1 00:00:08,000 --> 00:00:10,960 Hello, and welcome to the Physics World weekly 2 00:00:10,960 --> 00:00:12,900 podcast. I'm Hamish Johnston. 3 00:00:13,724 --> 00:00:14,544 This episode 4 00:00:14,925 --> 00:00:16,864 explores how quantum computing 5 00:00:17,244 --> 00:00:18,785 and artificial intelligence 6 00:00:19,324 --> 00:00:20,304 can be combined 7 00:00:20,684 --> 00:00:21,664 to help physicists 8 00:00:22,204 --> 00:00:24,304 search for rare interactions 9 00:00:24,925 --> 00:00:26,864 in data from an upgraded 10 00:00:27,244 --> 00:00:28,945 Large Hadron Collider. 11 00:00:29,989 --> 00:00:32,810 But first, I'd like to thank Delft Circuits 12 00:00:33,190 --> 00:00:35,929 for their generous support of this episode. 13 00:00:42,804 --> 00:00:45,545 This episode is supported by Delft circuits, 14 00:00:46,004 --> 00:00:48,184 a key enabler in the evolution 15 00:00:48,564 --> 00:00:49,945 of quantum technologies. 16 00:00:50,964 --> 00:00:54,824 While annealing quantum computers are already demonstrating 17 00:00:55,204 --> 00:00:56,265 practical value, 18 00:00:57,070 --> 00:00:59,869 Gate based systems are still on their way 19 00:00:59,869 --> 00:01:01,729 to large scale implementations. 20 00:01:02,909 --> 00:01:04,530 One of the biggest challenges 21 00:01:04,829 --> 00:01:05,810 is IO, 22 00:01:06,590 --> 00:01:09,329 reliably connecting thousands of qubits 23 00:01:09,734 --> 00:01:10,555 without compromising 24 00:01:11,015 --> 00:01:11,515 performance. 25 00:01:12,694 --> 00:01:13,674 Delft circuits 26 00:01:14,135 --> 00:01:16,715 addresses this bottleneck with a fundamentally 27 00:01:17,174 --> 00:01:19,275 different approach to cryogenic 28 00:01:19,975 --> 00:01:20,475 connectivity. 29 00:01:21,415 --> 00:01:22,234 Their flexible, 30 00:01:22,935 --> 00:01:23,435 multichannel 31 00:01:24,180 --> 00:01:25,240 planar cabling 32 00:01:25,859 --> 00:01:28,600 integrates filtering components and delivers 33 00:01:28,980 --> 00:01:29,480 exceptionally 34 00:01:29,859 --> 00:01:31,319 low heat load, 35 00:01:31,700 --> 00:01:34,760 paving the way to kilo cubit architectures 36 00:01:35,859 --> 00:01:37,640 because advancing science 37 00:01:37,939 --> 00:01:38,439 requires 38 00:01:39,204 --> 00:01:39,704 infrastructure 39 00:01:40,084 --> 00:01:42,185 built for the quantum age. 40 00:01:49,765 --> 00:01:53,340 In mid twenty twenty six, the Large Hadron 41 00:01:53,400 --> 00:01:56,859 Collider at CERN will shut down and undergo 42 00:01:57,079 --> 00:02:00,219 the final stage of the high luminosity 43 00:02:00,760 --> 00:02:01,260 upgrade. 44 00:02:01,959 --> 00:02:04,920 The aim is to increase the number of 45 00:02:04,920 --> 00:02:06,060 particle collisions 46 00:02:06,439 --> 00:02:07,340 in the LHC 47 00:02:08,055 --> 00:02:10,314 by about a factor of 10. 48 00:02:11,254 --> 00:02:12,635 By 2030, 49 00:02:12,854 --> 00:02:14,314 this should allow physicists 50 00:02:14,935 --> 00:02:16,715 to study rare particle 51 00:02:17,014 --> 00:02:17,514 interactions 52 00:02:17,974 --> 00:02:19,915 that are not visible today. 53 00:02:21,439 --> 00:02:22,340 These interactions 54 00:02:22,719 --> 00:02:24,580 create showers of particles 55 00:02:24,959 --> 00:02:25,939 that are detected 56 00:02:26,240 --> 00:02:27,780 by huge LHC 57 00:02:28,159 --> 00:02:28,659 experiments 58 00:02:29,280 --> 00:02:29,780 including 59 00:02:30,080 --> 00:02:31,539 ATLAS and CMS. 60 00:02:32,974 --> 00:02:34,914 These showers create distinctive 61 00:02:35,215 --> 00:02:35,715 patterns 62 00:02:36,014 --> 00:02:37,634 in the detector data 63 00:02:38,175 --> 00:02:39,555 which provide details 64 00:02:40,014 --> 00:02:42,834 of the sought after particle interactions. 65 00:02:44,334 --> 00:02:45,875 The challenge for physicists 66 00:02:46,449 --> 00:02:49,110 is to isolate the desired signals 67 00:02:49,489 --> 00:02:52,209 within the plethora of data that will be 68 00:02:52,209 --> 00:02:54,069 produced by an updated 69 00:02:54,370 --> 00:02:54,870 LHC. 70 00:02:56,129 --> 00:02:58,709 To do this, they must know how showers 71 00:02:58,769 --> 00:03:00,069 from rare interactions 72 00:03:00,689 --> 00:03:01,590 will interact 73 00:03:01,915 --> 00:03:02,735 with the detectors, 74 00:03:03,594 --> 00:03:05,294 and this is done using 75 00:03:05,675 --> 00:03:06,175 computationally 76 00:03:06,715 --> 00:03:07,215 intensive 77 00:03:08,075 --> 00:03:08,575 simulations. 78 00:03:10,075 --> 00:03:10,575 Therefore, 79 00:03:10,955 --> 00:03:13,615 it's not surprising that particle physicists 80 00:03:14,075 --> 00:03:16,094 are looking to quantum computers 81 00:03:16,680 --> 00:03:18,139 and artificial intelligence 82 00:03:18,760 --> 00:03:20,060 to boost the performance 83 00:03:20,520 --> 00:03:21,500 of these simulations. 84 00:03:23,000 --> 00:03:23,500 Recently, 85 00:03:23,800 --> 00:03:24,860 I was at Canada's 86 00:03:25,240 --> 00:03:26,379 Perimeter Institute 87 00:03:27,000 --> 00:03:28,620 and spoke to Javier 88 00:03:29,000 --> 00:03:29,500 Toledo 89 00:03:29,879 --> 00:03:30,379 Marine 90 00:03:30,885 --> 00:03:33,305 about the use of quantum assisted 91 00:03:33,844 --> 00:03:35,064 generative models 92 00:03:35,365 --> 00:03:36,745 in particle physics. 