guys here and now sort of you look at the second order derivative of that of the total energy of the final state with regards to this Parameter that is what this quantity implies so Okay, so so you do that you do an ensemble average over the initializations also you don't like so you know Or kind of you can equally sort of do like an ensemble average over sort of your update steps your optimization steps All right, so that's what the okay. So that's the variant. Yeah, exactly. So that's the distribution that you're that you're I should Yeah, so and this behavior seems to be reduced for this Fourier for the Fourier Representation here sort of you still find a reduction, but it seems to be sub exponential We are sort of given the number of cubits kind of we are motivated and we are working on sort of improving this I'm pushing this to larger numbers So that's why at this point in time we be considered an indication like or candidate for better plateau mitigation but so in the sense that up to this qubit number we find that sort of the The reduction of the variance is sort of sub exponential That is like a slower reduction of the of the emergence of your barren platoes You can kind of imagine why this is a problem, right? So if you're your error landscape your lost landscape becomes very flat and the system doesn't know sort of how to optimize anymore because the gradient is sort of So strongly suppressed especially for growing numbers of cubits where you would where you would hope you get at least some type of Hint from the system like how to how to implement your your your algorithm Okay, so this is kind of where I want to leave leave it for the moment For the about the barren plateaus sort of we tried out this kind of alternative way of Parameterizing your sort of control functions Specifically stepwise versus this Fourier parameterization and there seems to be sort of a tendency to mitigate sort of the spare emergence of barren plateaus Okay, so this is kind of the Playground on which we wanted to expand here So that's kind of remind ourselves a little bit of these properties of optimal control in this context with the purpose of having a high fidelity of a Given quantum transformation So basically this is a typical setup here, right? You have your Hamiltonian it contains maybe a part that is not controllable and then it has here these control terms Composed out of the control functions and the control operators These operators be J here and you have a certain number they're given sort of the setup that you're considering And then this control function we break up into these control parameters these pre factors and these and these mode functions that we Consider such as in the previous case the stepwise Parameterization here that so the mode functions there with these kind of steps and All sort of the Fourier mode the sine functions would also be sort of would be an alternative to that and But it could really be any type of expansion to use here Okay, and then Okay, so they can then like a typical sort of optimization task is you choose some initial state at some time to zero Then you do two things with it you propagate it in time with your Hamiltonian and then Is one thing so a row of TS or you compare you generate the target state by applying the target operation to your initial state And then you take the fidelity of this you see how well are you doing how close to the desired state that you end up And then you optimize this iteratively, right?  This is the loss function, which in our case is just the fidelity You you shift them a little bit run your time evolution again and take this difference quotient here as an estimate for the gradient with regards to that parameter and Then sort of you do this grape up update by shifting your Theta's your trainable parameters by this gradient times some learning rate Learning rate alpha alpha grape in this case. So here sort of on this slide You see already to hyper hyper parameters You have the magnitude of epsilon here, which we'll come back to in a moment and you have this learning rate here Alpha in this case, right? Okay, so again, there's of course a number of flavors like all of these Different these different algorithms that differ in the details and the implementation and all of that But this is I think a fairly representative kind of Key step of this algorithm. Okay. I want to bring into this Discussion now the notion of response function like an all-time classics and physics. You imagine you have some system Can be anything can be atoms solids, whatever you have and you apply a probing term to it of this form here You have a generalized force f of t and it couples to some to some operator B in your system And now you pick yourself an observable that you're interested in in your system a and you ask How much is this observable changed due to this probing term? So you pick the B you pick the a in principle You have this time dependence f of t and the statement of linear response theory is that you take this Convolution of this external force here with the response function with a linear susceptibility Chi a B depending on these times here t and t prime which is essentially this commutator of the Two operators a and B in the interaction picture Okay, so this is this classic like you all know all of these kind of here connectivity Polarizability magnetic susceptibility stairs physics is kind of full of this. So it's a very useful kind of concept Okay, let's take this concept is and apply it to this optimization test that we just looked at so the way to think about it is You we have these control operators BJ over here And now we take one of these guys here and we apply this type of probe term to it We have minus epsilon. Here's the delta function at a random time t r And this is this is how we now probe it. This is now what we probe the system with So you have two random choices here that you make in the algorithm that we're using there You pick randomly one of these NB Operators and you pick a random time on your time interval here In other words our generalized force here's of the form epsilon times delta of t minus t r So then sort of in our formula We have this you propagate now this guy sort of up to the time t r and the observable that we're looking at Is the following it's really the roll star It's kind of the you take the initial state and you apply to it the target Operator the time for example see not gate or what have you sort of that's our roll star This is now place the role of the observable which we then also have to propagate into the interaction picture So here's like a bit of a longer formula. I wish I could say I'm sorry But I really just want to discuss it with you guys All right, so I'm like, but it'll be over soon. I say I don't okay So um, so if I take now this formula that I showed you there Let's just throw everything in there that