Pioneering Quantum Material Discovery with OTI Lumionics

[00:00:00] Welcome to The Quantum State, a podcast exploring the latest research and innovation in quantum computing. Join us as we dive into ground-breaking breakthroughs, trends and use shaping the quantum landscape.

[00:00:31] Hey everyone and welcome back to The Quantum State. Today we have Scott Jenner, the vice president of materials discovery at OTI Lumionics.

[00:00:41] So Scott thank you so much for coming on the show and talking to us about really interesting world of quantum computing and how the materials all come together.

[00:00:51] Thank you for having me.

[00:00:53] Definitely and so can you share a little bit about your journey into the field of material science and how quantum computing has become a part of your work.

[00:01:00] There's been a lot of interesting advances in the field over the many years that people have been doing quantum computing, but one of the big scalability challenges we talk about on this podcast is in the materials especially for systems.

[00:01:14] Yeah, please tell us a bit about your career journey and what you all are doing at OTI Lumionics.

[00:01:20] Yes, our journey with quantum computing actually began in 2017. I had Michael Lylender convinced me to move from my previous job that working in the sign far from

[00:01:34] to start the idea of using mathematics and see that issue tools to improve design and solving different real stuff around was a historical design field.

[00:01:47] I feel those doing for former and you know we're plugging around with easy quantum, some methods and what the dynamics of this and a much larger is the model developments for predicting.

[00:02:02] I'm sure you all is putting how material behavior manufacturing etc and then one day he went to a conference in Quebec City and he carried on away back from that conference.

[00:02:16] I was in a conference with a professor at the VCR area in Moe who was and recently invested in a world of star up with the quantum computing and they are chatting and well as well.

[00:02:31] You know, I would did a doing quantum computing for material simulations and then he took my back at Toronto and he asked me to send us in motion.

[00:02:40] I was a little bit of a work and then they just well aligned up the creative destruction. I have a new program that they opened for getting so we applied and got it for that and absolutely with spread out journey into quantum computing.

[00:02:59] Can you explain how OTI luminatics utilizes quantum simulations and machine learnings or developing new materials for OIDs?

[00:03:10] Yes, so the quantum computing is really a insellering or replacement for conventional quantum chemistry calculations done on the possible computer.

[00:03:23] So quantum computing really enhances the accuracy why it's a quantum chemistry by essentially because we specifically are trying to solve the Schrodinger equation for any basis of this ill and understatement.

[00:03:39] You know, at least the ordinary 600 and is in the conventional framework that you have to use on classical computer, at least how it was formulated.

[00:03:56] There are a circular significant chaos in terms of day, night, accuracy versus D. So to get the perfect solution on classical computer is with that using conventional frameworks, respectively possible or you know, all at any old model.

[00:04:14] I think it is as fast as the entire time is here or at least until there's sun is done to, you know, definitely so fast for us exploring using computer kind.

[00:04:25] The algorithm is simply computer on this tiny so we have to go along on computers but that's not unexpected.

[00:04:32] It's how it works so people knew what I'll say with a cloud computer there are kind of other trade off so you can actually get to a near perfect solution.

[00:04:46] There are consequences of great off the make but it's no longer as fast as the only.

[00:04:52] So that means that there is full guy you don't know that it's possible to solve the life structure of complex materials like local or old materials are not felt on classes.

[00:05:05] You know, it breaks all day within a global day, they very little in holidays but we're always really been focusing on this basically very.

[00:05:15] The purpose of this is to build off the manulators just to do a form of research or calculations.

[00:05:21] And so that has really enabled us today's lead to near perfect simulations, very high accuracy for large monitors it does it still take the same amount I yes the slow calculation that runs for a week.

[00:05:37] So by you know we're able to basically outperform conventional methods in terms of accuracy precision of a grow it here design properties.

[00:05:47] Using the mathematics of how to work with your actual will still do the problem structure.

[00:05:53] How big are those molecules that you're seeing and in manage for.

[00:05:57] So the multis are about 70 atoms but it's not just about that it's like these molecules will have a metal center to do like a fifth row a little internet so that is to me it produces something like you know in a word of 70 electrons.

[00:06:14] And you know risk or introduce us so.

