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Last 27, December 7th, 2020. okay. So what's happening today?

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It is.

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Session 2, for final project presentations we've got the list of people below.

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For the previous class and.

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2nd corner Gal and gave us note told us about this new.

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Announcement from China here called claiming quantum supremacy question for you is do you believe that the, or not.

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May be 2 or maybe not. I'm not competent to evaluate it, but it's very interesting.

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So, the 3rd thing is the core surveys are open. I hope you enjoyed discourse. I work hard tried to make it. Very interesting. And tried to make it. Relevant might translate into, getting, better, paying jobs when you get out.

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So, if you talked to course, was worthwhile. If you did spend a little time and fill out the survey, this helps me to log inside our Pi if students in my courses.

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Tell department head and so on that, they liked the course and so very few students actually fill out the surveys about 20 or 30%. So each response has an outside weight. So thank you.

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I'm now coming back. Um, so here's who we have today.

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If I type things, right? And so I can just go in order like this.

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If you wish, no, 1 sent me any videos. So Cohen.

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You just share your screen and I'll mute myself.

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See here all righty. I can see you. I can hear you.

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Let's see.

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All right.

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So, for my presentation, I wanted to discuss a little bit about quantum computing in the context of the transportation slash automotive industry.

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Which there's a number of projects going on in the industry right now, and a number of collaborations between different companies in the industry and companies outside the industry.

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So I wanted to discuss a few of those and how they could impact the industry moving forward.

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So, the 1st company I wanted to discuss was, which is the parent company for Mercedes Benz.

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Now, they've been collaborating with IBM with a bit over the past couple of years, and they expect some of those projects to start maturing and more fully realizing themselves over the next decade.

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1 of the things they've been looking at is applying quantum computing in their production facilities. For example, some of their welding machines, they would be able to run quantum simulations to help determine when recalibration.

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Of that machinery machinery would be necessary, which will help them reduce costs in quality assurance.

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1 of the larger projects we're working on, relates to battery research, which they've invested over a 1B dollars into.

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And here, they're trying to develop lithium sulphur batteries, which are higher capacity and cheaper relative to their lithium ion siblings currently used in electric vehicles.

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So, to get a bit more with the battery research, what they're trying to do is to simulate the ground state energies and backbone moments of the chemical products during that charging and discharging reaction of the battery.

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And you can see a graph relating to that process over on the right there, which was provided by IBM and 1 of their blog posts about the research.

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But during this discharge, you have multiple chemical products, including lithium hydride, hydrogen sulfide, lithium, hydrogen sulfide and lithium sulfide, which is the desired result.

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So, what they're essentially trying to do is to reduce the energies of those systems of products to make them more stable, which leads to a more stable battery that would function as expected.

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So, up until now, they've been using classical computers a bit here and there, which do help simulate these smaller molecules.

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But they have to supplements those classical calculations with data from the lab because when you increase the size of these molecules, classical computers have more and more trouble.

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Since, and then evaluating how stable they are.

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So, when you're manipulating these bonds, you can describe the system with a highly entangled quantum state.

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And then you can evaluate the relative stability of all these states simultaneously because of that parallel isn't granted to you by want of computation.

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So 1 of the algorithms, even using during this process is referred to as the quantum solver algorithm.

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Which, technically, speaking here, that algorithm just finds the minimal Ivan values of assistance him with Tony, which are just means that they're trying to minimize the energy of that system.

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So this is a hybrid class quantum algorithm, which means in this case, that they're essentially using the quantum computers an accelerator, and it's being fed a molecular structure by the classical computer.

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And then the computer will run its calculations and then feed that back to the classical computer.

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But you can actually find the implementation of that in the kick off a library. If you want to check that algorithm out.

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Ibm used use case kits quite a bit during this process, especially for 3 of those.

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Chemical products as obviously quantum computing is still.

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Fairly new quantum computers are fairly small. You don't have a ton of bits to work with.

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And obviously noise is something you have to contend with. So it was them just trying to introduce themselves to.

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I'm quantum computing in this context to see if that kind of processing wouldn't work.

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Those 2 minutes to run tests on the lithium sulfide molecule.

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And you can see some of the results from that research in the top rights on the X axis. You have the bond length. Is there manipulating that bond length?

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Within between the atoms and the molecule.

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And then on the Y, axis, you have the energy of the system.

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So, looking forward with the software batteries, they're very energy dense. You're looking around 2601 hours per kilogram.

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Relative to their lithium ion counterparts, which are around 300 to 501 hours per kilogram.

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So if they're able to work the kinks out of this process, and we see maturing at quantum computing in that, you don't have to worry as much about noise. And you have more more cubes to work with.

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You can see some significant develop developments in these batteries and in other materials in the next few years, which could really help the electric vehicle industry.

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So, next company I wanted to look at was the Volkswagen.

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Now, they've also been doing their own work with batteries in their own supply chain evaluation.

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But with them, I wanted to look more at their and their route management.

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They've been working on and they've been working quite a bit with Google,

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google's obviously very good with neural networks and they want to explore if you can apply quantum computing to neural network training to reduce that training time,

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which would obviously be very useful for.

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Hey, I really related to self driving vehicles.

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1 of the larger projects that I wanted to look at was their quantum routing as it's called.

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And for this, they were working with some D wave hardware. There are 2000. Q. and Mila to be specific.

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And they started showing off their work here back in 2016.

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Where are they aggregated data from 10000 public taxes in Beijing.

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And then they provided routes for all these vehicles who were heading from the center of the city to the Beijing Airport outside of the city.

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Now, traditionally you would do what's called selfish routing where every part essentially gets the same routes, which would be the hypothetical, fastest route from where they are to the desired.

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When when you do that, every car, it ends up going on the same route, and you get a lot of congestion. So not being the fastest route.

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So, what this, what the D D wave hardware was doing here was optimizing the distribution of these vehicles on roads throughout the city so that you wouldn't get that congestion and people would get to their destination faster.

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So they worked a bit more on this and in 2018 they showed off some work they were doing in Barcelona where they were aggregating cell phone data from people traveling throughout the city.

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And then they waited through the data to find pedestrians people just walking around without a vehicle.

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And then they use a class computer to model where people were typically throughout the day where, and when.

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And the classical computer could predict where people would be. So they constructed a model there.

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That model was fed to 1 of these quantum kneelers, which was able to then provide an optimal distribution.

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Of taxis throughout the city.

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So, that way, you would maximize the usage of the taxi fleet and getting more people into these taxis.

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And you would reduce the amount of time that people had to wait for the taxis.

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And this was able to be done around an hour in advance to when those taxes with theoretically be needed.

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Up to that point, it was really just simulations of this technology, but they started practically applying it back in 2019 in Portugal.

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Where they worked with 9 different buses.

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On a series of 26 stops, and they did the same passenger slash pedestrian data aggregation where they modeled where people would be throughout the city. And when.

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And again, they said that to the wave hardware, which provided up more routes.

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And they would distribute those routes via an app to these bus drivers.

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And they could do that in close to real time. It actually took about 20 seconds.

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Or the D wave hardware to finish its calculation and to get that route distributed.

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But that was fine as the drivers would simply get updates at their stops. So, after they dropped people off, they would get another route updates. They would follow the next route. And so on.

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So, it was still very practical. So, again, this how many people were on their buses at once so.

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So, they were fully utilizing the public transport system.

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And they were minimizing how long 1 people were on the bus and 2 people had to wait for these buses.

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So, looking forward, Volkswagen is looking to start applying this and testing this in other European countries and cities.

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I'm not entirely sure how long it would take to see something like this practically applied in the US, but it'll be interesting to see how quickly it catches on in Europe and how it affects a public transportation as a whole.

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Own manufacturing manufacturing developments.

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I've been working quite a bit with NASA and their quantum computing lab again, working with the way of hardware to minimize the amount of diesel fuel used or transport vehicles.

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But 1 of the more interesting things that they've been working on was a collaboration with Microsoft, and you can see some of the data from that off to the right there.

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Where they did some of this quantum routing that Volkswagen has been doing.

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And they routed a 5000 vehicle fleet in Seattle so they had computers going from 1 place to another with different computers, having different destinations.

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And they use this dealer to find the optimal routes in distributing these people throughout the city.

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And you can see on the right as the dealer is continually optimizing the result across the different iterations until it finds an optimal result with a minimum congestion score.

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So, in that simulation, they were able to reduce reduced by 73% and actually reduce commute time on average by 8%.

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So, I thought this was a particularly interesting example as you can see that graphical output from the dealer as it's optimizing the data.

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So, aside from these new developments with quantum routing, these new technologies with battery material design.

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I believe that even just with quantum dealers, you could see some significant improvements.

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In processes with traditional vehicles.

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Just with optimization with the dealers, you could look at minimizing brag on vehicles. You can improve fuel efficiency. You can improve.

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How materials are designed and bust their durability.

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So, even just with dealers, we can do a lot of new interesting things with how vehicles are manufactured.

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I think as quantum computing becomes more mature, and we have more cubes to work with and noises reduce significantly. You'll be able to see a significant advancement and computer aided design.

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There's quite a bit you could do with more advanced simulations with quantum computing, for example, vehicle crash tests. You could vary a particular bear a variable.

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Quite a bit all do a number of test runs all at 1 time simultaneously because the parallelism granted to you by quantum computing.

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And that could significantly improve the design process there and result in safer vehicles.

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In fact, by 23rd, it's predicted that you'll see around a 3B dollar impact.

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To be moving forward, how quickly some of these new technologies mature and catch on and I'm interested to see how transportation and the automotive industry.

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The change going forward.

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There's some references.

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Yeah, I'd be happy to answer any questions. People might have. Yeah, thank you. Calling. Does anyone have any questions.

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I'll ask 1 then so it looks like this is all good applications for D wave more than IBM. Actually.

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Yeah, D wave obviously because their dealers are much more mature.

