Hey everyone and welcome back to another video. In this tutorial, we're going to be looking at why you should be using the relationship and the data model in Excel also available to us when working with your tables and predominantly when you're trying to bring data from multiple tables into one pivot table. I haven't got an example to show you the problem I'm trying to solve, but in essence what it is is when we've got multiple sheets of information or tables or whatever format they're in and you're wanting to consolidate them into one single piece of information so that you're able to then provide a pivot table to obviously go through and analyze that information. As you're aware that the situations or the solutions to try and to solve that problem you would be trying to pull information all into one sheet maybe by using VLOOKUP or any other formula so that you had like one store of all the data so that you could pivot off that one central piece or source of data. What we're going to do in this is show you that actually you can retain keeping that information separate in separate tables and how you can then obviously use the relationship manager to obviously manage those relationships and identify those relationships between the table and then be able to treat them as one whole data set. So in order to do so we've got some information here so we've got a sales table what gives us simply just the country code the date and obviously the amount of the sales and it's probably obvious but we obviously the country code relates to a country in the country table that we'll get to in a second. So for each country we've had to see every month and I believe we went from July 2019 to June of 2020 so there's 12 months of data you'll see that there is again a month for each of those and an amount of sales achieved in that month. Similarly for budget we've got a similar very similar data set it's got the country code again the month or date and then you've got the amount what was budgeted for that country in that given month. And then lastly we've got the country table what gives us the country code and then obviously the country that aligns to that country code. So these are the three tables that we want to bring together and be able to just pivot into one single pivot table to allow obviously us or if in real world experience the end user to be able to filter through this data and the benefit obviously keeping it separate is as these two data tables or multiple data tables continue to grow or new information is added to them everything's going to flow through and refresh rather than having to obviously update any formulas and make sure things are pulling into new rows so on and so forth all that usual issues that we want to try and avoid. The one additional thing that we have to add to this and I was just going to add it now rather than prior to the video is in order for this to work we also need one other additional table we're going to need a date table and that table it simply just consists of this date table a distinct list of all just the dates that we are using here and it'll become more valuable or not valuable sorry it'll become more obvious why we need it as we move on but if you're used to working with databases especially relationship databases you'll understand why we need to have this separate date table for doing this. So what I'm going to do is just create another sheet and we'll call this one date and all I'm going to do is go and copy this column and just paste in column A so I've got the date there we'll put it into date format and all I'm going to do is go to date remove duplicates okay and yeah so it was July to June of 2019 or 2020 so you can see that's all the dates we've got available and just as a recap for anyone sit to convert that into a table all you do is click that table button yet my table does have headers because what the date header there click okay and then that's now converted into a table for us and lastly in the table design I'm just going to change this and call this date table just so we know what that one is so we've got the full piece of information now we've got the date table so we can obviously group everything by date what the sales were per country and dates what the budgeted amounts were per country and date and lastly obviously the mapping table for country to the country code so the first thing that we need to do is identify the relationships between each one of these tables so all we need to do for that is we go into our data tab and you'll see over the far right hand side or just off the middle we've got this relationships button here so it's going to sit here we're going to click our relationships and then you can see at the moment it starts as blank so we just need to go in and define those relationships so the first one we need to do is obviously we need to make sure there's a relationship between country and obviously the two budget and sales so we're going to go into here we'll go to table and we can find the country table and the column that we want to use to map is going to be the country code because country code is the unique reference between country and budget and sales so country country code related table and we can go into here and we'll get budget and we want to use the country code so we're going to do okay and then we're going to do another one so this time we're going to go between the country country code and this time we can do the sales so it's going to be the country code there and you can see as you build along what it looks like so you can see what our table is so we've got the budget country code and it's related to the country code within the country table and so forth with sales as well next one we need is we've got the date table so we now need to go into here and we need to go our date table and it's going to be just a date because anyone feel the name and then we can go into our budget table and select date and then we can go into our date table again and this time we want sales and you can see Excel has automatically recognised that it's obviously done a search and it realised that this is based on the naming it's obvious that we're going to probably want that date column so you'll notice that Excel will make a suggestion based on what it feels would be the actual column and you can see it's populated there in that example cool so we're going to enter there so let's just recap on what we've got here so we've got the budget is matched to the country budget is also matched to the data table our sales table is mapped to the country and our sales table is also mapped to the data table so I believe that is everything we have so