we just set up We have here sort of some pre-factor the theta function doesn't matter anymore because like t r is always smaller than tf and what we have here is here's our initial state because that's what we need to trace over we propagate our probing operator forward in time to the random time Then we propagate our observable to this time sort of and you can take this entire structure use the magic of Taking traces and shifting around unitary operators and you can formulate the whole thing slightly differently Which looks like so you take the initial state Propagate it to the random time take the commutator Propagate it all the way to the rest project it on the target state this object here Kai J is equal to the Graded it's an exact expression for the f over epsilon like so you can calculate this object in a single run You don't need to have two runs, but have just a single run and it's also a hyper parameter free Representation of this gradient. So these hyper parameters in in a practical setting always I always feel like they're kind of a pain because in principle you have to scan over them and look for the optimal hyper parameter in order sort of Basically, that's another optimization test You're saving this here by having the exact gradient the exact gradient also implies that you're always walking in your gradient ascent type method you're always walking up the The mountain in the right direction and not within not within approximated gradient, right? So that's actually a limiting factor of doing grape the way I described it to you earlier Okay, so we have now this gradient like we we probe of him we probe our system We perturb our system with this operator BJ at the time TR. What do we do with this? You take this object here and we expand now this delta function in the mode functions of your Control operator. These are the mode functions f j k of t and these are now the projection coefficients of those mode functions onto the delta function And this then taken together leads to this update step That we propose to use as I'm you take these sitar's and you take the Response function at this time TR times the projection of these mode functions times on learning rate alpha zero Okay, so this can be then sort of summarized into our pepper update which looks like it is shown here sort of I'm just summarizing this here What I so described to you before here's our time interval We are and you proceed as follows you pick your random initial state you propagate it forward to the randomly chosen time PR Take the commutator and propagate it all the way to the final time Projected on the target state take the fidelity of that This gives you chi of j and then multiply your chi of j times the projection of the mode functions onto this onto this Onto the delta function here and this is how you update the corresponding parameters, okay? So that's the algorithm That we proposed and that we tried this out here in these this kind of two qubit example So here we wrote down ourselves as two qubits. We have an H x sigma x H y sigma y So this year some type of Rabi control for each of the cubits individually And we have some type of Heisenberg coupling between these two qubits. We chose ourselves a sort of a target Operation which does the C not gate and here we work then again with our sine functions So all these all of these three functions here This H x the two H x and the J here are written in this kind of sine function expansion with a finite number of modes here So the mode functions take this form and the projection on Onto the delta function take this form here So this results in this context in this type of update here The theta JK is then updated by this quantity here once we have determined the response function for a given time step So this is how this then compares So here we have our our pepper Trajectories and these are the grape trajectories here's the number of runs and this is the log of the Of the infidel sources is the mean log of the infidelity of our realization Like perfect fidelity would be minus infinity And here I should also say so our this pepper method here has this kind of Fast convergence and this floor here is determined by the numerical Accuracy of your ODE silver you can shift this around or so pick like Some others are like a lower fidelity and this one to one determines this lower floor here so indicating that there's nothing else like that you're doing wrong other than sort of the In light and the inaccuracy of the ODE silver for great sort of you see this This kind of shift here. So we picked here. We sort of tried to be challenged our method by Doing everything in favor of grape here. We took we took also this kind of multi-parameter update like it is shown here and we are showing here a bit. It's a bit of a It's more like a guide to the eye than a useful quantity But we took we show here the mean log The the the mean log sort of of these trajectories which actually favors low infidelities If you do it the other way around mean infidelity Then for example this blue line would sort of all be all all the way up here because you have here these There's a large number of trajectories that get stuck in this bad minima here and they're basically overall sort of the impression I want that we're getting is that essentially all blue curves lie essentially above all orange lies So in a given setup if you just have the two methods set up and you run one and the other one You'll always find like it that an individual trajectory Does a better performance for this kind of pepper update? Okay, so here just for the fun of it. This is what you find then for these optimal protocols for For for pepper and you can sort of visualize then the motion of the cubits here on the on the block sphere And it does exactly this kind of see-not desires, you know, okay There's what I can illustration of so so listen guys. What did you find actually, right? So, okay? Okay, so this is our conclusions on this method so we The ingredients that we have in our algorithm is that you choose this Random time random operator one of the control operators and you determine the response of the fidelity to that to perturbation of that control operator and Then you update your theaters your trainable parameters by taking this response and Sort of projected onto the mode function This is this update step here and we find that that we have both an improved convergence You have a faster convergence and also the final implementation here this floor is below sort of what you find in a typical grape realization Okay, so and this appeared on the archive this morning So I'm I'm assuming most of you read this but in case you haven't you're very welcome. Okay So like so if if any of you readers I buy your beer so like I'd be I'd be sort of so grateful Okay, so this I'm just going to Roughly click through here sort of so we're working here