[00:06:18] It's not just that as some of the atoms it also has some pretty big atoms in there bringing a lot of electrons and those also create a lot of additional other complexities in the calculations itself like relativistic effects become important.

[00:06:34] And so these are the Hamiltonian and generally have a form of complicated.

[00:06:39] And you know these are problems that conventional methods sometimes struggle with but the quantum key methods you know they they're able to somehow through the room eight you know how they're exiting operations.

[00:06:51] They seem to handle these edge cases significant better.

[00:06:57] So can we dive a little deeper into that the the challenges that you have for designing materials for these applications and how you all are addressing these challenges like you mentioned already the center is a little bit different than just a general molecule so are you all.

[00:07:12] You know looking at parts of the molecule or what other challenges are you facing there.

[00:07:17] So in terms of the overall design of an old molecule be the real challenge is that unless you actually have a billion dollar or multi go your old foundry and it will into throw 30 million dollars a week at it.

[00:07:31] To testing materials, you have to be really confident that your material needs justification.

[00:07:37] That's one of the challenges of the old industry is that you know and materials not material in close task has to question both Asia.

[00:07:47] And to do mass production both Asians easily over every million all enough commits a massive company as an OLED foundry to do that test.

[00:07:57] That's the way one of the big challenges so the idea of using the quantum you know what the methods is really to make sure that the properties of dictate for we consider the size of the rules are going to be incredibly accurate.

[00:08:10] And so you know not only does it look like I focused on making very accurate on a chemistry simulations you'll be having higher.

[00:08:18] Not divisions departments dedicated to actually doing is hearing around poll and make actually equipment.

[00:08:26] You know, we have a pilot scale, all in manufacturing.

[00:08:30] A production line in the quantum.

[00:08:34] And so with that we can test things like how does the material decompose over a week he costly heated.

[00:08:43] And that's really that's kind of the two sides in that you know it's why.

[00:08:49] You know, accessible in this area is because we're able to approach the material design for kind of multiple directions.

[00:08:56] We're able to really accelerate and you know then present to our customers very accurate why would they so that they know and they have confidence that will be sent in material is going to work.

[00:09:08] So you mentioned that you're with these kinds of molecules you have to really pay attention to effects like relativistic corrections and so forth.

[00:09:20] So I'm wondering like you know there's been some work on the theory side for quantum simulations of chemistry and so forth.

[00:09:32] Are you are you having to sort of come up with new approaches to quantum algorithms and do you have a team that you can work with that that you help develop those.

[00:09:44] Yeah, so actually a lot of people on the materials don't reside there.

[00:09:48] They're dedicated for development in quantum algorithms. If that's O.K. I.S.

[00:09:52] I'm in the general or sort of answers to the key bit of cluster method.

[00:09:58] That myself was a very kind of long journey because others, you know, I mentioned that go 2018.

[00:10:06] But it started from you know opening up the you know started from downloading Fermi live, which is the over for the honest the open source can't live and start from Google.

[00:10:19] So we get to see the water and then sitting there and then it's if they ask them what is the you know where is the issue that water sold within two hours.

[00:10:30] And then we'll think of the entire circuit that prints out and saying wow that's just a lot of really big circuit and sassy part is getting smaller so you know we invented an entire algorithm for carrying quantum chemistry circuits that is still be theoretically most compact form.

[00:10:48] And it still has not been exceeded in terms of the generating the fewest number of quantum circuits of like fiscal feed operations.

[00:11:00] They won't be speaking at the nab of sex.

[00:11:04] They won't let me continue to develop when you know we tie on your methods of it or let's get a cluster involatorial in your college and which is a methodology for doing.

[00:11:16] The claims for the table is we've been basically set up guitar old as a major slow agent problem on a classical computer.

[00:11:23] So how to know seemingly circuits that have that set little quantum computer today.

[00:11:29] So that was similar to the class of computer and the key best of any matter of minutes.

[00:11:35] You know, we published all of this information so it's all publicly available for our memory.

[00:11:42] You know, we don't hire the quantum chemistry of that point especially not on the work that we do.

[00:11:47] We're very proud of the very college scientists and have worked in software to pursue come up with very very similar fields and very hard ways of programming around basis date.