the date against those and the country against those so I think we're all ready to go so once you're happy with your relationships all you now need to do is close out of here and now we're going to scope and proceed without inserting a pivot table so just to make this presentation purposes I'm going to go onto this essential Excel sheet here and I'm just going to go into insert a pivot table and as you're probably familiar with using you would even normally go into this top option here so you can say you're just going to select a table or range of data what we're actually going to do is go use an external data source choose connection if you then go into tables you can see this first option at the top here this workbook data model and it can see it's recognized that data model what we've built with those four tables if you haven't done the relationships obviously you wouldn't see this top part here you would just see these available tables here so once we've selected that we can go into open and where do we want this? it's obviously selected that we're going to go into C7 what's our last active cell in the sheet you can go OK and you can see it's just now thinking about it and yet there we go we've got a pivot table published for us so when we now look over the right hand side here we can see we've got our four separate tables and within each of those tables you can see the fields available to us so straight away we can see that it's worked everything is consolidated into this table and we can now proceed with populating up here at table as we require so the first thing we want is to take from our budget so I'm just going to take the amount from here and then we can go down here and we can pick country so we want the country from the country table as well that's the only place it's actually available and then the last one we can go into is sales and then we can go down to sales amount and actually we don't want country on there there's quite a lot of country so we don't actually need that there so we're just going to take country out and what I'm going to do is go into the data table to pull in our date so just to recap here I know we can see there's a date column in the budget and obviously also in the sales table but we need to ensure that we're pulling date from the date table because this is our central point for date and then we're going to use this as a starting point and then the other fields will map against this so to avoid any problems we just need to make sure we get date from our date table and we can see that everything has now mapped into here for us one thing we just need to do actually just before we get confused so the first one I pulled in here was budget so I'm just going to go field settings and we're going to go into put budget and I'm going to just change the number format to a number okay okay and then the second one was then our sales let me change that to a number as well okay so there we are we've now got a pivot table that has summarized our information and we can see the benefit of this is the tables are still separate so they're separate entities that we can continue to update with more information but they're ultimately going to get pulled through to this single table here the other benefit we have with this as well is that we can also insert a slicer so let's go insert and where it's gone over here slicer and let's go to the country tab so this is where I jumped ahead really by pulling country into the pivot table initially go to the country sheet and we're going to select country and you can see we now got this slicer available to us and let's go down to somewhere like the United Kingdom if it is even there no it's not there I don't know where it is let's pick another random country I don't know what the number is going to show let's go United States of America so there we can now see the benefit of being this slicer in is we're able to now filter the table and see the different amounts based on each country and you go to UAE as well Uruguay any other one available to us and it's now got to all centrally stored into that pivot table so this right here is basically the main reason of why you start using relationships when working with data especially in multiple tables or multiple pieces of information that you're working with one other bit we can do just to go a bit further with this okay so just to improve this and go one step further we can actually add a measure what will allow us to work better with this budget and sales amount all we need to do is we're going to go into our pivot table and the first one is budget so I'm just going to right click on to budget and you can see I'm just done that on the right hand side here hopefully you can see that and I'm just going to go to add measure so you can see it's in the budget sheet so what's my measure name going to be well actually I'm going to call this the budget and the formula for this is going to be the sum and it's going to be where has it gone budget amount close that okay and you can now see we've actually got this measure in here what's going to be the budget amount and all it is is just the sum of the amount and this works really well just because obviously it defines it as the actual value what we're looking for but also it's going to work when we come to want to do other measures like changing the variance maybe the difference between the budget and the sales amount so I'm just also going to come down to sales and do the same down here so I've got sales measure name is sales and the formula for this is going to be equal sum and we want sum of sales amount close our brackets there go enter and we can see we've got that value and now available to us here as well so let's just actually just go into edit measure let's just make sure it is a number so we want to enter number format and also if we go into budget we can go edit measure into number so you can do this when we first set it up I just forgot to do it now I'm just going back and doing it now and then if we remove these two that we had here what we can do is now just drag in budget and also bring drag in the sales you can see how it works the same but we can now identify these actually as what they are the budget and the sales the one last thing that I'm going to add into this is another key piece of information could be to see how does our actual sales amount fair against the amount we're budgeted for so what I'm going to do here is just go into our sales table and do add a new measure and this time we're going to call this their percentage and basically all that stands for is the variance and now this is going to come into our formula here so