with Klaus and Christoph and their teams on Implementing sort of quantum gates and Rittback systems here sort of you have the user Playground of having these sort of cool sort of high energy states of Of atoms where you have this very high quantum numbers. They have this massively enhanced fundamentals interaction Into the 11th, which is sort of a unreal sort of Enhancement I would I think sort of coming from other parts of and then you put them in your tweezers array and you look for gate Realizations in this context. There may be two notable limits or so that That might be worth your consideration If you take a fairly large distance and they're fairly slow And the very fairly low sort of Rittback state then you then your gate realization is based on accumulating the dynamical phase Whereas if you take a close Distance between these tweezers and or so and a large kind of quantum number then you end up have a gate realization in the so-called blockade regime so we sort of find in our Optimization that these regimes are sort of very relevant for for them for the behavior of this Okay, so we made a model here's our 3p0 states. We take two of these as our logical qubit We have our Rittback state which belongs to the 3s1 Many-fold we have a Rahman two-fold on process between the logical qubit states here and we have a Rabi Pulse between the logical one state and the Rittback state you take some of the dissipation into account all of this is done here in an Etterbium setup Okay, I could have saved myself some time saying this and here these this is just more like an illustration I'm not going to go into too much detail For now sort of them You said get then can optimize in the space of these individual Rahman transitions here Rahman pulses and in this global Rabi pulse you can optimize this here with so realistic conditions that that we learned from our friends and Then you find that you can realize kind of a nice CNOT gate for example where you see here the learning process And this pie here is again some sort of global constrained on the system that that he needs to need to meet in order to end up in a good regime here For your your CNOT gate Okay, and you can also maybe last remark on this here. You can also check the robustness of this implementation This is sort of a typical question you might get asked What about sort of spatial fluctuations or spatial? Sort of that you that you don't quite put the tweezers at the location that you're looking at but for the more you have sort of emotional vibration of the of your atoms in your tweezers and you can check the robustness of this prediction and what Survives and there's that you both the blockade regime and this kind of weak coupling dynamical phase regime Have both a good robustness behavior, but there's a intermediate regime here We're all the energy scales the Rabi pulses and so on they're all on the same scale as the interaction And that's where you have low robustness. So that's a regime to be to be avoid that you should avoid in an experimental realization Okay, so this was here my conclusion site. So I kind of ran through this a little bit This was a bit of a speedrun of this part here just to just to give you an impression of sort of Utilizing these optimization ideas in a practical context Okay, so this next topic here. So before saying anything this is really all Lucas first credit users all the credit in the world for this So if you conceive the idea develop the idea, I was happy to and excited to help sort of but it's really all sort of his credit. It's like You should watch out for this guy. This is really Very impressive or so Yeah, okay, so we call this exclusive all-base quantum algebra you're shifting gears a little bit in case I haven't mentioned it Sort of and we call this here or come So by the way sort of we we picked up on this on this sort of trying to give like cutie names or so kind of We have now fully embraced this In presenting our work. Yeah Okay, so Let's try out the following little game here sort of Which is a little bit the starting point for establishing this method Here sort of you take the Pauli matrices like so you add the identity to it So you have zero one two three and you write those numbers in the binary Representation zero zero zero one one zero and so on right Okay, that's what we call all connotation now And now let's do that to a couple of products for example if you take sigma x times sigma y It is now this product here based on our Based on our notation you get I sigma z out of it, which is now I sigma one one You can also take this product for example and you get it means that the product of these two guys becomes This matrix here zero one out of this year or you can also here throw in a trivial one where you just multiply sigma zero one By sigma zero zero and you get of course the same matrix back The interesting thing about this in our opinion is the this the operation on these indices here Just check out these indices here and let's kind of see what happens if we look at them digit by digit, okay? So what happens there? For example here the first digit is a zero second one is a one and you get a one So right this is this guy here, right or here sort of the second one is a one The first the second of the first operator is a one and the second one is a zero gives you a one Right, so this is this line here, right? If you have two ones here that are collided you get a zero if you have two zeros here You also get a zero in other words the indices in this binary representation follow an exclusive or sort of logic table, right? So this is the starting point for this methodology So Which is basically kind of cute like so like I'm not sure this has been okay, so Then sort of there's a second part of this yeah, we have to take care of these pre factors here Right, we have this I or minus I or one or so and we write this in the following way So first of all here we wrote our sq2 algebra If you now have a general Product of these four operators here You get sort of here sigma j x or k right sort of in the sense of having your binary numbers And you're operating on the x or digit by digit And you have the structure factor as we call it yes as a pre factor Which we write as I to the something as a product type of form and this bjk Sort of you can make a lookup table like it has shown here that sort of gives you the right pre factor over here Okay, so Okay, so so this year I should mention maybe sort of these are all very computer friendly operations, right? Taking x or operation digit by digit on binary numbers perfect, right? Also this lookup table you won't need anything more than a four by four lookup table Even if you scale this up to large tensor products So these are all sort of very computer friendly operations So for example, you can extend this to tensor products of These matrix of these Pauli matrices for example