93 00:03:37,525 --> 00:03:39,784 Javier is based at the Triumph 94 00:03:40,165 --> 00:03:41,224 Particle Accelerator 95 00:03:41,605 --> 00:03:43,224 Center in British Columbia, 96 00:03:43,750 --> 00:03:46,969 and he also has an appointment at Perimeter. 97 00:03:47,590 --> 00:03:48,889 Here's that conversation. 98 00:03:57,235 --> 00:03:59,974 So I'm at the Perimeter Institute in Waterloo, 99 00:04:00,034 --> 00:04:03,014 Ontario, and I'm joined by Javier Toledo 100 00:04:03,314 --> 00:04:03,814 Marine. 101 00:04:04,275 --> 00:04:06,455 Hi, Javier. Welcome to the podcast. 102 00:04:06,995 --> 00:04:08,614 Hey, Hamish. Thank you. 103 00:04:09,330 --> 00:04:12,129 So Javier, you've recently published a paper that 104 00:04:12,129 --> 00:04:15,509 describes how quantum assisted artificial intelligence 105 00:04:16,129 --> 00:04:18,769 can be used to simulate how high energy 106 00:04:18,769 --> 00:04:19,269 particles 107 00:04:19,889 --> 00:04:21,189 interact with detectors 108 00:04:21,704 --> 00:04:23,644 such as ATLAS and CMS 109 00:04:24,105 --> 00:04:25,805 on the Large Hadron Collider. 110 00:04:26,584 --> 00:04:29,324 Why do physicists want to do these calculations, 111 00:04:29,785 --> 00:04:32,444 and why can't they be done using conventional 112 00:04:32,584 --> 00:04:33,084 computers? 113 00:04:33,544 --> 00:04:35,544 Yeah. So that's a great question. That's, I 114 00:04:35,544 --> 00:04:37,084 mean, that's the starting point. 115 00:04:37,649 --> 00:04:38,149 So 116 00:04:38,610 --> 00:04:39,830 as you might know, 117 00:04:40,449 --> 00:04:43,410 the Large Hadron Collider is closing. It's shutting 118 00:04:43,410 --> 00:04:43,910 down, 119 00:04:44,449 --> 00:04:47,430 for a few for some time now, 120 00:04:47,810 --> 00:04:50,629 because they're updating upgrading some of their detectors. 121 00:04:51,795 --> 00:04:53,795 So, so the new run of the LHC 122 00:04:53,795 --> 00:04:55,735 is called the high luminosity LHC. 123 00:04:56,115 --> 00:04:57,814 And so it's called high luminosity 124 00:04:58,115 --> 00:05:01,254 because, fundamentally, they're increasing the collision rate. 125 00:05:02,915 --> 00:05:06,250 So it's being increased by tenfold, I believe. 126 00:05:06,389 --> 00:05:08,550 And so that means that when they do 127 00:05:08,550 --> 00:05:11,209 these runs, we'll be getting more data. Right? 128 00:05:11,669 --> 00:05:13,990 And so this data is important because if 129 00:05:13,990 --> 00:05:15,129 you're trying to 130 00:05:15,589 --> 00:05:17,529 look at rare events, 131 00:05:18,375 --> 00:05:21,675 like, for instance, the dye Higgs, self interaction 132 00:05:21,814 --> 00:05:22,314 prompt, 133 00:05:23,334 --> 00:05:25,334 well, you actually have to it turns out 134 00:05:25,334 --> 00:05:27,354 that the dye Higgs is 1,000 135 00:05:27,735 --> 00:05:30,074 times less often than just the Higgs. 136 00:05:30,694 --> 00:05:33,149 So you need a lot more data, and 137 00:05:33,149 --> 00:05:35,470 so that's why it's being upgraded. And on 138 00:05:35,470 --> 00:05:36,930 the simulation side, 139 00:05:37,230 --> 00:05:38,290 you also need 140 00:05:38,750 --> 00:05:40,209 similar amount of data, 141 00:05:40,750 --> 00:05:42,769 and so that is becoming increasingly 142 00:05:43,149 --> 00:05:43,649 challenging. 143 00:05:44,110 --> 00:05:46,129 And one of the reasons why it's challenging 144 00:05:46,350 --> 00:05:47,730 is because of the 145 00:05:48,254 --> 00:05:48,754 calorimeter 146 00:05:49,134 --> 00:05:50,435 pipeline in the simulation. 147 00:05:51,134 --> 00:05:53,555 So there's in the ATLAS 148 00:05:54,014 --> 00:05:57,694 detect the ATLAS experiment, there's a detector, which 149 00:05:57,694 --> 00:05:58,514 is the calorimeter. 150 00:05:58,894 --> 00:06:01,235 And so when particles go through this calorimeter, 151 00:06:01,949 --> 00:06:03,490 they deposit the energy, 152 00:06:03,870 --> 00:06:06,189 but they also create secondary particles. And so 153 00:06:06,189 --> 00:06:07,810 they create these these showers. 154 00:06:08,509 --> 00:06:10,689 And so when that happens in the simulation, 155 00:06:10,829 --> 00:06:12,910 you need to track all of these particles 156 00:06:12,910 --> 00:06:14,449 as they go through the detector. 157 00:06:14,774 --> 00:06:16,375 And so you can imagine that if you 158 00:06:16,375 --> 00:06:18,535 increase the number of particles going through that, 159 00:06:18,535 --> 00:06:20,074 then you're going 160 00:06:20,615 --> 00:06:23,035 to need be needing more data from that. 161 00:06:23,654 --> 00:06:26,455 And so that is where it's becoming challenging 162 00:06:26,455 --> 00:06:27,595 because with the current 163 00:06:28,360 --> 00:06:30,939 computational capabilities, it turns out that it's 164 00:06:31,800 --> 00:06:32,300 unsurmountable 165 00:06:32,600 --> 00:06:35,660 at this point, using the traditional methods. 166 00:06:36,600 --> 00:06:38,759 So that is why people around the world, 167 00:06:38,759 --> 00:06:40,939 scientists, they're looking to use, 168 00:06:41,480 --> 00:06:44,194 different tools like deep generative models 169 00:06:44,574 --> 00:06:46,194 to generate these showers. 