[00:12:01] Yeah, I was going to ask about that you said 200 qubits simulators.

[00:12:06] Yeah, that's our first thing I would love to repeat this time to go right.

[00:12:11] Oh well and so it's internally it's you're not using any of the cloud systems that are out there.

[00:12:18] No we have some partnerships which I can disclose up this time those will come out soon though.

[00:12:25] Yeah, they're using file you know we're working together in terms of using the systems but we if we.

[00:12:35] I was pretty expensive just showing a while ago that can't go down right but the challenges that is only so many people we have and.

[00:12:45] Yeah well the if the use file instance is they go down like that calculation is done and it's expanded right and so it's not you can't continue the words.

[00:12:57] You know he will need for buying a tutor like node is expensive but it's not that expensive right.

[00:13:07] You can go through a lot more so that you can definitely.

[00:13:12] For a whole for your wallet was out of computing with a two-gerabyte Rang or.

[00:13:18] Definitely yeah I remember so so the Cirque simulator they say you can do about 30 I don't remember 32 or 35 qubits with about 8 gigs of RAM and then they say for every additional

[00:13:30] qubit that you add you have to double-dram so you guys are scaling not not that quickly when for each qubit which is why you know we'd always talked about.

[00:13:40] For a lot of the systems kind of 40 qubits this kind of the limit for a simulator right now for a personal use because you just can't.

[00:13:48] You're yeah you don't have the funds to to buy those systems and that much RAM.

[00:13:54] Yeah we also use a different approach than what Google uses we use another fire in O ring to that spells.

[00:14:03] Oh and to the four instead there are what else to do in it you have to you know it's not a clean oh and to the four there's a really awesome.

[00:14:14] So let us defend a little time for naming it is it's it's a very delicate operation with strangers of programming.

[00:14:23] People to spend more than a year asked for anything where I was like you do a circus and away simple kind of C and still smooth.

[00:14:35] Iron nature as there's smart intern to do it in an afternoon you know Google has the needs to do that.

[00:14:43] You went over that tree off where you know after 32 qubits as we said at a free dislocated tolls the rent and that's expected but we.

[00:14:54] We basically developed a new method to store a hand walk away in instead of in a massive a first and we use that for we almost about it.

[00:15:05] So it's not a huge price is just a title that I don't know what else really.

[00:15:10] Dops that method but how else would make your mid that it's not a universal simulator right or tonight execute a.

[00:15:19] We're a trans long right a basis of capitalization that was by for one down to the story really neat.

[00:15:27] Right and it's like if you find a solution market that uses it no one's going to care what method use along to get the results.

[00:15:35] Yeah that's basically what are you know our real customers tell us you know we often when we show results we just sent to talk to the confidential tennis set of various companies and they just care.

[00:15:48] Is this faster than the other CCS key is as fast as you are key and.

[00:15:54] If it is then they say okay we'll use it but they they don't even ask is this better than what I want to get it right.

[00:16:04] It's just Sarah is this better than what they can do now right but I mean all this started from the.

[00:16:10] So it's just a very serious issue of how on the work so it is technically still on the side.

[00:16:16] Definitely so you've been talking quite a bit about the processes and the backend that you have and the breakthroughs that you made there which are quite impressive so can you tell a little bit about any breakthroughs in the actual material discovery side of actually applying these tools that your team has achieved.

[00:16:31] Yeah I mean the you know the conductor cathode pattern material was invented using the video is failing methodology at least out to be as not sure exactly how much well.

[00:16:46] You know the conductor material it's not a good stuff so you can go buy or be solid it will buy it and I won't be buying it.

[00:16:56] That's it that's good but let's say but the the material the first generation of material was discovered in very late 2017 about six months.

[00:17:12] And then the next thing was just the other issues about that generation we call it the recent risk to the nation.

[00:17:19] But generation two as we go lot more and more accurate tools and I have take one we were able to basically develop and reflect each second generation of a conductor we fit up the green was and that's with technically only one that has been.

[00:17:45] So you can see how many settleration really happen and then generation three was developed within a whole so that we must have a little three cameras and now we all are kind of so fair closer responsibility role is the scale gosh you can show that the material is pure when it gets you down for us and it will working on the original for now.