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They're obviously getting a lot of work,

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because there's a,

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you can do with right now,

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but I think once computers become much more mature,

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I definitely think that with simulations,

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especially you'll be able to do quite a bit to make vehicles again,

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safer and speed up the design process as you can do so much more testing all at 1 time with varying variables and whatnot.

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Okay, thanks anyone else.

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Okay,

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Amanda next,

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but 1st,

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I'm thinking for the orders since we had the group of 4 people,

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I'd say,

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like to put them at the end and then do I,

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if Isaac,

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if you can talk before Nathaniel and chase and Ricardo so at the end right now,

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Amanda,

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if you would like to talk now.

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How you.

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Silence.

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Okay, I can see your screen so.

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Perfect. Okay. So good afternoon. My name is Amanda.

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My presentation today, the idea of quantum, tunneling its effects on electronic devices.

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This is the agenda for today's presentation I will begin my talking about the early history of funneling and what exactly it is.

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I will then discuss some recent articles and experiments.

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I'll talk internally applies to human bodies and finally issues it can pause. When do we look at it on a very small scale?

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So what exactly is quantum, tunneling busy, quantum, mechanical phenomenon way function can propagate through a potential barrier.

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Real quantum, tunneling particles can simply pass through energy barriers. They don't have the energy to surround.

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Is actually the reason that the sun shines while the sentence is not hot enough to give the sun's protons enough energy overcome their electrostatic repulsion.

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The tunneling probability means that some articles can still make it through their repulsive barrier.

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Since the sun has large amounts of hydrogen, the probability translates.

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Into about 10 to the power of 38 fusion events per 2nd.

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Which produces light and heat and large enough amounts to sustain life on earth.

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Tunneling is actually a misleading word as it suggests that the particle is going through barrier.

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A particle can be described in terms of an oscillating wave and when encountering a barrier.

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The way doesn't abruptly and it continues inside and on the other side of the barrier, just with a smaller amplitude.

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Tunneling is actually the probability of finding a particle on the other side of a barrier.

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The lighter particle and the smaller and narrower the barrier.

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The higher the probability of finding a particle on the other side of a barrier.

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Really quickly this is a simple diagram that picks quantum tunneling in a more physical science.

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You can see the comparison of classical physics, verses, quantum physics.

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And there was a brief explanation of the phenomenon.

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So, beginning with their early history of quantum tunneling was 1st, noticed in 1927 by Frederick while you're trying to calculate the ground state the double. Well.

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The bubble while potential 2, other men also discovered the idea later that year while analyzing the implications of the shown injure wave equation.

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The 1st, application of quantum tunnelling was a mathematical explanation for alpha decay developed in 19, 2008 by George.

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2 other researchers, Ronald, Bernie and Edward Condon.

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Used the idea of quantum tunnelling to solve this, showing your equation for a moderate model nuclear potential.

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And derived a relationship between the half life of the particle.

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And the energy of mission, the 1st, tentative calculation of tunneling time appeared in print in 1932.

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And the study of summer conductors and the development of transistors in diodes.

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Led to the acceptance of electron, tunneling in solids by 1957.

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In 1960 to Thomas Hartman wrote a paper explicitly embracing the implications of quantum, tunneling.

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And found that a barrier seemed to act as a shortcut.

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What a particle tunnels the trip takes less time than if the barrier weren't there.

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And he found that thickening and barrier hardly increases the time it takes for a particle to travel to tunnel.

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This later became known as the Hartman effect a fact that still boggles the lines of many scientists.

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I'm now going to discuss some more recent research related to quantum tunneling and it's important in the scientific world.

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An article release this past July businesses have shown that content tunneling is not instantaneous despite other news that has said, otherwise.

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A new experiment, Chuck, the time of particles burning through barriers.

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The team of scientists down to about 1 Nano Kelvin before, moving them slowly in a single direction.

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They bought the path of the particles using a different laser, creating an optical barrier about 1.3 thick.

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The goal is to measure how much time and particle spent in the barrier as it tunnel through.

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To do this, the team built a special clock known as the alarm clock.

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The time spent in the barrier is measured by the amount of rotation of the clock hand.

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Which moves as the particle interacts with the magnetic field.

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Using weak measurement due to the instability of quantum states. The experiment discovered that the atom spent about 0T.61 milliseconds inside the barrier, which is far from instantaneous.

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The experiment also therapy, the strange idea that the lower the energy.

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Or they're slower the movement of a ton of of a tunneling particle.

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The last time and spends in the barrier, which is an idea of contrary to typical believes.

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Other experiments have proven that particles should be able to go faster than light.

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However, these claims may not be completely accurate.

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Some experiments have concluded that the duration.

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The clock a clock, a choice using other experiments measured is not a good proxy for tunneling time.

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1 researcher even suggested that electrons that tunnel out of the barrier, almost instantly can be said to have had a head start.

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However, it is entirely possible top particles traveled through barriers at speeds faster than the speed of light.

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Calculations indicate that by making the barrier really thick.

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The speed up would allow Adams to tunnel from 1 side to the other faster than the speed of light.

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Known as the Hartman effect, no matter how carefully the idea of quantum tunneling is redefined or how precisely they measure it. Punching tunneling invariably exhibits the heart and effect.

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That is a completely possible for a particle to travel faster than light.

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So, a very popular question often ask when people discover the idea of pocketing paneling is can humans clock and time.

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The short answer is now humans can the tunnel because for something as large as a person.

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Probability is so small that you could wait until the end of all time and still not find yourself on the other side of a barrier.

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Even though it is near impossible for full human to caught in front of.

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Internally is actually a sir, it is a central quantum effect. Quantum biology.

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It is important as both electrons and protons funneling.

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Worked on, tunneling is a key factor in many biochemical Redox reactions as well as enzymatic palaces.

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Talk totally as a key factor in spontaneous standing mutation as well.

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Spontaneous mutation occurs when normal DNA replication has taken place after a significant proton has tunnel.

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A double while potential along a hydrogen bond separates a potential energy barrier and hydrogen hydrogen bonds are what joined based payments.

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It is believed that a double was asymmetric with 1. well, deeper than the other.

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The proton typically, Russ in the.

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However, for a mutation to occur, the proton must have tunnels into the shower. Well.

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Are there other instances of quantum tunneling induced mutations in biology are believed to be a cause of both aging and cancer.

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Quantum tunneling has also given rise to new issues with electronics, especially on such small scales.

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The effects are often unusual and unexpected.

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And can cause changes in how electronic devices and signals behave.

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Multiple effects typically happen behind the scenes for most of the chip industry, and are embedded into a set of design rooms developed from data that companies really never used.

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Foresee boundaries and Mac manufacturing equipment companies. So far are the only ones that have been directly affected, not been making adjustments to account for the emerging effects.

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However, as designs continue to string.

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Has become increasingly more difficult to adjust to the issues that have arisen.

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And the effects grow more widespread and more significant.

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Quantum effects have had an impact on some technologies for quite some time. Now.

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And more effects are expected to to transistor body dimension reduction.

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Which is required to maintain electrostatics quantum effects, have always been there. But 1 matters is the extent to which they influence the ability to understand the physical and electrical behavior of the devices.

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Flash memory was 1 of the 1st places that chip makers began experiencing quantum effects.

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Several years ago, NAND memory companies reported seeing unexpected irregularities.

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And how data moves in and out of memory, the effects for 1 of the main reasons, why flash memory moved to vertical and and.

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Carnival effects are largely a function of the dual nature of electrons as both particles in waves.

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It isn't clear how the effects will impact even smaller designs at this point.

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What is clear is that more effects more merge as devices become smaller and smaller.

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In conclusion on to tunneling is a very important idea that not only applies to areas related to quantum mechanics.

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But also to Ponce and biology and other related topics.

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The effects of quantum tunneling have not only allowed for growth and money fields of research, but they're also spent into new research related to smaller technologies.

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And how to work with the adverse effects, that constantly totally may produce.

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There's also a lot of new research being conducted to study the time of quantum tunneling and its relation to the Harmon effect.

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And, yeah, that is all have for my presentation today. Thank you so much.

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Amanda, so the floor is open for questions.

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So, I hadn't quite realized it's important in biology, so.

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Interesting there are also certain enzymes that use quantitatively as well, like specifically.

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Silence.

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Okay, anyone else.

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Okay, great.

239
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Conner Macallan over to you now.

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Uh, it's actually me at the wrong names that last time.

241
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I still got it wrong. Yeah.

242
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Okay email me too. And so I thought I corrected it. So you emailed me again so.

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Yeah.

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Um, so I chose to do my.

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Um, I'm supposed to do my presentation on quantum machine learning.

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And so this is the general outline, I'm going to follow them and.

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Briefly explain what classical machine learning is for those who don't know it.

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And then a specific.

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Type of algorithm is a support vector machine so I'm going to show a quantum support vector machine and give it a.

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Applied example, that was used on IBM quantum simulator.

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I'm going to talk about some issues that arise with reading classical data into a quantum machine.

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Um, and then some current work that's going on in the field right now.

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So, a classical machine learning.

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It's question machine learning algorithms are algorithms that automatically learn and improve from.

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Input data that is given to train them.

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And this is that input data is.

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Direct experience from whatever system they're trying to model.

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And it uses that data to them predicts to make future predictions or to label unlabeled.

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Input data in the future.

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And the main idea is to have computers automatically learn without needing human intervention.

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The 3 main types of machine learning, and they're supervised learning, unsupervised learning and reinforcement learning.

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Supervised learning is a technique that uses labeled example data.

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To input and train the algorithm. So then it can predict.

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Future input unlabeled, input, data.

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And it can also learn from incorrect data.

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And that could be you have an algorithm that has to.

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I guess identify different objects that would be a type of supervised learning algorithms.

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In unsupervised learning is you passing unlabeled data?

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And it's supposed to find it's supposed to infer a structure or find hidden patterns in the data.

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A good example for this would be Netflix.

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Netflix probably uses a machine learning algorithm.

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Where it takes in all of its customers data, it analyzes it.