170 00:06:46,735 --> 00:06:49,235 So in our case, we're combining deep generative 171 00:06:49,295 --> 00:06:50,175 models with, 172 00:06:50,814 --> 00:06:53,634 quantum computing or, in this case, quantum annealers. 173 00:06:54,654 --> 00:06:57,160 So I think that answers the question. Right. 174 00:06:57,160 --> 00:06:59,160 So so the idea is that you're you're 175 00:06:59,160 --> 00:07:00,779 gonna have so many collisions 176 00:07:01,400 --> 00:07:03,740 going on in these detectors 177 00:07:04,360 --> 00:07:04,860 that, 178 00:07:05,319 --> 00:07:07,960 and you need to or I I suppose 179 00:07:07,960 --> 00:07:10,199 the physicists at CERN will need to focus 180 00:07:10,199 --> 00:07:11,764 in on a specific, 181 00:07:12,944 --> 00:07:15,745 event. But, of course, they they probably don't 182 00:07:15,745 --> 00:07:18,324 know exactly what that event will look like, 183 00:07:18,384 --> 00:07:20,785 and that's where your research comes in. You 184 00:07:20,865 --> 00:07:23,345 you're you do your simulations and you say, 185 00:07:23,345 --> 00:07:24,564 well, we think 186 00:07:24,889 --> 00:07:26,430 the event you're looking for 187 00:07:26,810 --> 00:07:28,970 is going to look like this in the 188 00:07:28,970 --> 00:07:31,709 detector. Yeah. That that's correct. So so basically, 189 00:07:31,769 --> 00:07:33,769 like, you have your experiment and the way 190 00:07:33,769 --> 00:07:35,850 to validate your experiment with theory, you do 191 00:07:35,850 --> 00:07:38,669 these simulations and then you compare your hypothesis 192 00:07:38,970 --> 00:07:41,435 from your simulations with what you actually get 193 00:07:41,435 --> 00:07:44,495 from from this experiment. Correct? I see. Okay. 194 00:07:45,914 --> 00:07:49,774 And can you explain in relatively simple terms, 195 00:07:50,074 --> 00:07:51,534 I mean, that might not be 196 00:07:51,834 --> 00:07:53,615 that might not be an easy task, 197 00:07:54,089 --> 00:07:54,589 How, 198 00:07:55,209 --> 00:07:56,589 your quantum assisted 199 00:07:56,889 --> 00:07:57,389 generative 200 00:07:57,930 --> 00:07:58,910 model works? 201 00:08:00,490 --> 00:08:03,209 Sure. Yeah. That's, okay. So let me give 202 00:08:03,209 --> 00:08:04,029 it a try. 203 00:08:04,970 --> 00:08:06,655 So, I mean, let let me start with 204 00:08:06,655 --> 00:08:08,995 generative models. So generative models, they're, 205 00:08:09,694 --> 00:08:11,694 way older than the current revolution that we're 206 00:08:11,694 --> 00:08:13,235 living with, with AI. 207 00:08:14,014 --> 00:08:16,415 And you can think about it as the 208 00:08:16,814 --> 00:08:18,814 like, they come to solve the task of 209 00:08:18,814 --> 00:08:19,314 generating 210 00:08:20,360 --> 00:08:24,220 data, generating samples from specific probability distributions. 211 00:08:24,600 --> 00:08:27,899 Right? So sometimes, like, many years ago when, 212 00:08:28,360 --> 00:08:31,500 you didn't have all these, programming languages, like, 213 00:08:31,639 --> 00:08:34,759 when you wanted to generate, Gaussian distributed random 214 00:08:34,759 --> 00:08:35,259 numbers, 215 00:08:35,815 --> 00:08:37,274 you would normally use, 216 00:08:37,654 --> 00:08:39,975 the Box Mueller method in which you would 217 00:08:39,975 --> 00:08:42,855 generate uniformly distributed random numbers, and then you 218 00:08:42,855 --> 00:08:45,834 would give those random numbers to a function, 219 00:08:46,054 --> 00:08:48,074 and then you would get your Gaussian distributed 220 00:08:48,294 --> 00:08:49,080 random numbers. 221 00:08:49,639 --> 00:08:51,240 These days, you you I mean, you just 222 00:08:51,240 --> 00:08:53,639 use your whatever. If you're using Python, it 223 00:08:53,639 --> 00:08:55,799 already has a a package that does that 224 00:08:55,799 --> 00:08:57,960 for you. So in that way, it's like 225 00:08:57,960 --> 00:09:00,040 we're trying to generate what we're working on 226 00:09:00,040 --> 00:09:03,160 is generating these showers, which come from a 227 00:09:03,160 --> 00:09:04,299 probability distribution, 228 00:09:04,695 --> 00:09:06,934 but we don't know that probability distribution a 229 00:09:06,934 --> 00:09:07,434 priori 230 00:09:08,134 --> 00:09:09,975 rate. So what we're doing is that we're 231 00:09:09,975 --> 00:09:12,075 sampling from an easier distribution, 232 00:09:13,014 --> 00:09:15,174 and then we pass it through some function. 233 00:09:15,174 --> 00:09:16,855 In this case, this function is a neural 234 00:09:16,855 --> 00:09:19,960 network, and then it gets converted to the 235 00:09:19,960 --> 00:09:20,460 shower. 236 00:09:20,840 --> 00:09:23,080 Now the way we are doing it is 237 00:09:23,080 --> 00:09:25,879 that we use quantum annealers as the initial 238 00:09:25,879 --> 00:09:26,379 sampler. 239 00:09:27,000 --> 00:09:29,160 So we sample from the quantum annealer. We 240 00:09:29,160 --> 00:09:31,340 get a a random vector. 241 00:09:31,664 --> 00:09:33,264 We pass it through a neural network, and 242 00:09:33,264 --> 00:09:34,164 we get the shower. 