[00:18:09] But you know I was in a nut gore and it is a is the copy example of the generation is a copy example of a material design problem right but the first generation.

[00:18:21] You know we had to shop that around a lot that's why it's like it was basically started each up to around the 2018.

[00:18:29] I would never see feedback from customers or suppliers into upper customers is like eight to generate the next generation.

[00:18:37] So I was like you know that's why that delay well in the display industry your right customers now spend $30 million they really want to trust you.

[00:18:51] You really have to earn that or step I think that's one of the issues that you know a lot of other startups they you know or the professors you know we see that see this a lot.

[00:19:02] That's what it's like you know I would have you know a little material on its life well yeah no clue whether that's going to go far enough right it could be a great material and it could do want property that tells it.

[00:19:15] And no one said it's kind of pretty money you got to assess it right and so for us you know when we send them that's actually they know it's not solid because they know we tested it isn't it.

[00:19:28] So they have a lot of confidence that that is the you know the second I'd say the third gyroscope and the rest is the angriest of almost fair enough this.

[00:19:38] I you know the generation one to design with the electronics such an delicious two.

[00:19:44] You can design chemical now with the electron structure package like kind of investing gains quantum dear company right on you just going to get you better answer slightly faster hopefully.

[00:20:01] Can you talk a little bit about how the this computational material design actually feeds into next generation consumer electronics and maybe even auto mail locations yeah that's kind of a.

[00:20:15] That's it's a fairly.

[00:20:19] Yeah that's great there can be a very long I can be a very short discussion or I mean a very long discussion we can get you all the intricacies of what the auto industry requires or one up but the idea is that generally quantum computer or those.

[00:20:35] So well if we're talking about that's high accuracy simulations right the birth of the high accuracy simulation is to reduce the in principle is to improve the confidence that material has to the properties then so I actually those and said this has a.

[00:20:53] Space is a confidence engine so you know not a mode of the standard that the material is that past is much is much stringent then for a iPhone display or that or has on his play or.

[00:21:09] Hey, it's true that because a car in on mode of is objective to far more time UV radiation it sits outside a lot and your phone you don't hold your phone up and you don't point it out the sun wave around for hours on end lose head you don't.

[00:21:30] But so now the material is even more into viewer is resistant well that's a problem you know UV visit calculation and the resist to you the best tradition can in model you seem to be going to get more easy if.

[00:21:43] By it is you know it has issues with some relations by or some of the accuracy you know if you want to try to change the design of the material like oh I want to.

[00:21:53] To leave the change out the you know the core conditions of this I have changed your directional 5G for this to so you end up having to have really get it like a lot of like a lot of a lot of academics these are the status related for a very nice little systems that show me over when you feel a story type home when reality there's kind of this.

[00:22:19] So where D.H.E. results are usually experimental results are actually used to see the T.H.E simulations to actually are going over the rest of the years to do this is why we aim for very kind of a lot of little simulations there we know methods like to get out of roster it does have to be a consistent so yeah all of our entries basically a task to problem is asking that no.

[00:22:45] In the treat dramatization of that address will allow us to work with our after you do this in your show.

[00:22:52] Which would allow us to add on a say the risk made a great faster a unique system which we help us develop our address for all instant movie distance.

[00:23:02] As a fact chain I was fine computing overall benefit industry the companies that know how to live in the series of South.

[00:23:11] It's just going to accelerate their processes right you can approach the material to stop it from both all different directions and as long as you do out here some of the great offer ever is right.

[00:23:23] It's still a like you have to think of materials of discovery as how to express it.

[00:23:28] How to come right not sure guarantee on the other C.D.R.P. you know, just stuff.

[00:23:36] I would just say that though your discovery is a business operation so you're going to see it from companies that are really important you know next models and combined the machine yorken.

[00:23:49] What the simulation excellent modeling and validation in actual and use basis those are the companies that can basically mimic a supply chain and entire manufacturing process and end user requirements are going to be able to utilize the problem here to generate like this much faster or in prove the number materials that you can need to go through that see a legit platform.