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And then it groups people up on shows, they like.

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That way it can then recommend another show in the future.

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That you haven't seen yet and that's 1 1 example.

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And then reinforcement learning is it's a trial and error search.

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Kind of algorithm where it will continue to try different things. And as soon as it finds something that works.

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It remembers that learns from it.

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And then keeps moving moving forward and it only goes and does that.

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And this allows the algorithm to automatically determine the ideal behavior.

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Within a specific context to maximize its performance.

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Examples for this is mostly in the problem spaces. So like, games situations, self, driving simulations.

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And different types of algorithms like that, or different types of implementations like that.

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So, quantum support back to machines.

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Implement standard classical support back to machines, which are a supervised learning technique.

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And the idea behind a support and back to machine is.

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It's supposed to the data into 2 groups, like the image on the side.

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Where there's a distinct difference between the data.

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And so you would pass in label data it would split it up based on his features.

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So then in the future, it knows how to separate the data.

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The unlabeled data to label it.

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And quantum support vector machines actually, originally, when they were originally implemented, they used Grover search algorithm.

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Which was they use that to minimize the cost function that helps separate the data.

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But now most implementations use some sort of these squares method.

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To train the data, which makes more sense and the algorithms.

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Kind of follows a pattern where it performs some quantum phase estimation.

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And then the H L algorithm which the algorithm stands for.

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Arrow has edema and Lloyd and.

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It solves linear systems of equations, which.

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For machine learning, that's all you deal with, which makes it very easy to kind of.

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Get this data separation.

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Um, and so the example I used was Parkinson's disease and.

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So, what happened was the.

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I have 10 data sets 5.

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5 of the data points are.

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Don't have Parkinson's and 5 due and each.

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Data set had is its very good point, I guess has 9 features.

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And a feature is a distinguishing characteristic.

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That can be used to identify whether they have, or don't have Parkinson's.

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And this is based off of their speech indicators.

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And I used quantum simulator, and because they have a lot of built in functions actually to do this. Um.

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I need just at a I use there it's you have to create a feature map.

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And then you just call their algorithm name.

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Which is and then you can run predict.

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And so I did that and I chose 9 because.

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You you want relatively the same amount of.

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Features as you do quits, which makes it difficult because most of the time when you're dealing with machine learning.

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You have large amounts of data and usually.

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A lot more features, it could be hundreds of features so makes it even more difficult.

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With current quantum computers, because they don't have enough credits to.

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Handle those large scale algorithms.

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Um, but I ran the algorithm and this was the output I got.

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I got a 50% accuracy, which is not good at all.

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But seeing how I only used 10 input data points.

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It actually was, it wasn't as bad as I thought it was going to be.

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Where I found this example, actually, they got a different output. They go to higher output.

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So, we can just show how different implementations can get different results.

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And to point to note on that, you kind of want above 96%.

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In some cases you want to even higher, depending on what you're using the algorithm for.

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So that's just something to keep in mind.

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And so 1 of the main issues actually that comes up with quantum machine learning is reading classical data into the quantum machines.

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Because you're dealing with so much data that you have to input into the machine and then.

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Process and then output. Yeah. There's just a lot that can go wrong.

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And so the input problem is.

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What happens when you have to read the data into the machine.

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Because it can take actually exponential time to read the data the classical data converting over to.

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Quantum States for the machine to understand.

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And that that exponential time, actually.

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Pretty much cancels out the exponential increase that.

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A normal quantum machine learning algorithm would obtain.

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A way to get around that from is to use quantum RAM or a cube.

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As the more memory you have, the more you can read it in, in parallel, read, all that input data in parallel. So.

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That would help cancel out the problem. But the thing is Watson RAM is very expensive.

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Which is the cost problem and so when you're trying to deal with.

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Millions of data points that that can get really expensive. Very fast.

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And but there is ways around this, so you can use different algorithms.

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To reduce the, the need for so much data.

348
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And so 1 is the linear housebroke based algorithm.

349
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Can kind of get around that problem, but the thing is with the linear How's your base algebra based problem?

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You run into the output problem and the problem is very similar to the input problem where you have to read.

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Pretty much measure the results from the processed algorithm to the.

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Classical computer to be understood and that could take exponential time.

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Especially for using when your algebra based approaches, because.

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They use the algorithm or some sort of principal component analysis, which.

355
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You'll get results for large amounts of data and.

356
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They're kind of harder to, since there's such a large vectors, they can take exponentially.

357
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Long to to estimate the results.

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And so those are so 3 of the main problems that kind of come up, there's also.

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The hardware problem where the hardware isn't quite up to speed to handle some of these algorithms. So, most of the outcomes are actually being.

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Develop mathematically, and not a lot of them have been implemented or tested.

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For the very reason of not having the right.

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Supporting hardware, there's also a benchmarking problem, which we haven't gotten to yet because.

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You have to be able to run the algorithm on the quantum computer to benchmark it against the classical computer. So, that would be a very difficult problem down the line.

364
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So, some, some current work going on right now is, uh.

365
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We're getting is developing a clustering algorithm and to run on their machine.

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And a clustering algorithm is in.

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An unsupervised learning algorithm, so, like I described before.

368
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It groups data up to then be understood in in future.

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In future cases, kind of like customers, or my example was Netflix.

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And then and going have been exploring mark of logic models on quantum platforms.

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And that's kind of used for to help with.

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Predictions and modeling systems.

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Far he and Evan have been working on classification with neural networks.

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Which would be very similar to that. Um.

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Uh, quantum support vector algorithm. I explained earlier.

376
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Alan it and his colleagues have been working on.

377
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Quantum machine learning as well as hybrid classical models.

378
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So, a hybrid classical model is, it takes the best parts of a classical algorithm and best parts of a quantum algorithm and mixes them together.

379
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So it could be, you need.

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You have the classical algorithm do most of the legwork.

381
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And then you only pass in some of the data into the quantum part.

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Of the algorithm, so it can only cross so processes what you need to do there. So maybe there's a specific simulation need to run on part of the data.

383
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But it has to be done in a quantum computer because of the, the atypical manner of the system you're trying to model.

384
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That could be something that would consider a hybrid classical model.

385
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But for a truly quantum machine learning model.

386
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It's actually very interesting because the goal is to develop.

387
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A quantum machine learning algorithm that takes in quantum data. That is.

388
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Could be some sort of performance benchmarking from.

389
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The CPU or some sort of quantum processor.

390
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It has the algorithm take that as input data.

391
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It process it and learns what they most.

392
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The most optimal performance of the CPU or some ship is and then it will feed.

393
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That back into the processors to try and.

394
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I take it to always perform ideally.

395
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And so that's just 1 way actually, the quantum machine learning algorithms could be applied.

396
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Solely using quantum data and so I guess that's everything I chose to cover. I do have.

397
00:43:25.349 --> 00:43:28.769
There are some actual applied examples.

398
00:43:28.769 --> 00:43:36.059
Of quantum algorithms that I didn't touch on, because I was going to focus mostly on the conceptual part, but I'll just mention them now.

399
00:43:36.059 --> 00:43:39.570
You can use quantum machine learning to help.

400
00:43:39.570 --> 00:43:45.780
Understand nano particles to create new materials through molecular anatomic maps.

401
00:43:45.780 --> 00:43:52.590
You can use it for to discover new drugs and medical research kind of like my Parkinson's disease. Example.

402
00:43:52.590 --> 00:43:56.250
I can use to understand the deeper makeup of the human body.

403
00:43:56.250 --> 00:43:59.309
Enhance pattern recognition and classification.

404
00:43:59.309 --> 00:44:03.269
Further space explanation, and it can be used to create.

405
00:44:03.269 --> 00:44:08.670
Completely connected security through merging of the Internet of things and blockchain.

406
00:44:08.670 --> 00:44:12.599
That's everything I have so.

407
00:44:12.599 --> 00:44:16.019
Uh, questions.

408
00:44:16.019 --> 00:44:19.260
Thank you very interesting. Anyone have a question.

409
00:44:21.719 --> 00:44:28.380
So we're getting is 1 of the leaders in this, and I guess, okay. Yeah I'm and so on.

410
00:44:28.380 --> 00:44:35.400
Uh, yeah, so Rick, Eddie and IBM are pushing for it and then also D wave.

411
00:44:35.400 --> 00:44:38.940
Since D wave users, quantum annealing, it's.

412
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Focuses on optimization actually.

413
00:44:41.940 --> 00:44:46.199
Cohen mentioned it and the other and his, um, industrial.

414
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Uh, quantum in the industry.

415
00:44:49.769 --> 00:44:54.090
Talk so that's just another actually.

416
00:44:54.090 --> 00:44:58.590
Way to connect the 2. okay. Thank you.

417
00:45:00.360 --> 00:45:06.389
Great. So, as I mentioned to Isaac, 1st, because, like, you are doing a 1 person talk, and then we'll do the 4.

418
00:45:06.389 --> 00:45:15.869
For Q2, so you can run off as long as you want at the end. Isaac are you are right there? You are.

419
00:45:25.530 --> 00:45:31.679
Isaac, um.

420
00:45:31.679 --> 00:45:37.920
Oh, good here. Yes, I can hear you. I'll mute myself now.

421
00:45:37.920 --> 00:45:42.329
Oh, okay. Yeah, I'm having some audio issues today. Let me know.

422
00:45:42.329 --> 00:45:56.400
You need me to stop or speak louder or anything like that. Your screen's coming to Greg.

423
00:45:56.400 --> 00:46:05.190
Okay, so this is quantum games. Isaac Phillips.

424
00:46:06.989 --> 00:46:10.980
So you might be thinking, oh, I'm sorry hold on 3rd.

425
00:46:10.980 --> 00:46:15.780
The, uh, the video I initially recorded is, uh.

426
00:46:16.405 --> 00:46:17.094
Plane over

427
00:46:34.465 --> 00:46:37.764
over again and this is quantum games.