243 00:09:34,865 --> 00:09:36,865 Now that's the way once the model is 244 00:09:36,865 --> 00:09:39,105 trained, we we use it. Right? To train 245 00:09:39,105 --> 00:09:40,565 it, it's a bit more complicated. 246 00:09:41,345 --> 00:09:42,084 But, essentially, 247 00:09:42,464 --> 00:09:42,964 it's 248 00:09:43,424 --> 00:09:45,184 you can think about it as a data 249 00:09:45,184 --> 00:09:48,149 driven machine learning problem in which you have 250 00:09:48,210 --> 00:09:50,290 a data set that you feed to this 251 00:09:50,290 --> 00:09:52,629 model and you try to reconstruct it. 252 00:09:53,009 --> 00:09:55,570 And by reconstructing it, then you're you're, fine 253 00:09:55,570 --> 00:09:57,590 tuning these parameters in your neural network. 254 00:09:58,049 --> 00:10:00,129 And then you use this sampler, the quantum 255 00:10:00,129 --> 00:10:01,889 analyzer, to to sample from it and pass 256 00:10:01,889 --> 00:10:03,304 it through the through the neural 257 00:10:03,684 --> 00:10:05,284 network. I see. And and the data that 258 00:10:05,284 --> 00:10:06,504 you're using, is that 259 00:10:06,965 --> 00:10:07,784 data from 260 00:10:08,164 --> 00:10:10,264 scattering experiments that have been done 261 00:10:10,725 --> 00:10:11,465 in detectors? 262 00:10:12,004 --> 00:10:12,504 Or 263 00:10:13,125 --> 00:10:14,264 do you also incorporate 264 00:10:14,884 --> 00:10:15,384 physics 265 00:10:15,759 --> 00:10:17,519 into that as well, or is it purely 266 00:10:17,519 --> 00:10:19,919 just the the data, or is it both, 267 00:10:19,919 --> 00:10:20,579 I suppose? 268 00:10:21,360 --> 00:10:23,679 So we started off by using this dataset 269 00:10:23,679 --> 00:10:24,899 called the Cayo Challenge. 270 00:10:25,759 --> 00:10:27,839 And so the Cayo Challenge is a challenge 271 00:10:27,839 --> 00:10:30,914 that started, it was launched in 2022, 272 00:10:31,394 --> 00:10:33,375 by a group of, 273 00:10:33,914 --> 00:10:36,834 scientists around the world. And so the the 274 00:10:36,834 --> 00:10:38,674 scope of this challenge is precisely to see 275 00:10:38,674 --> 00:10:40,995 if we can use generative models to, 276 00:10:41,794 --> 00:10:44,399 to to sample, to generate these showers. 277 00:10:44,860 --> 00:10:47,120 So it's a publicly available dataset, 278 00:10:47,740 --> 00:10:49,200 and it's a 279 00:10:49,899 --> 00:10:52,540 different dataset than what you would normally get 280 00:10:52,540 --> 00:10:53,679 from the simulation 281 00:10:54,059 --> 00:10:56,475 pipeline that they used in in ATLAS. Right? 282 00:10:56,554 --> 00:10:59,754 It's still generated from that simulation pipeline, but 283 00:10:59,754 --> 00:11:03,434 it's then processed in a way that you 284 00:11:03,434 --> 00:11:05,754 might say it makes it maybe easier for 285 00:11:05,754 --> 00:11:07,674 the neural network to learn it. Right? That 286 00:11:07,834 --> 00:11:09,269 I mean, very yeah. 287 00:11:10,470 --> 00:11:12,470 So that's the dataset that we used in 288 00:11:12,470 --> 00:11:13,529 this this paper. 289 00:11:14,149 --> 00:11:18,149 And so right now, we're now looking to 290 00:11:18,149 --> 00:11:18,649 use 291 00:11:19,509 --> 00:11:22,330 data that's actually generated for the ATLAS experiment, 292 00:11:22,389 --> 00:11:25,654 more realistic dataset. Right? Now this dataset is 293 00:11:25,654 --> 00:11:27,975 composed by like, you think think about it 294 00:11:27,975 --> 00:11:30,294 this way. So when the collision happens, you 295 00:11:30,294 --> 00:11:30,794 get, 296 00:11:31,575 --> 00:11:33,415 particles going through that. Right? And then these 297 00:11:33,415 --> 00:11:35,415 particles go through the detectors. Right? So the 298 00:11:35,415 --> 00:11:37,575 data that we're feeding through to to our 299 00:11:37,575 --> 00:11:39,720 model, it's the data in which you you 300 00:11:39,720 --> 00:11:43,100 have that an incident particle entering the calorimeter 301 00:11:43,480 --> 00:11:45,019 with a specific energy 302 00:11:45,399 --> 00:11:47,480 and a specific type. Right? So a specific 303 00:11:47,480 --> 00:11:49,899 energy in the order of of GeV, 304 00:11:50,440 --> 00:11:52,945 giga electron volts. And the type of particle 305 00:11:52,945 --> 00:11:56,384 that we've considered thus far is, electrons. Right? 306 00:11:56,384 --> 00:11:58,165 But we are also looking to consider, 307 00:11:59,024 --> 00:12:02,004 photons and pions in the near future. 308 00:12:02,625 --> 00:12:03,285 I see. 309 00:12:03,679 --> 00:12:06,579 So Javier, you've used a a quantum annealer 310 00:12:07,039 --> 00:12:08,740 from D Wave systems. 311 00:12:09,279 --> 00:12:12,000 What is quantum annealing? And how does it 312 00:12:12,000 --> 00:12:15,279 differ from the the gate based models of 313 00:12:15,279 --> 00:12:17,024 quantum computation that 314 00:12:17,425 --> 00:12:19,264 maybe some of our listeners will be more 315 00:12:19,264 --> 00:12:20,165 familiar with. 316 00:12:21,184 --> 00:12:22,644 Right. Yeah. So then 317 00:12:23,345 --> 00:12:25,045 okay. So you have 318 00:12:25,665 --> 00:12:28,945 basically two different types of quantum computing, which 319 00:12:28,945 --> 00:12:30,725 is, like you mentioned, gate based 320 00:12:31,160 --> 00:12:33,019 and adiabatic quantum computing. 