[00:24:17] Companies which just use a bunch of times to synthesize they will have very similar work flow as they can use the FT and if you can't use the FT design model.

[00:24:26] Here's how to do that.

[00:24:29] So are you seeing the rise of certain industries requiring or them reaching into this technology more than others or you've seen legacy systems like I'm seeing you know everyone asks like how is AI you know the rise of AI impacted your industries.

[00:24:45] Other you know applications of self driving cars and because they're ahead they're more able to embrace quantum computing or maybe the processes are more agile the like systems or you seeing the opposite where the legacy systems have those robust testing process in place where you can integrate the cloud into it.

[00:25:05] So I think the materials and terms through will be legacy systems there is a lot of safety.

[00:25:11] And there's some that I would say well I guess it's a certain post those secure requirements but they had very far ahead start in terms of like a self riding lot so drunken last a little bit difficult because a road is still very good at attaching around one glass from a request all of.

[00:25:31] And that's a very important role.

[00:25:34] The first of the way we're going to see AI and maybe probably came to also the tarot like robotics is just to be far more from left is in pharmaceuticals where I want to tell you this we can be done in a quiz.

[00:25:50] So one is the advantage that they have right so even before I worked at our D.I. you know I was studying a self-being distributed points of a row of liquid annuals that you decided to parents so even back in the 16th.

[00:26:05] I don't know why we in the pharmaceutical industry that was really the dawn of our leader to the sun and stuff and be a formulation was released.

[00:26:13] 2016.

[00:26:15] We had a 40 back and I'm certain that it to it and it is a part of the basic people following the NIST handbook for design experiments with a machine that could set up 96 experiments in peridote.

[00:26:28] And they still use those methods today because people understand it people get good data from it and it's incredibly well idle.

[00:26:37] It's now become a legacy sister in pharmaceuticals right and he has now we're going to need like a little eight built an entire facility in I believe San Diego California.

[00:26:49] Very impressive facility.

[00:26:51] It's more or less it's no longer about you know, it's exactly eight years old.

[00:26:55] But it's still that's the job done. You know maybe it's not a lot of it is as efficient as it could be given what we know now but it's still that's the job done for the cost of people respect it to.

[00:27:06] In it in old ads our chemistry is a lot earlier we have first solace we have a long reflux times we have heavy metals and heavy medicine heroes like fat lung every young.

[00:27:21] These are not safe or fall if that robot drops it while moving like and no one notices it sitting on the tennis tennis drops of fat they notice it is nothing.

[00:27:34] But for robot drops it and it does notice it and I know one's aware that someone comes in slits and now we have shots of glass.

[00:27:43] So is the risk factor in putting a more earlier chemistry or what we say like more industrial solvent based chemistry is a lot you know it is even more question and you know I wish we could use at least.

[00:28:00] I wish our chemistry worked in any solutions you know by metals you know thinking we're going to help chemistry is really we're coming in any case solutions.

[00:28:12] And when it is these incredibly slow and so we're just you know we're pounding on the reaction time of street by you know we are about to see we are going to see an acceleration.

[00:28:28] I just think that it is going to be a lot of legacy players who are just a little bit ahead of the curve they may not be the most enhanced in the past but they'll just be able to allow it to use our characters and how we can automate this process and now we can do you know house and work tests.

[00:28:46] And we've been validated for some more years here yesterday and I just got a separate the need for a candidate generation which is a optical process.

[00:28:56] And so that's where I think this is going to be a feedback who by especially to intervene in the kind of experimental site as it demands from work and kinds of candidates.

[00:29:07] And with these industries that you're targeting are you seeing the materials discovery what are the parameters are they optimizing for is it efficiency lifespan.

[00:29:18] What what a yeah for all legs specifically all ends into display industry there are about to mine for blue so it was big challenge even most perfectly possible if it's not by a couple of nanometers it's unacceptable and no one will like it.

[00:29:35] Big lock line for the white time right the Watson make sure that those blue meters last very long time.

[00:29:44] There was some younger into the chemistry cannot recently where they managed to you know narrow basically you know they've been able to take too many emissions after very precisely high tones and all chemistry around it so you know blue phosphorus and emitters still a huge area of reception development with the new lead industry.