this time I'm going to do is divide open brackets so our numerator we're going to do a calculation here because we want to find initially what the difference is so for us because we've now got those so because we've now got those measures available to us all we need to do is reference those so if I start typing sales you can see we have this sales measure here so double click that so we want to do that minus the budget and you can see when I start typing that you can see obviously we've got the budget budget budget and the budget some of them out so these are all everything we're able to budget but because we've got that measure we're just going to use that one there just makes it a lot tidier on the page as well so the next thing we're going to do once we've done that is put in the denominator and the denominator for us is going to be that budget amount again and then alternate result so if there is an error and for whatever reason we just want to show blank so for us we're going to just go into one more I'll just click that a bit so just do comma and I'm just going to put in here blank like that like so and then that is the entirety of our formula so what it's going to do it's going to take our sales amount and the minus of our budget so it will tell us obviously what our amount increased or decreased is it will then take that increased decrease amount divide against the budget to then give us a percentage so what the variance percentage is and lastly all we need to do for that is we'll just change this to our number we'll put it to percentage one decimal place and select ok and we've now got that percentage here so let's just drag that into our table as well and we can now see what our percentage variance is so for that first one you can see we've got the 2196 and then the 2204 and obviously that's been a percentage increase of 0.4% and actually looking down that page there there's not been really a great deal of change happened at all and I don't know what's happened to my slicer so I'm just going to enter one more time so we go to slicer and we want the country here and obviously the benefit of having done those measures and also the additional variance percentage measure as well is when we go to set any one of our countries we can see that we're going to get that information automatically populated for us as well so this is a really good technique to be using not only does it simplify working with multiple tables of data it also just gives you that streamlined way of working with it allows continuous update to the data as well and also gives this professional presentation ability when it comes to like going through with a colleague into this information back in live time so I hope you enjoyed that video if you did please do give it a like it's not only greatly appreciated by myself but helps that all important YouTube algorithm if it's the first time finding the 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Thank you. Good afternoon. This was a talk I didn't really expect to give because I thought it would be rejected since it doesn't really have any python in it. Very much python. But I put it in to encourage me to learn more about content computing. And as I learned a bit more, I decided to change the subtitle to a gentle-ish glance into a likely future. And then as I actually came to preparing the slides, I decided all I could really possibly aim to do is to explain why everything you've ever read about content computing is probably wrong unless you've read the real stuff. So I'm going to try and give you one or two really important corrections to a lot of what I think is misinformation. It's great to see so many people here. I'm assuming some of you are here in Edinburgh for Europe Python. Maybe quite a lot of you. That's not actually why I'm here. I live here. I came to Edinburgh in 1986 because I was invited by Peter Higgs, humble brag, the guy they built the LHC for to discover the Large Hadron Collider, and had the privilege of being taught quantum field theory by him. And I say that not to imply that everything I get wrong is his fault at all because he's a genius, but just to say I have spent a lot of time thinking about this. And what you're going to find during this talk is at various points, I'm going to say things that are completely ridiculous and obviously wrong. And you're going to think these things are completely impossible and you're going to be reminded of Alice in Wonderland or rather through the looking glass where she said, there's no use trying. One can't believe impossible things. I am going to ask you to believe things that will appear to be impossible. It's entirely up to you whether you do, but it's something that everyone who tries to come to terms with quantum physics has to do, and there's no way around it, I'm afraid. If you have read or learnt anything about quantum computing, probably the most likely thing you've heard is that if it ever really works, cryptography is in trouble and SSL, the security of the internet, is in trouble. And there's quite a lot of truth to that. Not complete truth. Not all cryptography is in trouble, but certainly cryptography based on the difficulty of factorising big semi-prime numbers is in trouble, which is what SSL is based on and RSA, public key cryptography. So, the basic idea with public key cryptography is that it's easy to multiply two numbers together, but it's very hard to figure out what the two prime factors are if you have a semi-prime number, one that has exactly two prime factors. But we need to be rather precise. When I say it's hard, that's actually not true at all. It's incredibly easy. In fact, it's slow. We can write just five lines of Python that will factorise any semi-prime number. This is only really using two things. It's using the fact that we know that if we have two factors in number, one of them can't be bigger than the square root of the number because obviously if we multiply two things that are bigger than the square root of the number, we're going to get something bigger than the number. The other thing about this incredibly simplistic approach to it is it uses one difficult or one slow operation, which is this percent operator here, which is integer division, right, integer modular division, and division is slow. So, if we didn't care about how long each individual step takes, there might actually be a more interesting way to do it, which would be dramatically less efficient, but would parallelise well. So we might just write a function that, given our semi-prime number n, our big number n, and two possible factors just returns true if it's truth to two factors, multiply together to