321 00:12:34,120 --> 00:12:36,940 So in theory, both of them are universal, 322 00:12:37,480 --> 00:12:39,420 quantum tuning in the sense that you can 323 00:12:39,960 --> 00:12:43,240 do any quantum algorithm and and using both 324 00:12:43,240 --> 00:12:43,740 technologies. 325 00:12:44,575 --> 00:12:47,294 In practice, there are nuances. You have, like, 326 00:12:47,294 --> 00:12:50,174 decoherence. You have noise in it in both 327 00:12:50,174 --> 00:12:50,674 cases. 328 00:12:51,214 --> 00:12:53,615 And so in the case of gate based 329 00:12:53,615 --> 00:12:54,595 quantum computing, 330 00:12:55,375 --> 00:12:58,495 what you're using there are unitary gates that 331 00:12:58,495 --> 00:13:00,115 operate on the qubits. 332 00:13:00,550 --> 00:13:02,950 So you can think about it more like 333 00:13:02,950 --> 00:13:04,490 a in a digital way. 334 00:13:04,950 --> 00:13:07,290 Whereas adiabatic quantum computing, you're 335 00:13:08,070 --> 00:13:08,570 continuously 336 00:13:09,110 --> 00:13:12,790 evolving your quantum system with, what's called an 337 00:13:12,790 --> 00:13:14,090 evolution operator. 338 00:13:14,434 --> 00:13:16,054 You're you're evolving your system. 339 00:13:16,914 --> 00:13:20,514 And what this adiabatic quantum computing does, it 340 00:13:20,514 --> 00:13:24,214 finds the ground state of a specific Hamiltonian 341 00:13:24,434 --> 00:13:25,735 that encodes your problem. 342 00:13:26,514 --> 00:13:28,500 Now like I said, in practice, there are 343 00:13:28,740 --> 00:13:29,639 lots of nuances 344 00:13:30,340 --> 00:13:30,840 related 345 00:13:31,299 --> 00:13:32,039 to decoherence 346 00:13:32,500 --> 00:13:35,220 and noise and error correction and and error 347 00:13:35,220 --> 00:13:36,519 in your in your system. 348 00:13:37,539 --> 00:13:39,620 So in our case, we're not when we 349 00:13:39,620 --> 00:13:42,019 evolve this simultaneous to get the the ground 350 00:13:42,019 --> 00:13:43,779 state, we're not really interested in the ground 351 00:13:43,779 --> 00:13:44,235 state. 352 00:13:44,875 --> 00:13:46,654 We're interested in getting a 353 00:13:47,115 --> 00:13:49,375 sample that comes from a Boltzmann distribution. 354 00:13:49,995 --> 00:13:51,695 So it just happens that 355 00:13:52,714 --> 00:13:54,014 this quantum annealer, 356 00:13:54,394 --> 00:13:55,115 when you, 357 00:13:55,514 --> 00:13:57,534 when you do the annealing process 358 00:13:58,079 --> 00:14:01,360 in a specific time window, you're going to 359 00:14:01,360 --> 00:14:03,360 get a sample that comes from a Boltzmann 360 00:14:03,360 --> 00:14:06,399 distribution. Right? And so we use that because, 361 00:14:06,399 --> 00:14:08,879 basically, our framework is a combination of what's 362 00:14:08,879 --> 00:14:10,500 called a variational autoencoder 363 00:14:11,164 --> 00:14:13,584 and what's called a restricted Boltzmann machine. 364 00:14:14,044 --> 00:14:16,784 So this restricted Boltzmann machine is, 365 00:14:17,404 --> 00:14:19,644 like I mentioned earlier, where we sample from. 366 00:14:19,644 --> 00:14:21,024 We use it as a sampler. 367 00:14:21,564 --> 00:14:23,345 And and so that makes it, 368 00:14:24,049 --> 00:14:27,169 oh, like, basically a direct way to sample 369 00:14:27,169 --> 00:14:29,669 from a quantum annealer. Like, it's completely straightforward, 370 00:14:30,129 --> 00:14:31,750 sampling from a quantum annealer, 371 00:14:32,209 --> 00:14:34,370 and using it as a as a quantum 372 00:14:34,370 --> 00:14:36,850 version of a restricted Bose machine. I see. 373 00:14:36,850 --> 00:14:38,850 And so so is the is the idea 374 00:14:38,850 --> 00:14:41,355 that, you know, you've you've this electron has 375 00:14:41,355 --> 00:14:42,654 gone into the detector, 376 00:14:43,034 --> 00:14:44,414 and it's going to interact. 377 00:14:44,794 --> 00:14:46,414 And there's lots of different 378 00:14:47,195 --> 00:14:48,495 things that can happen. 379 00:14:48,955 --> 00:14:51,355 You know, this interaction can occur, then that 380 00:14:51,355 --> 00:14:54,090 interaction can occur. And, you know, it probably 381 00:14:54,090 --> 00:14:55,309 just blows up into 382 00:14:55,850 --> 00:14:56,430 a huge, 383 00:14:57,370 --> 00:14:59,629 I suppose, space of possible things. 384 00:15:00,170 --> 00:15:02,170 Do is the idea of the of the 385 00:15:02,170 --> 00:15:02,670 quantum 386 00:15:03,050 --> 00:15:06,269 annealer, does it give you the most probable, 387 00:15:08,100 --> 00:15:08,595 outcome 388 00:15:08,995 --> 00:15:10,534 of of that collision 389 00:15:11,394 --> 00:15:13,554 process? Is is that what you're looking for, 390 00:15:13,554 --> 00:15:14,914 or is it is it a bit more 391 00:15:14,914 --> 00:15:16,134 complicated than that? 392 00:15:16,754 --> 00:15:18,615 Well, I would say that it's not necessarily 393 00:15:18,754 --> 00:15:19,735 the most probable, 394 00:15:20,754 --> 00:15:22,929 event that we're looking for. We're just looking 395 00:15:22,929 --> 00:15:24,789 to be able to sample from 396 00:15:25,570 --> 00:15:27,730 from the manifold where these like, when when 397 00:15:27,730 --> 00:15:29,649 you mentioned this, you can think about, like, 398 00:15:29,649 --> 00:15:31,889 when this electron goes through this detector and 399 00:15:31,889 --> 00:15:33,029 generates the shower 400 00:15:33,490 --> 00:15:34,789 so that event, 401 00:15:36,004 --> 00:15:37,625 falls in some kind of manifold. 