here's now a four product like four of four Sigma matrices here's another one what you now do is you write down a multi index you write down our Orca Notation and you make a you concatenate them you just write them one after the other giving you the multi index Pointing towards this tensor product of sigma Matrices similarly here's another one. This would be this multi index here Now you take the x or sort of line by line So it does here digit by digit like it has shown here giving you I x or J This gives you here the right sequence of Tens of product of sigma matrices and then you have to look up sort of your structure factor this exponent be here sort of In pairs of tools and them this year Then you put here as the exponent in your eye to the farm in this case to the power of two So this is then how it scales up in a sort of basically ideal way sort of you know sort of you basically You're simplified instead of taking Products of matrices of very large matrices all you do is like some index algebra, right? It's essentially Sort of this is essentially an ideal speed up in this context It's a little bit reminiscent of doing pointer algebra in a in a computer program, right? Okay, and the plan is to use this structure now to have a sort of good sort of simulation tool of quantum dynamics in this case here On systems that can be thought of being Composite to level systems. So here's a little bit our generalized structure So if maybe in the interest of time, I'm going to sort of keep this short But sort of what we want to do is we now write both our density matrix here in this kind of space of tensor products of Sigma matrices like it is shown here. So now we have broken this up into these row eyes. These are now Sort of the coefficients the real value coefficients of these tensor products here and similarly here the Hamiltonian We also write this in this way, right? Okay And sort of we looked at a couple of difference equation of motion like so to describe different types of dynamics For example, you can have von Neumann and master equation or you can also look at the imaginary time evolution here Which is then you can translate this here line by line in this formalism that I described to you in this notation Okay, so to describe to you This is really the delays So this is then the algebraic structure, this is the algorithmic structure of the algorithm that we're using there You have a dynamical list your density matrix here first is here the super index This is now written here as a decimal number But you should think of this here as being a set of Binary numbers like I described to you these are the indices and these are the corresponding real valued numbers here And it's dynamical sort of the size of this list you will change in time in general This is the size of the list similarly. Here's your Hamiltonian, which we also break up in this in the same way Here's the multi index for this component and here the real valued components of your Hamiltonian Give it with a size h like it is shown here and this here now You can use to generate sort of the update step there of your time integration You take the x or for these multi indices you you look up your structure factor and this then generates the The time evolution the time step of your time evolution So first calculate this then you want to then you can update your your row according to this in the example That I'm going to show we use some longer putter of fourth order or so and then kind of in order to keep the system Feasible you have the option you don't have to but you can sort of truncate your sort of expansion or your representation Throwing out all of the all of the components that you found there that are below a certain threshold epsilon So that keeps the system that keeps the description sparse in your Okay, so we applied this here for quantum annealing to quantum annealing for the maximum independent set problem You can see we are a little bit influenced by sort of Working with Rittberg so so where this is sort of discussed as a possible application So the in a nutshell basically what you imagine you write yourself down some some graph here And you want to you imagine you put like a unit circle around each of these sites here and you don't want to read Errors in the same unit cycle. That's a unit circle. That's that's not allowed And then the question or the the maximum independent set problem is to find a set up or all setups In which you have a maximum numbers of red Vectors here Okay, and we applied sort of both versions of it. There's both the real time and the imaginary time Evolution great. Thank you Imaginary time version of this here So this is I'm gonna skip this here and you find there's some good sort of performance in identifying The maximally independent sets here both for the imaginary time with the real time Like it is shown here Okay, so maybe sort of Just to comment on this briefly here What we find for our algorithm here is the CPU time This is actually just done on the on the laptop even though you have up to 22 qubits or so Here you'll see the effective size of your computational test Size of your density matrix list and size of your Hamiltonian list that is shown over here These are then different runs or so and you see an approximately linear scaling or so so we are trying to learn about hash maps in What exactly is realized in a in a MacBook or so? Because that appears to be the remaining constraint or the what which might actually lead to a small logarithmic Dependence on top of this. Okay, so in conclusion on this here. I showed you this idea of Orca sort of where you introduced this or connotation and you find this kind of efficient way of taking products of Product of tensor products of Pauli matrices Reducing it essentially to taking the exclusive or on the index of replacing a Matrix multiplication with an index operation And so with this then add to this and also this the structure factor term Basically here write the spin algebra as an index and pre-factor algebra then you can take this set up and use it to for the some simulation of quantum dynamics like I described to you here and be used The maximally independent set problem as an example. Okay, so again out of time So I'm just gonna skip through this stuff here kind of even though it's dear to my heart or so but yeah, we can talk offline if you if you would like to Chat about this here. I'd be I'd be very excited to do that And let me just go here to our conclusion pay So in conclusion here these first three topics here Centered around sort of optimal control and optimal implementation of quantum Quantum gauge and quantum algorithms and in quantum systems So it has this optimization test associated with this and I would like to emphasize this kind of new sort of Algorithm that you put forth which is our our pepperon method here then this year is our sort of