give it, and false otherwise, and then we could just do a double loop, one over the small numbers and one over the big numbers, one's bigger than the square root, and one of those, if it is indeed semi-prime, would come back with our result. And if we had a parallel computer, if we had a really big parallel computer, like one with at least n squared processors, which is probably really quite big if n's big, then things would be quite nice, because we could just, you know, on one processor try, well, you know, does 15, I know 15's not a very big number, but you know, does 15 equals two times three times, does 15 equals two times four and so on, and one of these would find our answer. So this would be a nice way if we had a sufficiently large parallel computer, I'm sorry, I'm moving away from the mic, if we had a sufficiently large parallel computer to solve the problem. But there is a detail missing, and it's quite an important detail, which is that it's no good just finding the answer on processor seven, especially if we've actually got, say, 100 quadrillion processors, because we're probably not connected to all of them. So what we have to do at the end of the operation is we have to collect our answer, we have to arrange for the answer somehow to filter back to the actual processor we're talking to, the host processor, if you like. And that's not difficult at all, but we have to do that in operation and reduce them in pairs and get the answer back. But it is something we have to remember, and that will become important in about 15 minutes. So hold this thought, we're going to do some physics in a minute. You probably all know what bits are, I'm sure you all know what bits are, bits of boxes that contain zeros or ones, right? And if it's a bit in random access memory, then you can set that value to either one or zero, and you can read that value back and find out whether it's one and zero, and if nothing goes wrong, the same thing that you set it to is what you'll get back when you read it back any time later. And so the crucial thing, of course, is that the same bit in random access memory, in memory we can change, can have different values at different times. Hopefully that's not news to anyone. So what about quantum computing? Well, quantum computing is based on quantum bits. And quantum bits are boxes you can put zeros and ones in as well. It's just the slightly funny shaped boxes that Paul Dirac, great British physicist, designed and called KETS. Now these actually have a very specific meaning, but for our purposes, just think of it as a box that contains a zero or a one. Or possibly both. Not like half, but like maybe it's partly a zero and it's partly a one at the same time. And we would write that, in fact, there's one over root two of the KETS zero plus one over root two of number one. Why one over root two, not half? Well, it turns out we square these numbers. In fact, we take the, if they're complex numbers which they tend to be, we take the complex conjugate and multiply that by the thing to get our probability. But it doesn't matter, it's just the way we write it. But I want to go back to this important point that a single qubit, a single quantum bit can be simultaneously partly one and partly zero. And that's so important, I'm going to shout it as well. A single qubit can be both one and zero at the same time in some sense. And you're going to say, well that makes no sense, this is impossible. And I'm going to say, I completely agree that makes no sense it's impossible. It just turns out that's how the universe works. And what I'm going to try and do is tell you why physicists, even physicists who've thought about this an awful long time, whom it still makes absolutely no sense, believe this. And we're going to take our cue from Feynman, one of the greatest quantum physicists ever, brilliant teacher, who introduces volume three of his lecture series with three experiments. The first of which is an experiment with waves. So think about water, or think about light if you prefer, but I'm thinking specifically about water here, where we've got something that's bobbing up and down on the water creating waves, or maybe it's a set of drips falling on the water creating waves. And we've got a barrier in front of it that's got a couple of holes in it, in a wall, and then we've got an absorber at the back. And what we know about waves is that they exhibit interference. That is, if we have multiple drops falling on a puddle, then the waves around the droplets spread out, but they just go through each other, right? They don't stop, they just go through each other. And at the point where they meet, they exhibit interference.  And where a peak meets a trough, they cancel out and we get destructive interference. No great surprises there. And exactly the same thing happens with light. If we have this same double-slit experiment and make the slits really close together, then what we'll find is right behind the slits, we'll get this really bright point because the paths are the same length. But if we move a bit to the side, then one of the paths will be half a wavelength longer than the other path. So we'll get negative, we'll get destructive interference and they'll cancel out.  And when the difference in the path length is a whole wavelength again, we'll get back to positive interference and so forth. So now we'll do an experiment with bullets. And it's exactly the same setup, just slightly larger. We've got a rattling machine gun.   Bullets do not exhibit interference. They might occasionally bounce off each other and all sorts of crazy stuff will happen. But if when one hole is open, this is the distribution that we see of bullets from this rattling machine gun and this is the other, then we just add up those two distributions and that's where it will be. And if the slits are close, if the holes are close together, then actually the most likely place to find a bullet will be in the middle. Bullets do not exhibit interference. We know this, right? So now let's do it with electrons. Now you know what electrons are. Electrons are like tiny little bullets. They're the things that hover around in a sort of cloud around the nucleus of atoms. They're incredibly small. In fact they probably are not certain, but they're kind of point-like. But we can do things with them. We can have electron guns that are just like, well actually they're not very like machine guns, but we have electron guns that fire them out over some angle, and again if we have slits that are close together, we can do the same experiment and the completely unbelievable thing is that electrons do exhibit interference exactly the same