402 00:15:38,325 --> 00:15:40,004 And so we want to be able to 403 00:15:40,004 --> 00:15:42,485 sample from from those manifold from from that 404 00:15:42,485 --> 00:15:43,464 manifold. Right? 405 00:15:43,764 --> 00:15:46,884 And so this, this quantum annealer, this this 406 00:15:46,884 --> 00:15:49,620 framework of combining the quantum annealer with the 407 00:15:49,860 --> 00:15:50,839 variational encoder 408 00:15:51,779 --> 00:15:54,360 allows us to efficiently sample 409 00:15:54,740 --> 00:15:57,860 from that, from that manifold. Right? And so 410 00:15:57,860 --> 00:16:00,899 when I say efficiently, I'm thinking of mainly 411 00:16:00,899 --> 00:16:02,625 two things. One is 412 00:16:03,184 --> 00:16:04,325 accurately capturing 413 00:16:05,184 --> 00:16:07,504 the the physics or the properties of these 414 00:16:07,504 --> 00:16:08,325 these showers, 415 00:16:09,424 --> 00:16:12,384 for which we we we have metrics for 416 00:16:12,384 --> 00:16:14,485 that, and we can compare our 417 00:16:14,949 --> 00:16:17,350 framework with other frameworks, and we're doing well 418 00:16:17,350 --> 00:16:19,190 on that front. But, also, you wanna do 419 00:16:19,190 --> 00:16:21,350 it fast. Right? Because in the end, that's 420 00:16:21,350 --> 00:16:23,029 the the problem that we're tackling that. Can 421 00:16:23,029 --> 00:16:24,250 we generate those showers, 422 00:16:24,709 --> 00:16:27,225 and can we do it faster than, like, 423 00:16:27,384 --> 00:16:27,884 current, 424 00:16:28,664 --> 00:16:31,324 traditional methods are able to do so? Right. 425 00:16:31,544 --> 00:16:34,024 And the the I mean, these the the 426 00:16:34,024 --> 00:16:36,345 way that a particle will interact with a 427 00:16:36,345 --> 00:16:38,204 detector, that's, I mean, that's 428 00:16:38,904 --> 00:16:40,605 governed by quantum mechanics. 429 00:16:41,480 --> 00:16:43,740 You know, it's all probabilities, etcetera. 430 00:16:44,279 --> 00:16:46,679 Does the fact that you're using a quantum 431 00:16:46,679 --> 00:16:47,179 annealer, 432 00:16:47,720 --> 00:16:50,360 does that help you go faster in the 433 00:16:50,360 --> 00:16:52,120 sense that, you know, the system you're trying 434 00:16:52,120 --> 00:16:53,419 to simulate is quantum, 435 00:16:53,879 --> 00:16:55,980 your hardware is quantum? 436 00:16:56,725 --> 00:16:58,725 Therefore, you know, that that I suppose the 437 00:16:58,725 --> 00:17:00,264 old argument of Richard Feynman 438 00:17:00,644 --> 00:17:03,205 about, you know, why quantum computers could be 439 00:17:03,205 --> 00:17:06,724 useful for studying quantum systems. Does that apply 440 00:17:06,724 --> 00:17:07,224 here? 441 00:17:08,819 --> 00:17:10,919 Yeah. That's a very interesting question. 442 00:17:12,099 --> 00:17:14,339 I would say that at this point, it 443 00:17:14,339 --> 00:17:16,579 doesn't apply because we still like, when we 444 00:17:16,579 --> 00:17:18,119 sample from the quantum annealer, 445 00:17:19,220 --> 00:17:19,720 we're, 446 00:17:20,019 --> 00:17:22,419 I mean, we're doing a classical measurement, and 447 00:17:22,419 --> 00:17:25,945 then we're passing that through a classical algorithm. 448 00:17:25,945 --> 00:17:27,164 Right? So 449 00:17:27,625 --> 00:17:29,465 I would say that it doesn't apply here 450 00:17:29,465 --> 00:17:31,484 at this point, but that's certainly, 451 00:17:32,105 --> 00:17:34,664 like, the the scope of this project. Right? 452 00:17:34,664 --> 00:17:36,744 Because at this point, the the training that 453 00:17:36,744 --> 00:17:37,289 we do, 454 00:17:37,850 --> 00:17:38,750 it's basically 455 00:17:39,049 --> 00:17:40,190 a classical training. 456 00:17:40,970 --> 00:17:44,509 But we're certainly, like, we're looking forward to, 457 00:17:45,450 --> 00:17:47,869 getting more from this these quantum annealers 458 00:17:48,250 --> 00:17:50,250 and and using them for for this type 459 00:17:50,250 --> 00:17:53,204 of physics problem. Yeah. I see. And what 460 00:17:53,204 --> 00:17:54,025 does success 461 00:17:54,404 --> 00:17:56,565 mean? How how have you shown that your 462 00:17:56,565 --> 00:17:58,345 technique is useful for predicting 463 00:17:58,804 --> 00:18:01,065 how particles interact with the detectors? 464 00:18:02,724 --> 00:18:04,345 So at this point, what we're 465 00:18:04,884 --> 00:18:06,884 what we're using are it's a set of 466 00:18:06,884 --> 00:18:09,839 metrics that come from the kalo challenge and 467 00:18:09,839 --> 00:18:13,059 also metrics that are typically used by, 468 00:18:13,839 --> 00:18:15,059 high energy physicists. 