approach to Simulating quantum dynamics on the as on a classical computer Which sadly is all we have these days which we call the orco method I didn't get to talk about the dynamical BKT transition and here. Let me throw up the The reference to to this here and let me thank you so much for your attention. I Can Really what are you exactly? Thank you for the very interesting talk I wonder whether any work has been done to look at whether an X or Representation or something like it would work beyond just SU 2 looking at D dimensional quantum systems or say fermionic ones for example awesome, awesome. Yes, so we've We've had a similar thought or so like and I we can chat more about this Yeah, so there are extensions of this year to other types of operators We like so we have something in the pipeline sort of where you do a bosonic thing where you have a harmonic oscillator Where you have a where you can find a similar index algebra We are playing with the fermions or so kind of you can chat about this and get you involved or so. It's like and sort of Yes, like so it's a very natural and very good questions for like to how to expand this towards other degrees of freedom or so and Yeah, I think there I think something should should be possible. Let's put it like this. You like what? Appreciate the question. Yeah, I should really emphasize this year kind of especially on these methods or so if there's if you find any of this you're mildly amusing or so sort of Send me an email chat me up or so and we get you involved so I'd be excited to be Yeah, you can you can add some pepper to your life or write an ochre. So, you know, it's all there for you Didn't hear the one word sort of Okay. Yes. Yes better when you When you expand the controls in free space Do you introduce yeah cut off on the number of Fourier modes which are involved in the expansion, right? Exactly. Yeah, and did you check whether this mitigation effects depends on the number of free modes you keep There is so more modes give you better implementation. So it's it's Essentially if you look at the comparison that I showed you there's like so like you you're roughly sort of Re-shifting both of the curves both for the stepwise and the Fourier in a similar way at least by eyesight like maybe we could Scan this a little bit in more detail. So You have sort of I think you have end up with the usual optimization thing that You might have more challenges in the convergence that might be a bit slow down It has to scan a larger space You might end up with a lower floor because you give yourself the opportunity to have a better implementation So that's as a quality of level is a great question or so kind of We have some plot and the appendix on this I think but it's We can chat about more on this, but as a tendency, that's the global tendency that we let me see him Is any more questions? Yeah. Yeah, thanks for the nice talk. So I wanted to ask about the orca the So there are sort of two ideas here, and I'm not sure if they're both crucial for the same things but what one is this sort of Basing things on poly strings and how they couple and it seems like I could do that whether or not I wrote use this X or stuff right like this this seems to be what would give you the real sort of scaling advantage that you might be seeing and Then it looks like the X or stuff is just to make it really fast on a computer Is that right or is there some more fundamental thing there that I'm missing? I I think that's I think that's fair sort of And I can live with this description making something fast on a computer. It's not a not a it's not a dirty thing, right? So no, it's great. It's great. I just making sure conceptually. I understand It's like I think it's this this ease of usage of identifying I think what I think you're 100% right that you could in a similar way So take the X and the Y and make a look up take a table to get the Z But so if this is so such an easy sort of implementation, you know, that's like let's just go for that You know, so it's perfect and then I didn't really understand why you were getting linear and time Scaling I naively would not have expected that right. So I should so basically To maybe to Reintroduce the notation so so this is the length of the of the list of the your density component That's your major components. This is the length of the Hamiltonian and basically what's on the x-axis there That is this that is the product of these two guys kind of so that's that can be a large number and also kind of Especially for example, if you for example sparse problems are essentially ideal for this Basically, you're you're also we are also not beating quantum supremacy or so if your role is not sparse And this is scale I to to the end for example, you know that that I See some nodding so life is good. Yes. Appreciate the question Okay, I think we should stop here and thank you very much Ludwig again for your talk


In this lecture we will have a brief introduction to the smartphone hardware. Smartphones are used today for mostly communication and also for processing video, audio and many other high end applications. So this is the outline of the topics that we will be discussing. The number one is the hardware architecture of the smartphone. Second we will look at some of the important peripheral devices connected to the smartphone and we will take a quick look at a simple example of a smartphone. We will start the hardware architecture description with the application software because this is what actually executes on the smartphone. The software gets executed with the help of an application processor often taking help from a graphics processor for rendering scenes and whenever the application processor or the graphics processor needs data it will access the main memory which is dynamic random access memory. In the application software and the application processor sit two elements one is the operating system and the drivers that are necessary for handling various types of IO calls. We will look at the IO devices very soon and a runtime system or the middle layer which the application software takes help of to execute various types of application programs. The operating system must have drivers for various types of peripheral or IO devices for example the global positioning system display and the touch screen keyboard camera microphone speaker and so on. The communication is handled by a modem processor which receives all the signals and passes them on to the operating system and the baseband radio interface. The radio interface communicates with the transmitter and receiver and also the SIM card. The two different operating systems residing on the application processor and the modem processor usually communicate with each other for handling data communication between these two modules. For example when the modem processor receives some data it will interrupt