way all the other way phenomena we know about it. And you think, well that doesn't really seem to make any sense. But maybe what's going on is maybe electrons are going through both slits and they're kind of bouncing off each other, just like we can imagine bullets hitting into each other and somehow they're causing this interference pattern. Not quite clear how that would happen, but maybe that's happening. But that's not what's happening. We know that it's not what's happening because if you reduce the power of the electron gun so that there's only a single electron in the system at any time, you still get the interference pattern. Which I know makes no sense, but there's a lot of not making any sense in this talk. The electrons act as if they were being guided by waves and that the wave went through both slits and the wave actually caused interference and the position of the electron that we measure is only the places where there's positive interference and where it cancels out we never find the electron. And in fact that's exactly how quantum mechanics describes the world. So Schrodinger's equation and all the more modern versions like Dirac's equation describe the path of electrons, the evolution of electrons and all other particles in the form of a wave function, a complex wave function, whose amplitude at any point is a complex number and if you take the size of that if you square that amplitude by taking the complex conjugate at times itself, that gives you the probability of finding the particle at that position in space time. So the wave function can be in what we call a superposition of states. That is a mixture of states. It can be partly in order. It doesn't have to be a 50-50 mixture. It can be any mixture we like. These coefficients can even be complex. Schrodinger's equation tells us all the more modern versions tell us how psi evolves over time. But whenever we actually measure where the particle is, we always find it either went through the left slit or the right slit. If we measure the cube bit we always get a 1 or a 0 out, not a mixture. But in between when we're not looking at it it's as if it's in both states partly. We talk about the wave function collapsing or at least we used to talk about the wave function collapsing. Now, you're probably going to say that still makes no sense, and again I can't really argue with you. But what I don't want you to think is that means there's something bad about quantum mechanics because quantum mechanics is literally the best scientific theory we have and the best scientific theory we have ever had. It's one of the two great pillars of physics. There's relativity and they're quantum mechanics. There's this slight embarrassment that they're incompatible with each other, but don't worry about that. Relativity is really good at the big scale. Quantum mechanics is really big at the small scale. And in fact there has never been a reproducible experiment that disagreed with the results of quantum mechanics. Sometimes quantum mechanics gives us probabilistic answers. Sometimes it gives us definite answers. But we've actually verified those answers more accurately than we've ever verified anything else in the world. Like to almost a part in a billion. It is our best theory. It works. If you use the equations unless you make mistakes you always get the right answer. The only difficulty is understanding what an earth fiance means except in terms of the probability of where particles are. Now, in 1957 a guy called whoever it was working for as a student for John Archibald Wheeler and he came up with a particular interpretation that is favoured by almost everyone in quantum computing, probably largely because one of the fathers of quantum computing David Deutsch very strongly advocates this way and it gives you a very nice way of thinking about things as long as you don't mind your head hurting. Whatever it suggested is that every time there are multiple possibilities suggested by quantum theory what happens is the universe literally splits and all the different possibilities are realised in different universes. It's sliding doors if you've seen that film. Both things happen. Or in fact perhaps not even just both things but if there are lots of possibilities maybe as many as a continuous infinity of different things happen in different universes. So there are loads of all of you sitting here in different universes just with very slightly different quantum states. And what the many worlds interpretation says is that when we measure the outcome of an experiment when we measure Psi what we're actually doing is we're finding out the value of Psi in our universe the particular universe we're in at that point. But the really interesting thing is that the different universes interfere with each other. The wave functions from different universes interfere with each other, you get constructive and destructive interference and they influence the probability of you finding yourself in any particular universe. If it's in a superposition of states with alpha and beta then the probability of finding ourselves in a universe where the answer is 0 will be proportional to essentially alpha squared and the other possibility will be beta squared. And again you say this doesn't make any sense I know I'm getting a bit repetitive here but this seems to work. Now let's go back to stuff we all understand. Bites and words and things like that. So obviously we don't only do bits in computing we do bites as well. Bites are 8 bits, we look at longer words like 64 bit words and so forth because it's pretty difficult to do anything interesting with a single bit. We can do something similar with qubits. We can form cubites if you like or keywords if you like quantum bytes and quantum words using something called entanglement. An entanglement is a phenomenon whereby multiple qubits which are discrete physical things but multiple of them are described by a single wave function and that means that we say that they're entangled that is to say if we worked with 8 qubits then obviously that could represent up to 256 different values 0 up to 255 say then we can actually have a wave function that describes the state of this cubite if you like as being partly 0, partly 1 all the way up to partly 255 and we can hold that thing for a while and do computations on it which is weird. So probably you're getting where I'm going here, right? Why was I talking about all this parallel computing stuff?  