469 00:18:15,919 --> 00:18:18,879 And so what we're basically doing is that 470 00:18:18,879 --> 00:18:20,419 we're comparing the 471 00:18:20,744 --> 00:18:21,484 the performance 472 00:18:21,785 --> 00:18:24,424 of our framework with traditional with the traditional 473 00:18:24,424 --> 00:18:27,484 method and also with other generative models. Right? 474 00:18:27,704 --> 00:18:28,444 So that's, 475 00:18:29,785 --> 00:18:32,505 how success looks like in at at this 476 00:18:32,505 --> 00:18:33,244 point. Right? 477 00:18:33,809 --> 00:18:34,549 By looking, 478 00:18:35,009 --> 00:18:37,089 how do we perform compared to to other 479 00:18:37,089 --> 00:18:39,169 methods? Right? And so at this at this 480 00:18:39,169 --> 00:18:39,669 stage, 481 00:18:40,049 --> 00:18:40,869 we're doing 482 00:18:41,409 --> 00:18:43,809 quite well in terms of quality and in 483 00:18:43,809 --> 00:18:44,549 terms of 484 00:18:45,089 --> 00:18:46,149 speed up. It's, 485 00:18:47,195 --> 00:18:48,954 as far as we we know, it's the 486 00:18:48,954 --> 00:18:50,095 the fastest method, 487 00:18:50,795 --> 00:18:53,035 compared to other What what what I mean, 488 00:18:53,035 --> 00:18:55,115 you mentioned speed up. What I mean, how 489 00:18:55,115 --> 00:18:57,055 much faster is your method than 490 00:18:57,434 --> 00:18:59,055 a method using a conventional 491 00:18:59,640 --> 00:19:00,140 supercomputer 492 00:19:00,440 --> 00:19:02,839 or what what whatever these calculations are done 493 00:19:02,839 --> 00:19:03,740 on at the moment? 494 00:19:04,839 --> 00:19:06,759 Right. Okay. So let me be nuanced here 495 00:19:06,759 --> 00:19:09,400 in this this question. So we've used the 496 00:19:09,559 --> 00:19:11,240 for the for the Caleb challenge, which is 497 00:19:11,240 --> 00:19:12,914 the dataset that we used for this, 498 00:19:13,555 --> 00:19:14,055 paper. 499 00:19:15,075 --> 00:19:16,295 And our results 500 00:19:16,755 --> 00:19:18,295 show that we're about 501 00:19:18,914 --> 00:19:22,434 1,000 times faster than the traditional method, which 502 00:19:22,434 --> 00:19:23,815 is called g e on four. 503 00:19:25,075 --> 00:19:26,055 Compared to 504 00:19:26,389 --> 00:19:28,950 other generative models that were part of the 505 00:19:28,950 --> 00:19:29,849 Cayla Challenge, 506 00:19:30,230 --> 00:19:31,049 we're also 507 00:19:32,950 --> 00:19:35,450 faster than than Dose Method, currently. 508 00:19:36,389 --> 00:19:38,710 I see. And are you are you at 509 00:19:38,710 --> 00:19:40,634 the point where you're sort of moving out 510 00:19:40,954 --> 00:19:44,315 from the research phase, let's say, to, you 511 00:19:44,315 --> 00:19:46,634 know, sort of letting this thing roll and 512 00:19:46,634 --> 00:19:50,315 doing calculations that you will then pass on 513 00:19:50,315 --> 00:19:52,650 to physicists working at CERN? 514 00:19:53,369 --> 00:19:55,369 Or is there more work to do before 515 00:19:55,369 --> 00:19:56,349 you can do that? 516 00:19:57,130 --> 00:19:57,630 Yeah. 517 00:19:58,170 --> 00:20:00,330 Great question. So that there's still work to 518 00:20:00,330 --> 00:20:01,869 be done because, like I mentioned, 519 00:20:02,250 --> 00:20:02,730 we've, 520 00:20:03,690 --> 00:20:05,690 we first used the Kalot challenge dataset, and 521 00:20:05,690 --> 00:20:08,349 now we're moving to ATLAS generated dataset. 522 00:20:08,865 --> 00:20:10,785 So now, like, we what we have to 523 00:20:10,785 --> 00:20:11,444 do is 524 00:20:11,904 --> 00:20:14,224 everything we showed for the kilo challenge, we 525 00:20:14,224 --> 00:20:15,444 now need to be 526 00:20:16,065 --> 00:20:17,984 we now need to show it for this 527 00:20:17,984 --> 00:20:19,125 ATLAS dataset. 528 00:20:20,144 --> 00:20:22,080 So that means we need 529 00:20:22,460 --> 00:20:23,600 to train models 530 00:20:24,220 --> 00:20:25,440 on multiple datasets, 531 00:20:26,140 --> 00:20:29,279 run these benchmarks, and show that we're actually 532 00:20:29,660 --> 00:20:31,740 as good as we should for the kill 533 00:20:31,740 --> 00:20:34,320 challenge. So I think that's still gonna take, 534 00:20:35,534 --> 00:20:38,194 a reason about a reasonable amount of time, 535 00:20:38,654 --> 00:20:39,714 but we're certainly 536 00:20:40,494 --> 00:20:41,794 looking forward to 537 00:20:42,335 --> 00:20:44,414 to pushing this to to a place in 538 00:20:44,414 --> 00:20:46,815 which it could be deployed. Right? Deployable, at 539 00:20:46,815 --> 00:20:47,315 least. 540 00:20:47,615 --> 00:20:49,474 And final question, Javier. 541 00:20:50,180 --> 00:20:52,119 Are there any sort of non 542 00:20:52,980 --> 00:20:53,960 particle physics 543 00:20:54,259 --> 00:20:55,960 applications to this technique? 544 00:20:56,500 --> 00:20:57,779 I mean, it could could it be used 545 00:20:57,779 --> 00:21:00,500 in other parts of physics or you useful 546 00:21:00,500 --> 00:21:03,220 to physicists working in condensed matter or, I 547 00:21:03,220 --> 00:21:04,440 don't know, even biologists, 548 00:21:05,054 --> 00:21:05,554 chemists, 549 00:21:05,855 --> 00:21:08,254 or I mean even, I don't know, a 550 00:21:08,254 --> 00:21:11,294 trucking company that wants to, you know, plan 551 00:21:11,294 --> 00:21:13,154 the most efficient route across, 552 00:21:13,774 --> 00:21:16,014 across Canada. Are there are there up other 553 00:21:16,014 --> 00:21:17,875 applications for this, or is it only 554 00:21:18,190 --> 00:21:20,049 sort of a particle physics thing? 