the application processor through its operating system and the application processor will now handle the data copy to its memory, decode it and take appropriate actions. So in summary the smartphone is really a system on chip architecture with three primary components. They are first an application processor executing the end users application software with assistance from the middleware and the operating system. Second component is a modem or baseband processor with its own operating system components responding to the baseband radio activities such as transmission and reception of audio, video and other data contents. And the third component consists of a number of peripheral devices for interacting with the end user. So now we will take a look at the peripheral devices that are usually found in a smartphone. These are the IO devices through which the end user interacts with the smartphone and the operating system needs to have the driver software installed for each such device for it to operate properly. So here is a small list of typical peripheral devices that are found. For example the LCD and the touchscreen, the keyboard, the camera, the GPS, the speaker and audio output for headset and earphone, the microphone, the Bluetooth and the wireless connection and HDTV. Next we will take a look at an example but before we go on to an example we need to understand what kind of processors are used in a smartphone. So these processors need to balance performance, power consumption and cost. Arm-based processors have become very common because these are optimized for battery life as well as performance. They also have remarkably low area and transistor count leading to small form factors and low energy drain which are very important for smartphone applications. The modem processor is either a separate arm core or a digital signal processing extension of the application processor arm core. Some architectures also use a modem accelerator along with the application processor core without having a separate dedicated modem processor. So let's take a look at a typical arm-based smartphone hardware. It has several components so let's go through the important ones one by one. This particular example has a dual core ARM Cortex A9 application processor. By dual core I mean there are two processors that can operate simultaneously in parallel handling multiple application requests. It has an ARM Mali graphics processor and a video codec for handling graphics related requests. It has an ARM Cortex R4 3G 4G modem processor for handling the communication part. It has a SIM interface and control which also has a SC300 CPU. It has several forms of storage and storage interface. Storage has become very important for smartphones because they operate on complicated applications so they need big amount of storage. So in this particular example we have an MMC memory card. We have the typical SD RAM memory which is in this case low power DDR2 and also we have a NAND flash card along with this flash controller. And finally there are several peripherals and ARM peripherals CPUs in this example. To go through a few of them it has a touch screen connected to a Cortex CPU which manages the power and the other activities of the touch screen. It has an audio interface. It has an LCD and HDTV interface connected to the HDMI and LCD video interface. It has a camera. It has several communication CPUs. For example it has a Cortex M3 CPU for Bluetooth handling. It has an ARM 968 CPU for wireless connections. And it has a Cortex M3 CPU for the GPS connections. So this is pretty much a typical ARM based smartphone hardware that are found today. And with this we will close this lecture. Thank you.


Okay, we're back live here at Oracle OpenWorld 2012, day three, this is SiliconANGLE.com's theCUBE, our flagship telecast, we go out to the events, extract the signal from the noise. We go where the action is and the action this week is in San Francisco at Oracle OpenWorld. We're Larry Ellison, the CEO, longstanding tech, Titan CEO, tremendous keynote on Sunday night and yesterday really announcing Oracle, walking into the modern era of cloud computing and big data, as we say, co-opting innovation from others and putting their wrapper around a big red box. I'm John Furrier, the founder of SiliconANGLE.com and I'm joined by co-host. I'm Dave Vellante of Wikibon.org and John, it was interesting listening to Larry last night. He talked a couple years ago about multi-tenancy, how it was a bad thing and then this week announced the multi-tenancy database and the nuance there is Oracle has a different philosophy on things, John. They don't believe that doing multi-tenancy at the application level is the right way to do it. They want to do it at the database level. What a surprise. You know Dave, we were on this morning in our news program we're launching, commenting on the results of Larry and Oracle and certainly a different vibe this year, a mood of, wow, we now have some clarity. Oracle is so powerful, people are nervous. You know, if there's any kind of inconsistency or direction change, it affects millions and millions of dollars. So what's happening this year is the packaging of the messaging is tight. They're clearly going into cloud in a big way. They don't care what you call cloud. As long as you're running Oracle. And now big data with analytics and Exadata and DECA, the demo yesterday, clearly a direct strike using cloud computing and big data mega trends, owning those trends and using that against the competitors like SAP, like Salesforce and clearly Oracle saying we will have better products and solutions, not some mishmash of solutions. So Larry Ellison, again, that's his messaging, whether that's true or not, it's a whole nother story, but a little bit more clarity and I think people are comfortable with that. Well, I think you're right. I think people are nervous about Oracle and a lot of people criticize that sun acquisition, but if you think about pre-sun, what that demo yesterday would have looked like, they would have probably had Oracle and perhaps some HP hardware in there. They might have talked about that a little bit. Whatever hardware allowed them to run the applications fastest is the one they would use, not anymore. They'll claim it's the fastest and it's all theirs. The other big story I think you and I were talking about yesterday, you brought this up and I think it's a legit storyline. That is, the hardware companies becoming software companies and the software companies becoming hardware companies and Oracle, a software company, is becoming much more hardware-centric and integrated approaches, as I said, trying to be the Steve Jobs of the enterprise. That's Larry's vision. He wants a fully integrated data center solution. That's all Oracle all the time and then you got guys like EMC and others who are differentiating with