428
00:46:39.570 --> 00:46:43.769
So the reason that you might want some quantum games, because, um.

429
00:46:43.769 --> 00:46:48.659
On some issues here.

430
00:47:03.030 --> 00:47:08.760
Okay, the reason you would want quantum gains is mostly the industry is looking to.

431
00:47:08.760 --> 00:47:12.090
Attract talent really they want to.

432
00:47:12.090 --> 00:47:16.380
Not only have the current implementations for.

433
00:47:16.380 --> 00:47:19.440
Biology and chemistry, but also kind of track.

434
00:47:19.440 --> 00:47:25.710
Talent from a different background so you're getting programs is 1 of the areas that they're looking at.

435
00:47:25.710 --> 00:47:31.650
1 of the, I'll talk about the pitfalls as well.

436
00:47:31.650 --> 00:47:35.519
And the current implementation and.

437
00:47:35.519 --> 00:47:42.000
1 of the implementations is this quantum blur where originally when I was researching.

438
00:47:42.000 --> 00:47:49.349
We're going to talk about just games, but when I discovered this awesome blur technique, I shifted my focus and.

439
00:47:49.349 --> 00:47:54.750
Mainly talking about that, and then I'll wrap up with when I continue to work.

440
00:47:54.750 --> 00:48:01.230
And the things that I've done, and in regards to, um, quantum blur as well as.

441
00:48:01.230 --> 00:48:08.340
Uh, video games, so, like I said.

442
00:48:09.869 --> 00:48:12.900
The push for video games right now it's mostly a.

443
00:48:12.900 --> 00:48:17.610
Uh, recruitment assignment and I'm getting some new blood into the.

444
00:48:17.610 --> 00:48:21.449
Sphere of quantum another thing is that.

445
00:48:21.449 --> 00:48:27.480
Um, people have looked at the history of video games as far as the console wars.

446
00:48:27.480 --> 00:48:33.480
And how those have cause a lot of innovation and hardware. So they're hoping that.

447
00:48:33.480 --> 00:48:37.469
And create a priest interest and quantum that's going to help.

448
00:48:37.469 --> 00:48:43.260
Push for the next generation of where we're going, as far as both the hardware and the software.

449
00:48:43.260 --> 00:48:47.250
And ironically the thing that.

450
00:48:47.250 --> 00:48:51.630
Everyone is trying to get rid of right now. The noise is actually what.

451
00:48:51.630 --> 00:48:58.079
Is benefiting video games because there's a.

452
00:48:58.079 --> 00:49:01.530
Sort of randomness to it that you can't find.

453
00:49:01.530 --> 00:49:10.440
And classical computing, so the pitfalls to.

454
00:49:10.440 --> 00:49:17.070
The pitfalls to using a quantum computer, is that the is a limited number of that.

455
00:49:17.070 --> 00:49:21.480
So, the systems that we've been using here in class.

456
00:49:21.480 --> 00:49:24.929
Clock in at less than 50 and, um.

457
00:49:24.929 --> 00:49:30.900
The community that's working on quantum games is aiming for 20, because that's a.

458
00:49:30.900 --> 00:49:35.550
Fairly low number that can be simulated on a home computer.

459
00:49:35.550 --> 00:49:38.699
Instead of using some of the computers.

460
00:49:38.699 --> 00:49:42.389
Another pitfall is the quantum advantage.

461
00:49:42.389 --> 00:49:46.409
Out of all the things that are being done in quantum gaming right now.

462
00:49:46.409 --> 00:49:52.500
There's nothing that's truly unique in the sense of you can't do it on a classical computer.

463
00:49:52.500 --> 00:49:57.449
And a lot of that, like I said is do the hardware limitations.

464
00:49:57.449 --> 00:50:01.710
But the framework is being built right now so that.

465
00:50:01.710 --> 00:50:05.610
In the future.

466
00:50:05.610 --> 00:50:09.179
Isn't the same technology we'll be able to do some great things with it?

467
00:50:11.070 --> 00:50:15.360
So the 2 current implementations of using quantum and video games are.

468
00:50:15.360 --> 00:50:19.230
Random number generator as far and, uh, as well as procedural generation.

469
00:50:22.829 --> 00:50:28.980
So, the 1st, quantum game was made by Dr James and and he is.

470
00:50:28.980 --> 00:50:32.730
It was going to come up a lot because he's the real pioneer for.

471
00:50:32.730 --> 00:50:36.059
Quantum video games he himself has developed.

472
00:50:36.059 --> 00:50:43.170
At least a dozen different games, but this was the 1st 1 and it's a rock paper scissors clone.

473
00:50:45.030 --> 00:50:48.869
And the way it works is based off of and, um.

474
00:50:48.869 --> 00:50:52.170
As aggregates, or the conjugate of an escape.

475
00:50:52.170 --> 00:50:55.889
And basically how these gates work is a 90 degree.

476
00:50:55.889 --> 00:50:59.670
Rotation long as the access so, um.

477
00:50:59.670 --> 00:51:02.880
2 of them to the same Gates will cause and not.

478
00:51:02.880 --> 00:51:06.090
And to.

479
00:51:06.090 --> 00:51:09.389
Different Gates will result on. No change.

480
00:51:09.389 --> 00:51:13.139
The goal of the game is that the player picks 1.

481
00:51:13.139 --> 00:51:20.699
Either escape or a, or as dagger and then the computer is a superposition both.

482
00:51:20.699 --> 00:51:25.139
So the plan doesn't know which date the, um, the computer chose.

483
00:51:25.139 --> 00:51:28.949
But if the player in the gate and the computer to the same gate.

484
00:51:28.949 --> 00:51:35.579
Then they win if they're different than they lose. So that's just how the random number generator is the.

485
00:51:35.579 --> 00:51:43.349
Built in to here the way function collapse algorithm is actually a classical algorithm.

486
00:51:43.349 --> 00:51:50.309
But it said to be quantum inspired the way it works. So you can kind of think of it as a surgical puzzle where.

487
00:51:50.309 --> 00:51:54.869
There are all these empty boxes that have the possibility of being.

488
00:51:54.869 --> 00:51:58.440
Any numbers here of deny and.

489
00:51:58.440 --> 00:52:05.880
As you start to figure out 1, bot, you can eliminate the possibilities and the other boss and I have a simple.

490
00:52:05.880 --> 00:52:09.449
Example of that here on the right where I created 2 rules.

491
00:52:09.449 --> 00:52:14.010
Where there are 4 boxes and.

492
00:52:14.010 --> 00:52:18.269
Red is always somewhere little left of blue. Yellow is not touching green.

493
00:52:18.269 --> 00:52:22.019
So, in the beginning, they're all gray and you don't know which is where.

494
00:52:22.019 --> 00:52:31.530
You can immediately eliminate the blue box all the way on the left. Um, because the red box has to be the left, the blue.

495
00:52:31.530 --> 00:52:36.840
So, you can eliminate that and then you just pick 1 color from the remaining.

496
00:52:36.840 --> 00:52:40.800
Boxes and then on the next row.

497
00:52:40.800 --> 00:52:44.849
You can again eliminate blue because it can't be.

498
00:52:44.849 --> 00:52:48.690
It can't be to the left of red.

499
00:52:48.690 --> 00:52:53.550
So, basically you just go through and you eliminate the possibilities and until.

500
00:52:53.550 --> 00:53:02.579
You find the correct match anywhere where there is possibility there being multiple. You just take a guess.

501
00:53:02.579 --> 00:53:06.329
Now, this, like I said, this has been a quantum.

502
00:53:06.329 --> 00:53:09.989
Algorithm, but it is, um, currently.

503
00:53:09.989 --> 00:53:14.579
Dr. blue and then a lot of people are really trying really hard to.

504
00:53:14.579 --> 00:53:18.659
Implement in quantum, because it will help and.

505
00:53:18.659 --> 00:53:23.969
Procedural generation.

506
00:53:23.969 --> 00:53:32.219
So, when I discovered this some quantum blur, this is kind of where I, I stopped all my research into quantum games and I just look into this.

507
00:53:32.219 --> 00:53:37.619
And, um, this is a method of procedural generation using quantum.

508
00:53:37.619 --> 00:53:41.610
Misstatement just came out in July of this year.

509
00:53:41.610 --> 00:53:50.219
Again by Dr, James, what it does is, it basically takes a image.

510
00:53:50.219 --> 00:53:53.460
And it maps it to a string of.

511
00:53:53.460 --> 00:53:56.519
Final digit.

512
00:53:59.219 --> 00:54:04.710
And these digital map in such a way that the distance of each.

513
00:54:04.710 --> 00:54:09.150
Pixel, it's equal to the hammy distance of that. Same pixel.

514
00:54:09.150 --> 00:54:13.050
So any change in any direction of the X and Y.

515
00:54:13.050 --> 00:54:19.199
That same amount of change is equal to the number of bits you would have to change in the spring.

516
00:54:19.199 --> 00:54:23.519
And the way this all works is, it allows you to encode a pixel.

517
00:54:23.519 --> 00:54:28.320
Location X, Y, position to a small number of Cubics.

518
00:54:28.320 --> 00:54:34.710
You can perform your locations or any other operations need on the cube and then convert it back to.

519
00:54:34.710 --> 00:54:38.880
A position, so basically the probability of the cube.

520
00:54:38.880 --> 00:54:43.860
Is what determines the value of the pixel and I'll get to the pictures that kind of solidify that.

521
00:54:46.349 --> 00:54:49.469
Um, so, yeah, this is what I was saying before, that the.

522
00:54:49.469 --> 00:54:54.780
Once it's encoded as a Q that you just perform your.

523
00:54:54.780 --> 00:54:59.219
Algorithm on it Dr. was is recommended a.

524
00:54:59.219 --> 00:55:04.590
Rotation around the Y axis, but I use X axis because it's a little bit easier to program.

525
00:55:04.590 --> 00:55:12.750
So, the amount of the rotation is in the range of 0T to pie, and I'll get to why that is just a 2nd.