Presumably what I'm saying is that quantum computing is like this free parallel computing where we just run our computer run our computation across loads of different universes and one of them finds the answer and we're done. That's how I'm glad there are people wincing. That's how almost every pop article I've ever read about quantum computing makes it sound like quantum computing works. And there's a little bit of truth in it. We can set the computation up like this and we can set up the superposition of the state of the input state of the quantum computer so that every universe would do a different version of the computation. So it's kind of right but there's a but and quite a big but. And the but is that it's not just the quantum computer and the qubits that spread across the universes it's us as well. And so if we do set up the computation this way the trouble is we don't get to choose which universe we lock up the answer in. So almost certainly in this case we're going to find we tried three times four and it didn't equal 15 and the game's a bogey. So if you take nothing else out of this talk even if you think all this bullshit all this stuff I've said about quantum computing can't possibly be right quantum mechanics stuff this guy clearly doesn't know what he's talking about what you should remember is this is not how quantum computers work. It's not free parallel computing across universes it's a lot more complicated than that. Thank you very much. Ah but I'm not done because I'm going to try and tell you how it does work kind of sorter which is a bit harder it's quite a lot harder actually. So to benefit from quantum computing what we need to do is we need to find algorithms in which all the bad bits of the computation all the dead ends all the bits we don't want exhibit destructive interference if we think of this multiple universe model which I think is nonsense by the way it's not the interpretation I favour I like one that goes backwards in time instead it's much better. We have to arrange things so that all the bad solutions all the things we're not interested in destructively interfere and kill each other and the solution the thing we're actually looking for exhibits constructive interference ideally so that we get with probability one the answer that we're actually looking for and if we do that then it doesn't matter which universe we're in because all the universe is produced the same answer. We're looking for global properties of the computation of the system that we're doing and if you've heard of Shaw's algorithm which is the main way that this semi-prime factorisation problem is tackled that's what Shaw's algorithm is that distributes the work across a superposition of states such that the result is a global property of the result of the computation and Shaw's so quantum cubes have been built not with all that many qubits but they have been built and they do work and they have been tested and we have used Shaw's algorithm to factorise large-ish semi-prime numbers depending on your definition of large-ish biggest we've done so far is 21 but you know it's a start it's not like the smallest semi-prime number there's another way in fact of doing quantum computing called adiabatic quantum computing and we've got up as high as 143 with that so that's even more impressive and it turned out when they went back and analysed what had happened it had also factorised 56,153 just sort of as a little bonus which they hadn't asked for quantum computing is a little bit weird these aren't incredibly impressive results numerically because if I run my stupid little program it factorises 56,153 in about a 20th of a second almost all of which is python start-up time and even my mind crashingly stupid implementation does it in about a second and that's running on a single core if you gave me n squared cores where n is like a really big number I'd probably be slower anyway so the summary before I am going to try and describe how Shaw's algorithm works but I want to do the summary first that should be easy enough to explain the whole of the rest of quantum physics the physics behind quantum computing is 100% sound as the president of the US might say it's the best science we have it really is literally the best science we have it works the engineering is tricky to build quantum computers we typically have to operate at a few millikelvin that is a few thousandths of a degree above absolute zero that is that we can't keep them in order for quantum computation to work the system has to remain coherent what does that mean it means that other stuff in the universe mustn't interfere with its quantum state too much and other stuff in this universe and all the other universes does have a bit of a habit after not very much time of causing the kind of interference we don't want that turns the whole system to a state that we can't do anything with so it's very hard to keep the system's coherent but we're getting better at this stuff there doesn't seem to be anything in physics that says we couldn't build a big one we know that in principle the physics works so everything's good so it's probably coming now I'm going to try and explain Shaw's algorithm so the way Shaw's algorithm works is it doesn't tackle factorisation of large semi-prime numbers head on it tackles it kind of sideways it uses a number theoretic property which is if we take almost any number and we compute x modulo the number that we're trying to factorise modulo n that is you know the percent operator in python, integer remained a division in python so if we compute x modulo n and then x squared modulo n and then x cube modulo n we keep on doing that up to a really really really big power of x and it helps obviously that python is quite happy with arbitrarily large computations but it also helps that we're doing it modulo n so it doesn't actually get out of control it will turn out in almost all circumstances to be periodic that is this remainder will repeat it might have a very long period but it will repeat over some period that might be close to n the number that we're trying to factorise but it turns out if we can find that period of this sequence for x and we can find it for maybe half a dozen different x's that's enough to allow us to figure out what the prime factors of n are I can't explain exactly why that is you just have to take it number theorist that's good and in fact Euler worked this out in the 18th century so if we can find the period of such sequences for a few values of x we can find our two prime factors of n so how do we do that well what we do and this is another really crucial point we don't allocate different possible factors across our if we think of this multi-universe thing we don't create a superposition over the possible factors we create a superposition over the elements of this series that we're trying