software because with Flash, with this new technology, the performance gains of tweaks in the infrastructure hardware are driven by massive advancements in hardware like Flash and others, but it's the software that drives the value. So it's very interesting to see that take place. Yeah, I think you're right. I mean, that software has always been where the differentiation is. That's how you exploit hardware, but hardware is still critical. Ever since I've been in this business, people have been saying that hardware is going to get commoditized. Hardware price has continued to come down on a cost per performance basis, but it's still a critical aspect and I think Oracle's strategy, like Apple's in the consumer market of integrating hardware and software is one that the industry is now having to contend with. So, John, let's turn our attention to our next guest. We're here at the Q-Logic booth. It's been great all week. Dave, if it wasn't for Q-Logic, we would not be here. Q-Logic has supported us now three years in a row at Oracle Open World and this is really what it's all about. We are allowed to come in, extract the signal from the noise thanks to Q-Logic. So go to Q-Logic site, call the Q-Logic folks. They have the best solutions in the business. We totally love these guys. If it wasn't for them, we would not be able to bring you this great independent coverage and so I just want to put a shout out to Q-Logic to see the logos behind us and it's the Q-Logic Cube this week. So I'm really excited. Yeah, so I'd like to second that. And one of our favorite guests, Rob Davis, who's the CTO of Q-Logic is on now. Rob, welcome back to the Cube. Thank you guys. Thanks John, thanks Dave. Thanks for saying nice things about us. Awesome, thanks for hosting us. So good to see you again. We're here, as John said, Third Oracle Open World. So first of all, tell us what's new with Q? What's new with Q? Well, we're focused, I'm in the futures business at Q and we're focused on cloud and flash. Those are areas of exploration and new products. Well, it's interesting. We've been hearing a lot from Oracle this week on both of those topics, cloud and flash. So what's your take on cloud and flash and specifically what's different about the Oracle environment? What's different about the Oracle environment at the show here? Just in general. It seems like they're much more focused like you were saying earlier on integrating the sun acquisition. If you walk around their hardware demonstrations or boosts, they have a lot more spark. Spark is being used everywhere instead of X86 and Solaris is being used almost everywhere except the Oracle Linux. So I think they're really focused on integrating that sun product line. Yeah, we used to joke that the X and X data and X analytics stood for Xeon. I think Oracle's taking that to heart and really pushing Spark a lot more. It's very different than last year, that's for sure. Oh, so we'll talk from a technology standpoint, Rob. What, how do you have to approach the futures, the technology perspective in an Oracle environment versus say some of the other environments like a VMware or other general purpose environments? What's different about that? Well, their main focus there from our perspective is the database. And so our new product, the Montrenier product is focused on accelerating the performance of database. Yeah, so performance is critical. So from a cloud standpoint, what can you tell us about what you guys are doing and where you fit? On the cloud side, we're really trying to figure out how to integrate cloud storage with enterprise storage. We think that cloud storage has got a great future but there's a big disconnect right now between enterprise storage, which has a whole set of different characteristics and cloud storage. Obviously the price difference is dramatic and so we feel that that's going to be driving the cloud storage market. And we're in the connectivity business and we think connecting that enterprise to that cloud is a great opportunity going forward for us. So when you say cloud storage, can you tell us what you mean by that?  So technically in terms of how you approach designing products, that's a networking issue, it's a balance, it's a distance issue, it's a latency issue, it's just a mindset issue too because in the enterprise, they're thinking about high availability, they're thinking about high performance and in the cloud space, they're thinking about price and scalability and it's, you know, I've heard it said a few times, I don't remember exactly who said it, the problem is taking a watermelon, putting it through a straw and then recreating a watermelon at the other end. So, well, so now you guys got out of the InfiniBand business last year, you sold that business. True. So what's your fit in this new world? What's the fit of, say, Fiber Channel and Ethernet in this new world? Well, we've had a product doing distance connectivity between enterprise data centers and on the storage side for years. Back in the 90s, there was a protocol, kind of the first IP, Fiber Channel protocol called FCIP and that allowed you to connect two sands across the wide area network and so we have a storage router product, the 6200 product line in that space. We've continued to add features to that over the years. When Ice-Cuzzy came along, we added Fiber Channel to Ice-Cuzzy bridging, when FCLE came along, we added that bridging, we've added data migration software to it and that's a product that we're starting to put into the space connecting enterprise storage because it has all the right interfaces, it's high availability to cloud storage, which is wide area. So let's go back to Flash. We've been hearing a lot about that this week. Certainly Larry Ellison's talked about it. We had some other folks on and the Flash Refusion IO came on. Aerospyke was here talking about the benefits of Flash. They're a new type of database company. So what's so cool about Flash? Well, Flash is very interesting technology because it removes the mechanical aspects of storage, right? Distries have had mechanical pieces in them since they were invented, right? 