526
00:55:16.619 --> 00:55:22.889
This is my 1st example and what I did was I kind of, um.

527
00:55:22.889 --> 00:55:28.980
I kind of replicated a little bit his what he did in his paper where he took a.

528
00:55:28.980 --> 00:55:33.840
16 by 16 grid and black, and just put a couple of white dots in there.

529
00:55:33.840 --> 00:55:38.730
And then ran it through the algorithm and.

530
00:55:38.730 --> 00:55:44.130
The way that this 1 was made is, it has made in Python using numb pie. That's helped.

531
00:55:44.130 --> 00:55:51.119
He did it his 1st time basically just say you specify it as a, as a, um.

532
00:55:51.119 --> 00:55:55.500
A dictionary, which with the value here is 0T and here is 1.

533
00:55:55.500 --> 00:56:04.559
And then you run it through I use a beta 3.3 pie divided by 10, and it resulted in the image to the right and that's the blur that you start to see.

534
00:56:04.559 --> 00:56:12.030
And the values around the white spots are all randomly.

535
00:56:12.030 --> 00:56:15.239
Generated based off the probabilities of the cube.

536
00:56:18.750 --> 00:56:24.630
The next thing I wanted to try was using a color image with a couple of different shapes to see what kind of effect it would have.

537
00:56:24.630 --> 00:56:28.079
You can see that it had the same learning process.

538
00:56:28.079 --> 00:56:31.739
But then it kind of changed the colors a little bit like that. Um.

539
00:56:31.739 --> 00:56:36.119
It's most evident in the blue rectangle to the bottom right there.

540
00:56:36.119 --> 00:56:41.159
This picture required me to.

541
00:56:41.159 --> 00:56:49.829
Make a, uh, basically make a almost a new algorithm, because instead of being a black and white image now, I had 3 channels.

542
00:56:49.829 --> 00:56:54.119
That I had to work on separately and then combine them all at the end.

543
00:56:54.119 --> 00:57:02.039
I didn't want to do something a little bit more complex.

544
00:57:02.039 --> 00:57:06.659
This image actually, I thought it was going to be easy because it was black and white image.

545
00:57:06.659 --> 00:57:10.019
Well, they've gave me a lot of problems because.

546
00:57:10.019 --> 00:57:13.199
The way that.

547
00:57:13.199 --> 00:57:18.539
The algorithm was written initially, was that you were expected to.

548
00:57:18.539 --> 00:57:25.920
Just make 1 or 2 points in Python itself not to. But on this picture, I drew it out.

549
00:57:25.920 --> 00:57:37.230
And a paint program imported it, and then I had to I had to find a way to convert it to be able to using this algorithm. This was the hardest part for me.

550
00:57:37.230 --> 00:57:40.289
To get this, uh, Android image.

551
00:57:40.289 --> 00:57:46.320
Into the algorithm, but I ended up making a couple of functions and.

552
00:57:46.320 --> 00:57:50.639
Got it to work and the image on the right is what you see after a.

553
00:57:50.639 --> 00:57:54.269
2 pie divided by 10 rotation around the.

554
00:57:54.269 --> 00:58:03.630
X axis I didn't want to experiment and do do it again with a couple of different values of data.

555
00:58:03.630 --> 00:58:06.630
So, the small picture of the original.

556
00:58:06.630 --> 00:58:11.969
And then next to that is the rotation of 1.

557
00:58:11.969 --> 00:58:16.050
Times pie, and then next to that is point 2 times by.

558
00:58:16.050 --> 00:58:21.239
The bottom left is point 3 times by and then the final is just pie.

559
00:58:21.239 --> 00:58:24.780
That's when you break down into this.

560
00:58:24.780 --> 00:58:28.260
Kind of weird Blackie.

561
00:58:28.260 --> 00:58:33.989
Monochrome shape, and the reason it's limited to is because once you get past that.

562
00:58:33.989 --> 00:58:40.920
You're starting to head back towards your image if you think of it on a square, or? I'm sorry on the on the sphere.

563
00:58:40.920 --> 00:58:47.550
And once the rotation gets passed pie, you're heading back towards your initial position. So once you get to 2 Pi.

564
00:58:47.550 --> 00:58:51.719
It's equal to a, a rotation at 0T. So you're back at the.

565
00:58:51.719 --> 00:58:55.409
The, um, the same image.

566
00:58:55.409 --> 00:59:03.119
So, as far as this related to games, um, this whole image can, the whole blur can be used as a map.

567
00:59:03.119 --> 00:59:06.630
For say a train.

568
00:59:06.630 --> 00:59:12.030
So, I built this function to kind of plot out what the image would look like. Um.

569
00:59:12.030 --> 00:59:19.409
After the quantum transform plugged in the original image, and you can see that on the right there.

570
00:59:19.409 --> 00:59:23.400
And then I also plugged in to 3, 5 or 10 image.

571
00:59:23.400 --> 00:59:29.400
And you can see that to the right but the problem with this 1 is that it was kind of, um.

572
00:59:29.400 --> 00:59:36.420
The resolution so high in the and the points for so Jag, it's not really useful for anything.

573
00:59:36.420 --> 00:59:41.519
So that's 1 of the things I want to change that in my work.

574
00:59:43.949 --> 00:59:47.820
After I did the.

575
00:59:47.820 --> 00:59:56.130
The image I want to go back and do the 3 dot images that I had did originally, and run it through the hype map as well.

576
00:59:56.130 --> 01:00:02.849
You see the 3 D, part of it and this 1 looks a little better. But again, I think it's just because of the resolution. This is, um.

577
01:00:02.849 --> 01:00:07.320
16 by 16 where the other 1 was 256 by 6.

578
01:00:07.320 --> 01:00:14.969
So this 1, like I said, looks a little bit better as far as if you were to use this in a video game, you could see it may be being.

579
01:00:16.139 --> 01:00:20.579
You know, maybe a mountainous scene, but again, it's still those points is still a little bit to.

580
01:00:20.579 --> 01:00:29.309
Too high sync, so continued work. Like I said, I'm going to adjust the heights and the slopes of everything to be a little bit.

581
01:00:29.309 --> 01:00:33.300
More natural, as far as what actually occurs in nature.

582
01:00:33.300 --> 01:00:40.590
I also want to implement valleys because right now you start at a base that you can only go up from there.

583
01:00:40.590 --> 01:00:44.489
So, I had tried to do.

584
01:00:44.489 --> 01:00:49.409
Something like that on the right here. So I started with the image at the top where the black.

585
01:00:49.409 --> 01:00:54.000
Would be a low area, so maybe like the sea or a river.

586
01:00:54.000 --> 01:00:58.949
And then the gray would be sea level and then the White is a, a mountain.

587
01:00:58.949 --> 01:01:02.130
I run it ran it through the the, um.

588
01:01:03.449 --> 01:01:06.929
The the algorithm, but I forgot that.

589
01:01:06.929 --> 01:01:11.070
This is no longer a binary picture, because it's 3 colors here.

590
01:01:11.070 --> 01:01:14.369
So, it didn't work at all. Um, it just.

591
01:01:14.369 --> 01:01:18.960
Just spit out a white image. I ran it through the, um.

592
01:01:18.960 --> 01:01:26.429
The color algorithm, and it kind of resulted in something a little bit better, but it's still.

593
01:01:26.429 --> 01:01:31.860
Not perfect as you can see these guys, like, weird rectangular sections that don't make any sense.

594
01:01:31.860 --> 01:01:36.510
So, I'm going to fix that and hopefully I'll be able to get it into the final report.

595
01:01:36.510 --> 01:01:40.860
The other thing I want to add is color, so there's.

596
01:01:40.860 --> 01:01:44.400
Like, drain and foliage and stuff like that and.

597
01:01:44.400 --> 01:01:47.460
That might require a.

598
01:01:47.460 --> 01:01:52.110
A different rotation along the blogosphere or.

599
01:01:52.110 --> 01:01:56.610
Maybe some other algorithm, and I'm still working on that to get it. Right it's not perfect yet.

600
01:01:56.610 --> 01:02:05.280
And lastly, the thing I want to do is that I want to contribute the work that I've done so far back into the main.

601
01:02:05.280 --> 01:02:12.690
Often blur project, it's on get hub on Dr. it's actually on the, his kit.

602
01:02:12.690 --> 01:02:18.420
Community page, but he commits to it so I'll be.

603
01:02:18.420 --> 01:02:25.199
Submitting a poll request probably then during the break, once I have a little bit more time to.

604
01:02:25.199 --> 01:02:31.440
Thinking around that, these are my references. Like I said, the.

605
01:02:31.440 --> 01:02:36.570
The gate hub repo is there at the bottom.

606
01:02:36.570 --> 01:02:40.619
And, um, Isaac shorts Dan again.

607
01:02:40.619 --> 01:02:50.789
Any questions.

608
01:03:04.650 --> 01:03:04.739
I

609
01:03:04.764 --> 01:04:40.675
am.

610
01:04:45.630 --> 01:04:50.309
Yep, yeah, I can hear you. Yep. Share my screen.

611
01:04:52.590 --> 01:04:57.360
And you guys do the presentation.

612
01:05:01.530 --> 01:05:12.630
Yep. All right so our group's going to talk about top logical case words, and introduce themselves.

613
01:05:12.630 --> 01:05:17.519
Nick Murphy name is Nathaniel page.

614
01:05:17.519 --> 01:05:31.019
Recorded yes, so just a brief overview of the agenda. 1st Ricardo is going to be going over the science of both how they work and how they're built.

615
01:05:31.019 --> 01:05:43.739
And then Nathaniel or Nick is going to go over the history, I'm going to go over the advantages and disadvantages. And then the Daniel is going to go over top logical quantum computing today.

616
01:05:43.739 --> 01:05:51.989
All right, everyone so, today I'm going to talk to you about how top logic keep it work and how we can use them to compute.