to compute so we put x on one x squared on the next one x cubed on the next one and we compute on each one what the remainder when we divide it by n is and then we use a quantum Fourier transform to find the period you probably all use quantum Fourier transforms they're exactly like Fourier transforms except they're quantum because you know if you know nothing else about a Fourier transform you know that it finds frequencies and you know that frequencies are the inverse of periods so if we could calculate a quantum Fourier transform then we'd be done and that's what we're doing the crucial thing is that the period that comes out is a property of the whole computation that we can look up in any universe five minutes so I'm going to now try to illustrate this using an idea by Scott Aaronson but he didn't write the Python that wrote the JavaScript that does the demonstration so I'm going to claim some credit I'm not on a 24-hour cycle I'm on some other cycle and we don't know what it is but I'm very regular and I get up at the same time each day the same time on whatever cycle I'm on and what we've got here is we've got a set of clocks one of which goes round once every 21 hours one of which goes round every 22 hours one of which goes round every 23 hours all the way up to 30 hours and underneath each clock and underneath each clock we've got a pin board with a drawing pin that's stuck in the middle at the start and what happens is every time I get up I go to each of the clocks and I move the pin under each clock in the direction that it's hands are pointing it's only got one hand it's our hand is pointing and so what happens for most of the clocks is that they do this sort of little random walk and it never really gets very far because the period of the clock doesn't match up with my circadian cycle the direction the arrow is pointing is the arrow that the hand is pointing is different at different times on different days on different my whatever our days but there's something special about the one that does have the same period as me and things that have multiples of that period and so on which is that every time I get up on my clock the one that actually matches my cycle then the hand is going to be pointing in the same direction and therefore I'm going to move the drawing pin in the same direction and so if we watch this what we should find happening it's JavaScript but it was JavaScript that was generated by python so I think it's kosher what we should find is that most of these are wandering around in a fairly hap hazard way but one of them possibly the one for 26 hours is trying to make a dash I'm not going to let it get out of the circle don't worry it's not going to run right across the 27 or anything but this is the one we can see quite clearly is in fact the cycle and in an incredibly hand-wavy way that's how a quantum Fourier transform works it arranges the computation in such a way that all the bad periods disappear because we get things that cancel out in this quantum sense but the good period keeps reinforcing and therefore whatever universe we're in if you think of the multi-universe thing or in fact you can forget thinking about multi multiple universes now the result of our computation is a well-defined result so we find our period and then we reverse engineer that we do that with a few different numbers we find our P and Q we factorise it, we've broken the internet and everything's good so that's pretty much what I had to say so you're welcome to email me about this stuff or stuff I actually know about you can find me on Twitter and I pop up in lots of universes thank you very much we've got time for one question if anyone has one you do have to come up to the front the microphones don't walk this time thank God it's not Peter Higgs one question given the fact that these famous algorithms usually require maximum fault tolerance do you think there is any use of quantum computing before we achieve systems that require fault tolerance to implement the quantum algorithm? no so if people didn't know I think the question was basically are quantum computers with short lived small numbers of quantum bits useful for anything practical no I don't think they are they only become useful when we actually get long enough coherence times and large enough numbers of cupids to do meaningful things I could be wrong about this but I'm not aware of any algorithms that do anything interesting with very small amounts of time with very small numbers of cupids


Mr. Speaker, I called you before I realized that rescheduled reception for the president of Ireland. I was going to be away on the 29th, and I wanted you to stand in for me, but they tell me that they're going to have it tomorrow instead of the next day. Well, your reception, as I understand it, Mr. President, to the president of Ireland is right after the joint meeting tomorrow. Yes. That correct, sir? That's right. That's my understanding. Yeah, well, they had originally added to the next day, and I was going to ask you to stand in for me, because I'm going to be away. Yes, thanks. But since they've got it tomorrow, I'll be there tomorrow. Fine. Well, that's my understanding. I was going to give them a lunch, and then we canceled out the lunch, and I was going to give Mr. Devalera so that you could then step in. Fine. Well, I'll see you there then tomorrow. That's that. Yeah, the invitation, as you see. Where is it, Jack? It's Mayflower Hotel. Yeah, the 130. Let me see. I've got it. 130 Mayflower. In other words, this is McCormick and I. I don't know. This is a reception he's giving, you see. Is it Stagger? Oh, yeah. Yeah, I think that Johnson's going to be there, so I assume that he's inviting everybody. Oh, yeah. Speak to Mr. McCormick. I got a telegram. Okay. That's one. I'll see you tomorrow. Thank you, Mr. President.


Just calling people out. Haha. Haha. Woo. Oh, he's fading. The woman's fading. Oh. Oh. Oh. No two yet. I'm not giving up two. Two. Haha. Clap it up. Oh, wow. Oh, wow. Oh, wow. Haha. Tag team. Smith tagged the man. High school kid. Coach Smith just started out. He's fired up. He's a good guy. He's a good guy. He's fired up. He's a nut. You want a wrestle? I'll do it. Disabusing random campers. Calling our random kids and beating them up. How many more has he got? This is third match. Oh, he's passing it off. He's passing it off. He needs a beating. No. Good tag teaming it up. He's not going to pick a good guy. He's not going to pick a good guy. Pick a good guy. He had a close call a minute ago. And I got to his legs. Is he calling him out? He's just pairing up random kids now. I love it. Oh, that was great. That was nice.