50 years ago, probably almost. Spinning rust, exactly. And Flash has no moving parts, right? So you can really totally rethink how you design computers because for those 50 years, computers have been designed around the inadequacies or the deficiencies of those moving parts, whether it's availability, whether it's performance, whether it's power. And with Flash, most of those are eliminated. So you can look at designs on, especially on storage and networking completely differently and that's where our Mount Rainier product fits. So let's talk about that in some more detail. So can you talk about specifically how you change your thinking as a technologist as to how you design products with Flash in mind? Sure, so the interesting thing about Flash is that it's high performance and it's persistent. And so you can put it in the case of a database right in the server and you can put whatever part of that database is maybe even the whole database right in that server locally and it's persistent, right? And that's a great technology. There's lots of companies doing that. What we're doing that's different, which is enabled with Flash is we're adding the IO interface to that Flash storage. And that gives you all the aspects of sand because if you take a standard Flash drive and you put it in a server or you take a PCI Express Flash drive and you put it in a server, you're giving up all the sand aspects that you get, the management of the storage, the mobility, all those aspects. You're kind of going back to the days of early DAS. By connecting the IO to it, which we do with Mount Rainier, you're getting back all the sand aspects of that Flash storage along with the performance of putting it in the server. Rob, for the guys, the IT pros out there who have a lot of sands, they're like, might not be in the weeds and inside the ropes with Q-Logic, what's your message to those guys that have all these large sands? How does the new Mount Rainier product, what does it mean to them? Does it make your life better? Does it move things around? Does it enhance it, scale it back? I think it makes their life better because they can take advantage of Flash without giving up the advantages of their sands. I mean, just in a nutshell. So it's leveraging sand investments. Yeah, and those Flash cards are complicated beasts because you've got to have the right set of drivers for the OS. You've got to have the right connection to the application. You've got to make sure you've got enough memory, enough CPU power. So there's been a lot of talk with Hadoop and these other technologies that, hey, replace your sands. Let's get some Flash and let's kind of replace those sands, but people scratch their heads that I've talked to, like, hey, that sounds all great. Maybe I might want to take some EMC, new sands off the table, maybe experiment, but I can't just replace the sands. And this product allows them to keep their sands and add Flash technology without a lot of complexity. And so what's the performance going to look like for them? So just going to order a magnitude. What's going to change in the performance side? So we're running a demo here in our booth at Oracle OpenWorld and we're showing a 5x performance improvement over a standard Oracle Rack solution. With spinning disk, obviously. The spinning disk versus the Flash card. But you still get all those sand aspects with the Flash. Awesome. I have to ask you, so Stu had texted me the night, said that he read an article about one terabyte ethernet being not feasible. And just again, from a technology perspective, and I know you're not familiar with the article, so I'm going to put you on the spot, but just conceptually, one terabyte ethernet, what are your, I mean, I know it's way out there. What are your thoughts on that? My thoughts are that when I started in this industry, which was a long time ago, one gigabit ethernet wasn't feasible. And two, four gigabit fiber channel, and eight gigabit firetellin, 16, and 100 ethernet. I mean, they're always not feasible at this point in time. But somehow we figure out how to do it, don't we? Excellent. All right, Rob. Well, listen, thanks very much for coming on theCUBE. Rob Davis, CTO of Q-Logic. Always a good guest. I really appreciate your time. Thank you. And good seeing you again. Okay, this is theCUBE, SiliconANGLE.com's coverage of Oracle Open World. So those are brought to you by Q-Logic, and wasn't for them, we wouldn't be here thanks to the shout-out to Q-Logic. I'm John Furrier with Dave Vellante. We'll be right back with our next guest after this short break.


Welcome back to Kids Fun Science. My name is Ken. Today's experiment is the homemade water thermometer. As always adult supervision is required. What you need for this experiment is a stove, a drill, a jar, a pan, a straw, and some food coloring. So to set up this experiment you need a jar with a lid and so I've got my jar, a little canning jar, and take the lid off there and then I'm going to drill a hole in the lid the same size as the straw. You don't want it bigger than a straw so you want it the same size. So make sure you size that out and depending on your size of your straw and then once you have the hole there you're going to put it back onto the jar after we fill it up. So we step in I fill the jar up full with water you want it almost to the top if not to the top add food coloring and I know I'm adding blue and I'm doing a hot experiment I should be adding red for the color effects but blue was the only one I had so I went with it and so I apologize on that but you'll still get the same idea and see how it works so go ahead once you get the food coloring in screw the lid on and then we're going to take the straw and it helps to have a clear straw so you want to be able to put that in it should be very tight if you have the right drill bit and put that all the way to the bottom you'll see here the straw very hard to see I move it back and forth you kind of see it moving a little the straw's got to touch the bottom of the glass so once you get that there then we're going to turn the stove on high we're going to put a pan half full of water and so it's on high and then we're going to take our jar and put it in the middle and and then we're going to observe now we're going to sit back and observe the homemade water thermometer but while I'm doing that I'm going to explain the science behind it so as water is heated its molecules move faster right so causing them to spread farther apart from one another and as a result the heated water expands causing the water to rise in the straw because it has nowhere else to go right so the thermometer works according to the same principle right so as the liquid which is in regular thermometers mercury or alcohol is heated it they expand and they rise as it cools it contracts and falls back down so as you see is the water starting to get hotter and hotter we're going to notice that the water the hot water is going to expand and it's going to only place it can go up the straw that's why we need to have a clear straw so we can see how the thermometer works so this is a really fun one to do at home just be very careful as the water is extremely hot and you can burn yourself in the jar and then the jar itself and the water in the pan as it continues to get hotter it's going to expand more and more so it's a really awesome experiment and once it gets to the top it will have nowhere else to go but out of the straw so that's pretty fun to watch too so continue to watch is going to go up i hope you enjoyed this video remember to click thumbs up and to subscribe and thanks for watching