617
01:05:51.989 --> 01:06:01.889
So, let's start with the high level overview of what makes top logical to keep it so special. So, topological units are fundamentally different from Cupid.

618
01:06:01.889 --> 01:06:08.550
And that they create 3 dimensional space time grapes and then I'll go over explain what that means shortly.

619
01:06:08.550 --> 01:06:13.619
And, in fact, it's the topology that's traded on these braids.

620
01:06:13.619 --> 01:06:16.829
Uh, which are utilized to perform quantum calculations.

621
01:06:16.829 --> 01:06:27.239
And what special about using topology on these cubiks and are examining their topology rather, is that the brains that they treat are inherently more robust.

622
01:06:27.239 --> 01:06:34.289
In other forms of kibbutz and the break apologies far less susceptible to quantity coherence. Meaning that.

623
01:06:34.289 --> 01:06:42.030
There's a smaller transfer to cause an error when this system.

624
01:06:42.030 --> 01:06:49.980
Even though these cubes sound really great. Unfortunately, they're still only theoretical and I'll talk a little bit more about.

625
01:06:49.980 --> 01:06:58.139
How they work next slide this so.

626
01:06:58.139 --> 01:07:06.570
We're going to dive a little deeper into how this work and firstly, let me briefly touch on topology because you'll see soon.

627
01:07:06.570 --> 01:07:21.150
See, that the advantages of these Cuba try a lot into power. So topology is a branch of mathematics that studies properties of objects surfaces, manifolds and how they behave when they're smoothly.

628
01:07:21.150 --> 01:07:28.139
In essence, any quantum computation on the stupids is some top a logical defamation.

629
01:07:28.139 --> 01:07:37.380
Purely mathematically model these topological as a.

630
01:07:37.380 --> 01:07:44.369
Breakthrough as you see on the right here on the top, that's kind of like an abstract mathematical object that these.

631
01:07:44.369 --> 01:07:50.820
Topological Cuba checked reading, you're passing grades over each other and then looking back.

632
01:07:50.820 --> 01:07:54.389
Depending on how the brakes are laid out.

633
01:07:54.389 --> 01:08:01.949
You can perform quantum computation on them so the math group allows us to form these operations on models.

634
01:08:01.949 --> 01:08:06.929
In order to perform computations and these brains are treated.

635
01:08:06.929 --> 01:08:13.980
I've created by the and can be represented as a transformation transformation transformation matrix.

636
01:08:13.980 --> 01:08:17.760
That acts like many of the quantum Gates we've studied before.

637
01:08:17.760 --> 01:08:28.680
As you can see on below the other image that there's some of these topological braids, and you can build a, from the students next slide this.

638
01:08:28.680 --> 01:08:34.829
So, let's talk a little bit about what specific properties these Cubics need.

639
01:08:34.829 --> 01:08:39.510
In order to have in order to construct the template to keep it.

640
01:08:39.510 --> 01:08:44.520
So, they are constructed of pairs of 2 dimensional quasi particles.

641
01:08:44.520 --> 01:08:59.520
Known as an Adams, and since they're travel only on 2 dimensional space and 1 time dimension, that's where we get the 3 dimensional space type rates. So these are earned reality just 2 pairs of particles that can only making 2 dimensions.

642
01:08:59.520 --> 01:09:06.989
And through time, so they create 3 dimensional space time rates.

643
01:09:08.130 --> 01:09:15.810
Um, the quasi particles are special in that, the exhibit fractional charges, compared to an electron. So they don't have.

644
01:09:15.810 --> 01:09:27.029
And now the charges that those and ion pairs have are fraction are less than that in an electron. I'll talk to more about this in terms of the quantum politics.

645
01:09:27.029 --> 01:09:34.289
To the operations that can perform on these stupids are clockwise rotation and conquer counterclockwise rotation.

646
01:09:34.289 --> 01:09:42.630
They must also be not a, what this means is that a left hand turn is fundamentally different from a right turn.

647
01:09:42.630 --> 01:09:46.770
So, if you see the image that the contract clockwise swap.

648
01:09:46.770 --> 01:09:50.250
The green brain is on top of the blue brain, whereas.

649
01:09:50.250 --> 01:09:58.949
In the counter, clockwise swap, the, it's the other way around and it's important that we, these ions are not appealing because.

650
01:09:58.949 --> 01:10:04.350
If they are billing, then those 2 operations would really be the same and then.

651
01:10:04.350 --> 01:10:08.579
We wouldn't be able to perform any computations.

652
01:10:08.579 --> 01:10:12.149
Next slide please.

653
01:10:12.149 --> 01:10:17.909
How I'm going to touch on how physics physicists can practically make.

654
01:10:17.909 --> 01:10:24.659
These, it's a pleasure particles and although a non appealing pair has yet to be, uh, discovered.

655
01:10:24.659 --> 01:10:28.859
Um, appealing pairs have been documented we need the, the types that swap.

656
01:10:28.859 --> 01:10:32.039
Either our left hand, right hand, but it's the same.

657
01:10:32.039 --> 01:10:38.939
Um, so this image, uh, is a colorized micro graph of 4 gathering are denied semiconductor electrodes.

658
01:10:38.939 --> 01:10:43.529
And there's an electron gas interface gap. It's obviously not a guess, but it's just.

659
01:10:43.529 --> 01:10:49.050
And interface with free electrons and they're bounded to 2 dimensions.

660
01:10:49.050 --> 01:10:54.270
So, the electrons on that interface can travel freely in those 2 dimensions.

661
01:10:54.805 --> 01:11:07.465
And when this configuration creates something known as a 2 dimensional gas, which, as you can imagine is an electron gas bond into dimensions.

662
01:11:07.524 --> 01:11:11.635
And when a transverse magnetic field supplied to the gas.

663
01:11:11.880 --> 01:11:21.539
These fractional charges that we need in their ions are detected. So we can see that. There are a 1B. I'm pairs.

664
01:11:21.539 --> 01:11:27.359
In this the yellow specifically area of this.

665
01:11:27.359 --> 01:11:31.890
Experiment and the fractional charge of the.

666
01:11:31.890 --> 01:11:36.239
Uh, and I'm curious as a result of the quantum all effect, fractional model effect.

667
01:11:37.470 --> 01:11:39.204
Right. That's it for me.

668
01:11:43.944 --> 01:11:52.734
All right so now I'll talk a little bit about the history of these how the theory was developed and how that theory will.

669
01:11:53.069 --> 01:11:56.909
Eventually we to topological quantum computers.

670
01:11:56.909 --> 01:12:00.569
1 of the things that you'll notice is because this is such a.

671
01:12:00.569 --> 01:12:03.720
A new, new and recent field.

672
01:12:03.720 --> 01:12:09.420
All of this history is, is happening in the last 20, 30 years or so.

673
01:12:09.420 --> 01:12:17.819
So, I've in these next few slides, I'll lay out sort of a timeline as to how this theory has been developed.

674
01:12:17.819 --> 01:12:29.760
So, it all sort of started in the early nineties when the fractional quantum hall effect was actually observed in nature by these 2 researchers.

675
01:12:29.760 --> 01:12:39.180
The system that they that they saw that is the fractional quantum hall effect was a system in which a bunch of electrons were arranged.

676
01:12:39.180 --> 01:12:46.380
And their ground level state, and their low level excitation States basically were.

677
01:12:46.380 --> 01:12:50.010
Stable enough that it didn't matter.

678
01:12:50.010 --> 01:12:54.510
You know, what sort of local innovations? Each of those electrons experienced.

679
01:12:54.510 --> 01:12:58.350
No matter what they still remained in the same formation.

680
01:12:58.350 --> 01:13:08.579
So this was exactly the type of system that would that would be needed for fault, tolerant, quantum computing. And this kind of sparked the idea of, hey, maybe we can use this quantum hall effect.

681
01:13:08.579 --> 01:13:12.600
To create an extremely fault, tolerant, quantum computer.

682
01:13:12.600 --> 01:13:15.899
And so then in 90, 97.

683
01:13:15.899 --> 01:13:20.159
Alexa described the principles of.

684
01:13:20.159 --> 01:13:25.710
How to use this principle to create a topological quantum computer.

685
01:13:25.710 --> 01:13:31.109
This was considered to be sort of the breakthrough paper and topological quantum computing.

686
01:13:31.109 --> 01:13:42.119
And he describes how we can perform unitary transformations by taking the, those pair of that Ricardo had described.

687
01:13:42.119 --> 01:13:45.659
And rotating them in different ways around each other.

688
01:13:45.659 --> 01:13:51.899
And then he also describes how measurements can be taken and that is by basically.

689
01:13:51.899 --> 01:13:55.770
You take these and yon pairs and then.

690
01:13:55.770 --> 01:14:02.880
Fuse them back together and it turns out that the fusion of non a 1B yards.

691
01:14:02.880 --> 01:14:09.149
Results in a particle whose statistics are not determined by the components, but rather exist in a sort of.

692
01:14:09.149 --> 01:14:12.390
Quantum superposition. So it.

693
01:14:12.390 --> 01:14:19.140
Makes it very easy to basically just measure this added all sort of collapses into probabilistic measurement.

694
01:14:22.470 --> 01:14:23.845
Next slide. Please. Thanks.

695
01:14:25.795 --> 01:14:26.005
Yeah,

696
01:14:26.005 --> 01:14:26.154
so,

697
01:14:26.154 --> 01:14:26.845
then in 2000,

698
01:14:26.845 --> 01:14:28.524
these 4 researchers,

699
01:14:28.524 --> 01:14:29.845
Michael Friedman again,

700
01:14:30.533 --> 01:14:30.864
Alexa,

701
01:14:30.864 --> 01:14:32.545
Kitty of Michael Larson,

702
01:14:32.545 --> 01:14:41.545
and senior 1 proved in a series of papers that a topological quantum computer can perform any computation that a conventional quantum computer can do.

703
01:14:42.149 --> 01:14:48.750
And they, they proved that by some really complex mathematics, but.