Backroads is made possible by the Minnesota Arts and Cultural Heritage Fund with money by the vote of the people November 4th, 2008. Name Kitchee Boogie. I live out in Pennington, Minnesota, and Kitchee Lake is right a mile away from me, and Kitchee Creek is right behind my house, and that's kind of how it came up with the name. Kitchee in Ojibwe means big, and so we're a big boogie band, Kitchee Boogie.  Me and Isaiah were roommates back in college, and we had a band back then called Anomaly that we started, and that went on for years here in the area, and I met Steve through Mutual Friends a long time ago, and me and Steve started jamming together at jam sessions and stuff, and then we eventually, after playing with different people, we formed this group that we've been doing now for, I don't know, probably like six years, and Isaiah joined us as a drummer two years ago. Most of the time we've been a four piece with different guys coming and going, and this summer we've been a three piece, and the songs are kind of set up that you can play with three people or with ten people if we can find a horn section, you know? We also started out playing covers, a lot of covers, but Shane has a lot of music that he had sketches of music and stuff that he had written and some finished pieces, and so we just kind of decided to not do many covers anymore, and so we started writing our own songs, Shane brought his stuff in, and with the band we got together, and we took pieces from other songs and stuff like that, so that's how our kitschy boogie became because we wanted to do our own music. Some of the songs come out of like jam sessions and stuff like that, oh that sounds cool, we recorded it, and then I will show up the next week saying, okay this is what we did and this is some words I have or something like that, and you know we've tried to encourage everybody to help out and write songs and stuff like that, we have so much material that we just can't wait to get it all down. Writing songs and being creative is what we like to do, so I bring in kind of an outline of an idea a lot of the time, not always, sometimes it's other members of course, and then these guys help me fill in because it's a group effort. You guys kind of have a blend of all different kinds of genres, how would you guys describe the kitschy boogie sound? We like a variety of music and we bring that to the table, I mean a lot of it is just could be called straight up rock and roll, but that even has different meanings to different people and to us rock and roll has bits of funk and blues and country and folk and whatever else you want to throw in there. We like playing different sounds because there's different people in the audience, it's big boogie. It's hard to put us in a real category, we'd be one of those bands that in the old days, old days, when you go to the record store, when there were one, and they had all the bins, you know they'd have a hard time finding which bin to put it in. Come to say our show, you're going to see something new, expect that, you know that's what we shoot for is not necessarily improv on the stage every night, but we don't want to play the same thing every time. So you're going to get a little bit of a different feel, this tempo is a little different, but genuinely the song is going to be in the same range as usual, but you're going to see something different and that's what we're shooting for. That's what kitschy boogie is, we are an original band and we like playing music that we wrote and it's a little tougher in the bar scene and that kind of thing where a lot of bars like to have, you know either a country band or a band that plays at the top 40s and stuff, but we like playing around music, bringing it to people and hopefully they'll enjoy it. We're always trying to come up with new stuff, if you heard our band three years ago, we're maybe playing a couple of those songs, doesn't mean they're not still in our repertoire, but we're always trying new things because some things work and some things don't. And once folks come to see us or hear our recordings a few times they start to recognize the songs and then of course that's how anything grows, you know, but with us expect us, if you see us six months later we better have three more songs because that's what we're trying to do. Roads is made possible by the Minnesota Arts and Cultural Heritage Fund with money by the vote of the people November 4th, 2008.


Richard, and I'm here with Novus Energy Technologies, who are doing some energy harvesting techniques down at the IG TechEx show. I'm here with Peter, who's hoping to talk about some of his technologies. So, Peter, can you tell us what you're doing? Yes, sure. So, basically, we have a couple of different energy harvesting technologies. Number one, we have actually developed this material, which is centered material, which is different from the polycrystalline material, which is typically used in thermoelectrics. So, this is higher efficiency material, and will provide higher efficiency in both the thermoelectric power generation and also for heat pumps. What kind of efficiency do you have? So, for thermoelectric generation, we get increase of efficiency from 5% to 7%. So, you have about a 2% increase in efficiency, and then we've also developed this large area module. You can see the size of the conventional module, which is about 4 by 4 centimeters, and we've developed a 10 by 10 centimeter module, which basically can be used in large-scale applications, like power plants and heat, other areas of waste heat, industrial waste heat, where you have large amounts of waste heat that you want to take advantage of. We really believe that the thermal power plants like coal-based and oil-based power plants, and also other applications, such as geothermal, are very big applications. So, what kind of power output can you get out of it? Well, it's on the maximum. So, we've got 0.2.7 amps here. No, this is actually for the carcy cooler, but the power output for this small device will put out about 10 watts, and the large device will put out about 100 watts. Graham, what would you use out in the power plants to be the main application? So, the application would be, for example, the flue gases could be collected using a heat exchanger, and then be transmitted to the thermoelectric device, and then the thermoelectric device would then convert that to electric power. So, that would be one application. There's other areas in the power plant that could be used in other areas that actually can be utilized. Amazing. Okay, great. Oh, so this is actually carcy cooler technology. This is based on a thermoelectric that is pumping the heat away from this heat spreader material. The heat spreader material is basically a very high thermal conductivity material of 500 watts per meter Kelvin. And so, basically what it does is the thermoelectric devices are remotely located, so they could be located under the seat, and the heat spreader material then could be wrapped into the top of the seat, the person sitting on the seat, but only sit on the heat spreader material, and the heat spreader material would conduct the heat either to the person or away from the person, and you feel this is cold air, this is hot air, and that's based on the thermoelectrics and just the four corners of this carcy cooler. And we also have a cooling vest technology. So the cooling vest is a technology that uses a liquid cooling vest, and the thermoelectric either heats or cools the liquid in the vest, providing thermal comfort to the person who may be working in a hot or cold climate. First responders, soldiers, you know, they have to wear protective gear, and other people that are in hot or cold environments. Amazing. And what stage do you have with that? Is that a prototype or is it out there? No, it's a prototype. All these things are prototypes. So right now we're in the process of looking for investors that may be interested in this technology. Amazing. Oh, well, best of luck with it. It sounds like a great project. Thank you so much. Thank you. We've been really happy with ID TechX. You guys have treated us really well, and we're really looking forward to continuing the relationship with you guys. It's been fantastic. It's been a fantastic experience. We really appreciate all the work that you guys have done in putting this together. It's a really top notch operation. Great. Thank you very much.