555 00:21:20,750 --> 00:21:22,990 Right. Okay. So the the what we call 556 00:21:22,990 --> 00:21:25,549 the Cayo QVAE framework, which is the VAE 557 00:21:25,549 --> 00:21:27,950 with the using the the quantum annealer as 558 00:21:27,950 --> 00:21:28,609 a sampler, 559 00:21:28,910 --> 00:21:31,789 that you that you can use it for 560 00:21:31,789 --> 00:21:32,289 any 561 00:21:32,865 --> 00:21:35,365 machine learning problem generative model. Right? 562 00:21:35,664 --> 00:21:37,845 So you can use it either to generate 563 00:21:38,545 --> 00:21:39,045 pictures 564 00:21:39,744 --> 00:21:43,045 of people or of dogs or whatnot. Right? 565 00:21:43,664 --> 00:21:46,410 So it's not limited to to just physics. 566 00:21:46,410 --> 00:21:46,910 Right? 567 00:21:48,090 --> 00:21:50,090 However, you did mention, right, can you use 568 00:21:50,090 --> 00:21:52,830 it to optimize the routes of, 569 00:21:53,610 --> 00:21:56,250 no, like, delivery stuff like that. Right? So 570 00:21:56,250 --> 00:21:58,330 I guess that's a a good question. Right? 571 00:21:58,330 --> 00:22:00,430 So so quantum annealers, the 572 00:22:01,565 --> 00:22:03,884 the motivation is to to find this ground 573 00:22:03,884 --> 00:22:06,144 state. Right? So there are some problems 574 00:22:06,765 --> 00:22:08,924 that in principle you can map, like problems 575 00:22:08,924 --> 00:22:11,404 of optimization of routes that you can map 576 00:22:11,404 --> 00:22:13,750 to a quantum annealer. Right? So in that 577 00:22:13,750 --> 00:22:14,250 case, 578 00:22:14,950 --> 00:22:17,910 you wouldn't actually need the variational encoder. You 579 00:22:17,910 --> 00:22:20,730 would just use the the quantum annealer to 580 00:22:21,029 --> 00:22:24,150 to find that that, ground state of that 581 00:22:24,150 --> 00:22:24,650 optimum 582 00:22:25,029 --> 00:22:25,529 solution. 583 00:22:26,545 --> 00:22:28,785 That's the, I suppose, is that the famous 584 00:22:28,785 --> 00:22:29,765 traveling salesman 585 00:22:30,144 --> 00:22:32,384 problem, which I think is really difficult, isn't 586 00:22:32,384 --> 00:22:33,525 it? That's correct. 587 00:22:33,984 --> 00:22:34,484 Yeah. 588 00:22:35,424 --> 00:22:38,325 Yeah. That's correct. That's, that's the the salesperson, 589 00:22:39,105 --> 00:22:40,005 traveling problem. 590 00:22:41,710 --> 00:22:42,369 Of course, 591 00:22:42,829 --> 00:22:45,069 there you might have some extra constraints, like, 592 00:22:45,069 --> 00:22:46,829 in a real problem. Right? And that's where 593 00:22:46,829 --> 00:22:47,490 it gets 594 00:22:47,950 --> 00:22:50,829 really complicated. How how do you embed those 595 00:22:50,829 --> 00:22:51,329 constraints 596 00:22:51,869 --> 00:22:54,369 into the quantum in either? Mhmm. Right? 597 00:22:55,464 --> 00:22:57,704 Well, thanks so much, Javier. Thanks for speaking 598 00:22:57,704 --> 00:22:59,944 to Physics World about your research, and we 599 00:22:59,944 --> 00:23:01,944 look forward to, to hearing more from you 600 00:23:01,944 --> 00:23:02,764 and your colleagues. 601 00:23:03,384 --> 00:23:04,444 Thank you so much. 602 00:23:12,230 --> 00:23:14,730 That was Javier Toledo Marine 603 00:23:15,109 --> 00:23:19,130 in conversation with me at Canada's Perimeter Institute. 604 00:23:19,990 --> 00:23:22,970 The paper we spoke about is called Conditioned 605 00:23:23,744 --> 00:23:24,884 quantum assisted 606 00:23:25,424 --> 00:23:26,724 deep generative 607 00:23:27,424 --> 00:23:27,924 surrogate 608 00:23:28,305 --> 00:23:29,204 for particle 609 00:23:29,744 --> 00:23:30,244 calorimeter 610 00:23:31,025 --> 00:23:31,525 interactions, 611 00:23:32,224 --> 00:23:34,404 and it's published in NPJ 612 00:23:35,105 --> 00:23:36,325 Quantum Information. 613 00:23:37,099 --> 00:23:39,180 I'll put a link to the paper in 614 00:23:39,180 --> 00:23:40,480 the podcast notes. 615 00:23:41,340 --> 00:23:43,980 I'd like to thank Delft Circuits for their 616 00:23:43,980 --> 00:23:46,000 generous support of this episode, 617 00:23:46,619 --> 00:23:48,720 and also thanks to Javier 618 00:23:49,099 --> 00:23:49,599 Toledo 619 00:23:49,900 --> 00:23:50,400 Marine 620 00:23:50,714 --> 00:23:52,095 for joining me today. 621 00:23:52,474 --> 00:23:54,494 And as always, our producer, 622 00:23:54,875 --> 00:23:56,015 Fred Ailes. 623 00:23:56,714 --> 00:23:59,034 I'll be back again next week when I'm 624 00:23:59,034 --> 00:24:01,534 joined by a physicist and a sculptor 625 00:24:01,994 --> 00:24:04,875 to talk about the science and art of 626 00:24:04,875 --> 00:24:06,174 quantum steampunk. 627 00:24:07,039 --> 00:24:08,099 See you then. 628 00:24:14,319 --> 00:24:18,019 As gate based quantum computing continues to scale, 629 00:24:18,480 --> 00:24:21,380 Delft circuits provides the IO solutions 630 00:24:21,914 --> 00:24:23,215 that make it possible.