704
01:14:48.750 --> 01:15:00.149
That statement is pretty crucial. So now we know that this topological quantum computing method is just as strong as any standard quantum circuit model that we have already.

705
01:15:00.149 --> 01:15:04.770
And in another paper, in that same year, they also.

706
01:15:04.770 --> 01:15:15.060
Prove that it can't be used that that is topological quantum computing. Can't be used to define a model of computation stronger than the usual quantum circuit model.

707
01:15:15.060 --> 01:15:23.310
And they prove that by basically showing that you can simulate a topological quantum system on a conventional quantum computer.

708
01:15:23.310 --> 01:15:31.170
So you're not able to do anything more powerful or less powerful on a contact topological, quantum computer.

709
01:15:31.170 --> 01:15:36.600
And then 4 years later in 2004.

710
01:15:37.949 --> 01:15:46.710
Michael Friedman soccer summer and developed a specific scheme for performing quantum computation.

711
01:15:46.710 --> 01:15:50.430
And in a very specific fractional quantum hall state.

712
01:15:50.430 --> 01:15:54.510
And so these 4 people who are listed at the bottom are kind of the.

713
01:15:54.510 --> 01:15:59.609
The main people leading this research, and it's still ongoing today. Of course.

714
01:15:59.609 --> 01:16:06.300
And although at the end, Nyack is actually a member of the Microsoft quantum research team.

715
01:16:06.300 --> 01:16:11.369
And they're doing a lot of work on top logical, quantum computing, Nathaniel we'll talk about towards the end.

716
01:16:11.369 --> 01:16:26.159
Next slide please. All right so then a few years later in 2009, the 1st, sort of large scale topological, cluster state quantum architecture was developed for Adam optics.

717
01:16:26.159 --> 01:16:33.750
Um, so this architecture was proposed, it wasn't actually built because they, we don't really know how to do it yet.

718
01:16:33.750 --> 01:16:45.000
But the architecture is promising for the creation of large scale across computers with active error correction and is sort of the 1st, real architecture developed using this top of logical theory of quantum computing.

719
01:16:45.000 --> 01:16:51.180
And then in the following year, a team of researchers at Rice University and Princeton University.

720
01:16:51.180 --> 01:16:54.810
Found evidence of this phenomenon called spin polarization.

721
01:16:54.810 --> 01:17:07.560
Um, in this specific quantum hall state, this is 1 of the 2 properties, or 1 of the 2 conditions. That's that we have to prove in order to show that this particular quantum state is not a 1000000000.

722
01:17:07.560 --> 01:17:11.039
That is, it can result in the creation of these non 1000000000.

723
01:17:11.039 --> 01:17:22.560
And yards that we need for quantum computing so this paper sort of brought us 1 step closer to verifying that. We can use this quantum all state in order to create topological Cubans, which would be something.

724
01:17:22.560 --> 01:17:27.720
Pretty crucial and in terms of the development of these topological quantum computers.

725
01:17:27.720 --> 01:17:39.510
And then just the following year that same team of researchers at race university created what they called a quantum spin hall topological insulator.

726
01:17:39.510 --> 01:17:46.350
Or, in other words, a tiny electron superhighway designed for increased full tolerance.

727
01:17:46.350 --> 01:17:55.859
So this device in conjunction with a Super conductor, in theory could generate, which could be candidates for stable.

728
01:17:55.859 --> 01:18:06.000
So, all these developments are basically getting us baby steps closer to being able to actually create these, not a 1B that we need in order to.

729
01:18:06.000 --> 01:18:10.380
Perform topological, quantum computing next slide please.

730
01:18:13.109 --> 01:18:21.149
So, in 2012, a paper was released, describing and documenting the 1st, experimental demonstration of topological error correction.

731
01:18:21.149 --> 01:18:25.560
With an 8 foot on Cuba, optimal cluster state. So they basically.

732
01:18:25.560 --> 01:18:33.329
Arranged eat fulltime and encoded them in such a way that it sort of simulates a topological quantum or a topological keep it.

733
01:18:33.329 --> 01:18:41.819
And then they go on to prove that this error correction is possible and is superior to existing forms of quantum error correction.

734
01:18:41.819 --> 01:18:51.569
Um, it's error rate, or if I remember correctly is much much lower orders of magnitude lower than the error rate. That would be found.

735
01:18:51.569 --> 01:18:55.619
On standard quantum computing models.

736
01:18:55.619 --> 01:19:02.069
So this paper then in their conclusions, suggests that tough electrical error correction is quote a critical ingredient.

737
01:19:02.069 --> 01:19:10.710
For future, large scale of quantum computation and then 2 years later in 2014 scientists at the University of.

738
01:19:10.710 --> 01:19:17.100
Do quantum computations on top of logically encoded, keep it again, using sort of the same method.

739
01:19:17.100 --> 01:19:22.170
Where they included it in entangled States distributed over trapped, die on Cuba.

740
01:19:22.170 --> 01:19:29.250
So, they apply single cubic gates to this cubic to explore the computational capabilities.

741
01:19:29.250 --> 01:19:38.279
Um, sort of opening the door towards again, our, our main goal of creating these topological fault, tolerant, wanting computers.

742
01:19:38.279 --> 01:19:48.390
Next slide please send 1 year later in 2015 about will it of the labs.

743
01:19:48.390 --> 01:19:52.529
Began construction of a topological cubic through the use of.

744
01:19:52.529 --> 01:19:57.630
Very pure, very cold and very magnetized crystals.

745
01:19:57.630 --> 01:20:03.539
Now, he believes that he can create a pair of in superposition.

746
01:20:03.539 --> 01:20:08.069
And can modify their states by breeding a current of ambulance around them.

747
01:20:08.069 --> 01:20:13.710
So, he, he's sort of 1 of the researchers who has.

748
01:20:13.710 --> 01:20:18.659
Verified the existence of these of these.

749
01:20:18.659 --> 01:20:23.489
He's carried out a ton of different experiments. It has.

750
01:20:23.489 --> 01:20:29.609
Basically gathered evidence that verifies the existence of not a 1B annually.

751
01:20:29.609 --> 01:20:34.619
His work has not been able to be replicated by any other laboratory as of yet.

752
01:20:34.619 --> 01:20:45.810
Um, but many experts in the field who have looked at his work, believe that it's not an issue of the results but as an issue of the experiment being carried out, it's just a very delicate process.

753
01:20:45.810 --> 01:20:52.020
So, again, we're just getting closer and closer to being able to create these particles that we need.

754
01:20:52.020 --> 01:21:02.220
In order to carry out topological, quantum computing and then lastly, in 2019, 1, other possible substrate other than Kelly and.

755
01:21:02.220 --> 01:21:15.029
Um, for building these topological cubes is found titled uranium. The Telluride, this was actually an accidental discovery, but the Super conductor was found to have what's called a spin triplet state.

756
01:21:15.029 --> 01:21:25.079
Which has a really complex description, but basically, it just means that it can be a topological Super conductor and could also generate these not a 1B annually that we need.

757
01:21:25.079 --> 01:21:31.020
So, again, this is a very active area of research. There's still a lot to go in order to.

758
01:21:31.020 --> 01:21:36.869
There's still a lot that we have to figure out before we can build an actual topological quantum computer.

759
01:21:36.869 --> 01:21:41.430
But each of these each of these steps is kind of like we're getting closer closer.

760
01:21:41.430 --> 01:21:48.539
So, hopefully, you know, maybe the next 20 or 30 years, we can figure out something.

761
01:21:48.539 --> 01:21:52.890
That allows us to do this highly fault tolerant computing.

762
01:21:58.885 --> 01:21:59.215
So,

763
01:21:59.215 --> 01:22:01.194
for the advantages and disadvantages,

764
01:22:01.225 --> 01:22:01.823
I'm 1st,

765
01:22:01.823 --> 01:22:16.135
going to go over quickly what makes a quantum computer and then I'm going to talk about what is known about top logical candidates right now and then also the advantages and disadvantages of other 2 books that have already been.

766
01:22:16.529 --> 01:22:21.659
Produce so these are the 5 main.

767
01:22:22.770 --> 01:22:32.159
Tab objects that make a good quantum computer. So the 1st, 1 is long cubic coherence, which means that.

768
01:22:32.159 --> 01:22:40.140
The 2 bits are staying in superposition for longer periods of time so that you can leverage the quantum mechanical properties.

769
01:22:40.140 --> 01:22:48.359
So far, I, on track have the highest cubic coherence science, which is around 1000 seconds.

770
01:22:48.359 --> 01:22:58.590
Also, good quantum computer requires that the Cubans are highly connected, meaning that they're in an entangled state so that.

771
01:22:58.590 --> 01:23:04.560
Gate operations can act on multiple 2, but once making it up, right? Faster.

772
01:23:04.560 --> 01:23:14.729
3rd, you got our quick date operations and also high dates fidelity meaning there is low error rate.

773
01:23:15.930 --> 01:23:27.479
Sense quantum computers can't operate in a 0T or 1 state. As you add more gate operations. The amount of noise builds up, which increases the error.

774
01:23:27.479 --> 01:23:38.699
And then, lastly, a high degree of scalability, which means that the content computer can achieve high fidelity and connectivity through operations.

775
01:23:40.199 --> 01:23:47.760
So so far are top logical they know that they should demonstrate a high coherence.

776
01:23:47.760 --> 01:23:51.930
And they should have high gain fidelity since the.

777
01:23:51.930 --> 01:23:57.270
Information is not encoded in the cube, but actually in the.

778
01:23:57.270 --> 01:24:02.729
Grades as Nick and Ricardo explained.

779
01:24:02.729 --> 01:24:07.529
They also are going to have a high fault tolerance, but on the other hand.

780
01:24:07.529 --> 01:24:11.340
A lot is still all theoretical since they haven't been produced.

781
01:24:11.340 --> 01:24:16.949
So, there's unknown date operation times activity and scalability.

782
01:24:18.659 --> 01:24:30.239
From the other documents that have already been produced, they're superconducting candidates, which have fast 8 times, well, long cavity, but they must be kept very cold to work.

783
01:24:30.239 --> 01:24:38.130
Um, I, on track, as I said, have high gate fidelity, but their operations are a little slower than the other ones.

784
01:24:38.130 --> 01:24:46.979
atomics have a high scalability and no temperature requirements, but that is a still a new technology that hasn't been fully tested.

785
01:24:46.979 --> 01:24:56.159
Neutral atoms have long coherence times, but those are also new technology and they must have very cold to operate.

786
01:24:56.159 --> 01:25:02.789
Silicon chips are the most stable of the cube bits, but those also must be kept cold.

787
01:25:02.789 --> 01:25:08.460
Daniel is going to talk about our top logical cubex I used today.

788
01:25:11.760 --> 01:25:16.529
Yeah, so I'm going to be talking about what's been going on today, which if we had to the next slide.

789
01:25:16.529 --> 01:25:23.250
The main bulk of the work today and top logical keep it is actually being done by Microsoft.

790
01:25:23.250 --> 01:25:37.680
They've been the driving force for several years. Um, aside from that discovery, the discovery since 2015 that Nick spoke about briefly, there's been a ton of work put in by Microsoft as well as all of their, uh, everyone they're working with.

791
01:25:37.680 --> 01:25:46.529
They actually have 8 laboratories across the world, um, in Redmond, Washington, in Indiana, California, the Netherlands, Denmark, Finland.

792
01:25:46.529 --> 01:25:51.029
As well, as more in Denmark and Copenhagen and Australia.

793
01:25:51.029 --> 01:25:57.715
So, they have a wide net of researchers around the world who are a completely multi disciplinary team.

794
01:25:58.074 --> 01:26:12.385
So they have everything from math, mathematicians, and physicists to a material, scientists, and engineers and computer scientists. So, Microsoft is taking really a holistic approach to the quantum computing problem.

795
01:26:12.385 --> 01:26:22.255
When it comes to tautological inhibits and he's trying to attack all areas of the technology at the same time they try and get a leg up on anyone else who's taking sort of that more linear approach.

796
01:26:22.859 --> 01:26:29.909
Um, from these 8 laboratories, they've produced over 50 scientific papers, a few of which I'll discuss as we continue.

797
01:26:29.909 --> 01:26:36.090
And actually, just this year, they've also entered into my backup to who's working on it.

798
01:26:37.614 --> 01:26:46.824
And just this year, they've actually started a collaboration between, um, several of the US national laboratories, Microsoft, and a multitude of universities, including pregnant Stanford.

799
01:26:47.305 --> 01:26:55.045
And the reason that they're going for this collaboration with these universities is actually specifically to do with a lot of the material science research that the national laboratories do.

800
01:26:56.095 --> 01:27:10.104
And in terms of the universities, they specifically get a lot of access to obviously a lot of very high level researchers in computer science and quantum computer science specifically. And the end goal of microsoft's work, um, in quantum computing.

801
01:27:10.104 --> 01:27:18.715
And specifically, their top logical computer is to have them available through Microsoft, which is a similar platform to Amazon quantum bracket.

802
01:27:19.350 --> 01:27:24.239
These are fierce competitors and now, if we had to the next slide, please.

803
01:27:24.239 --> 01:27:36.810
Microsoft has made a couple of really major breakthroughs in 2018 when they 1st really got loud about their quantum program, despite having been working on it for a couple of years.

804
01:27:36.810 --> 01:27:39.989
Uh, the, they had made a huge.

805
01:27:39.989 --> 01:27:50.729
Right through that they like to call a selective area growth and this is in a highly controlled laboratory setting. They were spraying Adams, um, directly onto the surfaces of Super conductors.

806
01:27:50.729 --> 01:28:02.034
And they were able to observe a quantum, pathological phase. So, while they weren't able to directly, um, manipulate these and use it as a quantum computer, like we said, that hasn't really happened at scale.

807
01:28:02.034 --> 01:28:08.305
Yet they were able to observe the phase and know that, you know, through a couple of of Pre setup.

808
01:28:08.729 --> 01:28:13.890
Um, manipulations that it was acting as expected and could be used in the future.

809
01:28:13.890 --> 01:28:18.270
But this was clicked in 2020 by.

810
01:28:18.270 --> 01:28:22.439
The nano wire in a shell paper that they released. Um.

811
01:28:22.439 --> 01:28:32.399
So, 0T modes, which are a, not a 1B quasi particle, which is being researched heavily in connection to tautological. Cuba.

812
01:28:32.399 --> 01:28:36.899
They were simply doing research on these and trying to find interesting ways to manipulate them.

813
01:28:36.899 --> 01:28:42.234
They were using semi conducting wires and when they surrounded it with a superconducting shell,

814
01:28:42.534 --> 01:28:48.744
they actually found that they didn't need to pierce it with that strong a magnetic field as they had with all of the other,

815
01:28:49.194 --> 01:28:49.765
uh,

816
01:28:50.034 --> 01:28:53.154
processes they had used to achieve the top logical face state.

817
01:28:53.609 --> 01:28:58.140
So after continued experimentation.

818
01:28:58.140 --> 01:29:10.590
They resulted in a major reduction in the number of constraints on the system. So they actually have a really wide tolerance for chemical potentials of all of the materials involved and greatly reduced magnetic strengths requirements.

819
01:29:10.590 --> 01:29:17.939
Which is always a major plus when tackling an engineering, uh, endeavor to have reduced number of constraints in the system.

820
01:29:17.939 --> 01:29:27.359
Because it means that it widens your ability to have different manufacturing processes to create these systems in, which you can get to the top logical face date.

821
01:29:27.359 --> 01:29:39.810
And so, by having a wider array of constraints there, it'll allow for better manipulation of the particles in the Nana wire shell. So, this is a major breakthrough very recently. And more research is obviously still being done.

822
01:29:39.810 --> 01:29:44.189
And despite this, being, sort of considered larger than the select of area growth.

823
01:29:44.189 --> 01:29:47.670
Style they are continuing to pursue all ends.

824
01:29:47.670 --> 01:29:50.670
And if we go to the next slide here.

825
01:29:50.670 --> 01:29:57.510
They're actually doing a ton of parallel work, so I just chose to highlight a couple, but I'll talk about more than this.

826
01:29:57.510 --> 01:30:05.609
Very recently they've been looking into ground subspaces for top article phases because in the last couple of decades.

827
01:30:05.609 --> 01:30:20.364
1 of the main things that makes logical face dates great is the multiple grounds of spaces is what allows for the error correction, which is sort of what we talked about with the rotation in different directions, not resulting in the same state. But rather states that you can distinguish.

828
01:30:20.670 --> 01:30:33.720
And so a lot of research had been done on these models, but few had touched the, not a 1B models. Few of them had actually worked with, like, not a 1B States because they are so difficult to use.

829
01:30:33.720 --> 01:30:42.180
And because of this multi disciplinary team, they have, they were able to use a lot of the, the power of their mathematicians and their sort of pure science people to.

830
01:30:42.180 --> 01:30:50.310
Create these proofs to prove that ground subspaces do exist for not a 1B models of and ions. So.

831
01:30:50.310 --> 01:30:57.689
This is a really large, not necessarily breakthrough, but reinforcement of the science that they're doing, and the engineering that they'll be doing in the future.

832
01:30:57.689 --> 01:31:05.399
Additionally, sort of parallel to the work that's being done with the mantle liars and the chemical, the spray.

833
01:31:05.399 --> 01:31:08.430
Um, they're also working with quantum dots.

834
01:31:08.430 --> 01:31:13.920
The quantum dots are a promising field, the research, or it's sort of like toy, quantum computers.

835
01:31:13.920 --> 01:31:19.619
That have very stable 1 and 2 cubic Gates and they are tautological.

836
01:31:20.064 --> 01:31:34.135
And this work will allow them to possibly, like, observe things about these top logical states that they wouldn't otherwise be able to implement in the scale technology that they're trying to develop with things like.

837
01:31:35.220 --> 01:31:43.829
So work like, this is absolutely vital to be able to continue to develop the computer science and theoretical.

838
01:31:43.829 --> 01:31:50.819
Part of their research without necessarily having the engineering worked out yet to create these systems that they have.

839
01:31:50.819 --> 01:31:54.119
Uh, conceptualized in a lot of scientific papers.

840
01:31:54.119 --> 01:31:59.399
Uh, Microsoft continues to also work on a lot of, um.

841
01:31:59.399 --> 01:32:09.418
Algorithms based approaches to quantum computing and a variety of different problems that they continue to work on with universities along with internal work.

842
01:32:09.418 --> 01:32:14.609
So, Microsoft really is like the, the forefront leader here of pathological.

843
01:32:14.934 --> 01:32:25.644
Computing and they've been doing an absolute ton and you can actually find out more about that. If you're interested on microsoft's website, they have a list of every scientific paper that all of their laboratories have released completely publicly.

844
01:32:26.154 --> 01:32:30.594
So, I highly recommend you check there if you would like, more information.

845
01:32:30.929 --> 01:32:34.738
So, thank you very much. Is there any questions.

846
01:32:34.738 --> 01:32:38.219
And we also have our references here.

847
01:32:55.288 --> 01:32:55.679
Uh,

848
01:32:55.703 --> 01:32:56.873
I can't hear you if you're speaking

849
01:33:23.123 --> 01:33:26.694
so there's no telling a specific timeline for when it might get useful,

850
01:33:26.694 --> 01:33:30.804
but we're definitely at least a couple years out from anything commercial scale.

851
01:33:31.229 --> 01:33:40.559
So, as exciting as all of these sort of breakthroughs are, um, there's a lot of work that needs to be done specifically in the material sciences before this really gets to.

852
01:33:40.559 --> 01:33:45.838
To a point where it can solve the world's problems.

853
01:33:45.838 --> 01:33:49.288
Okay.