[00:30:07] And then we get within display in general for all that's the also see you like new digital space.

[00:30:14] So new staff in combinations and cereals new circuit design within those all its stats is really fun as well.

[00:30:25] The question really thinking is more of a feature is what do you think are some potential shifts in the electronics and photonics industries as results of advancements and materials design.

[00:30:40] Well really advance materials really enable and they open up by why range in which of the possibilities.

[00:30:48] I don't classify myself as a futurist.

[00:30:52] I'm technically not as I'm not super optimistic was about the future.

[00:30:57] I can be more for I'm saying we're realistic in the now.

[00:31:02] But at least in terms of friends were seeing trans to ask a place right people companies are making various types of testers laser duty.

[00:31:11] Yeah and we're all trying to date how a summer will react to it right so you know heads up this plays in the automotive sector might you know I wouldn't say it's three or three two years away.

[00:31:25] Or like it's definitely will that in terms of a you know viable application by with display is only the material site is the need to factor sign on this human interaction side right.

[00:31:39] And why it's important that companies you know jammer 80s initial prototypes even if the prototype is like you know costs similar cost and all some me.

[00:31:49] So let's say our the test here computer wire even if cost 100,000 was made is an important administration is really need to start an age how customers and consumers actually use that.

[00:32:02] Whether they would actually be accepting of it right it does she actually make that massive produce that's really expensive it's not 30 million dollars is far more sensitive to make a stay out that will be affordable to a senior.

[00:32:16] So really a lot in his you know things that we see in us somebody shows them like boss data sort of your own computer felt those are really important.

[00:32:26] I said people actually may and that's where you can kind of just but I would say that.

[00:32:32] 80% of them are going to have a you know and long fail because a consumer reaction interaction with the person.

[00:32:42] But if they don't make them were never going to learn.

[00:32:46] And that's really what we're what people are in the display.

[00:32:49] I have had the experience using the other shows is really each for someone and whether people watching you these to us display is a very intimate you know,

[00:32:57] between an intimate experience right it's one of our main visual processing you know it's a visual processing.

[00:33:05] And so it's a huge part of our lives to change how this place radaping change this voice is directly why they're why because people need to start an occupable but I would say you know that to that I think are,

[00:33:19] you know, one that I think is definitely on a horizon is there's going to be more apple face ID type technology with infrared.

[00:33:28] And so we're going to see that and I agree on this place of that is what I want.

[00:33:33] And there is the underpiled tenders those are probably too close this technological changes in display and from a you know I never really see that first probably within the phones.

[00:33:46] And then we expect to see that actually with the moat house.

[00:33:50] They're already prototypes out there and none of them are really on the other three university at all by you know the industry is there's a lot of interest from the end user.

[00:34:00] And for the panic is actually this happens to be thing up it is just a call us around this making this a fire goal and product.

[00:34:09] So there's a lot of kind of work still to be done in the quantum computing space so what milestones are you looking for that would accelerate your work.

[00:34:19] The reality of the of the gay operations.

[00:34:24] What is it I don't want for exact names and just hit them exactly want but there was especially sort of a dozen.

[00:34:32] She did quantum computer.

[00:34:34] And that's already way that's you we were in hastin to do so actually for care to see some relations.

[00:34:41] But those students have to be your guys right there's a little room for error in these things that quantum is recalation is.

[00:34:51] Yeah, it's actually please tell me fractions of process that comes accuracy.

[00:34:58] And so you know these did all these it's no longer like well I need to see the fault all right quantum computer or any you know robust error correction and we need perfect quantum computer.

[00:35:10] When we have a perfect quantum computer that has the delities that have a erase or like you know, the delities that are like you know 99.9.

[00:35:19] Richie for my another more decimal places then we have a one of the real work for chemistry up until when you know it's just the accuracy requirement that quantum computer is.

[00:35:33] So it takes a lot of people or not on the field.

[00:35:38] Are you seeing any certain types of quantum hardware getting closer for your purposes?

[00:35:46] Yes, we are seeing some like a kind of making a logical few bit out of the.

[00:35:57] I have a boson of both.

[00:35:59] I didn't say this is still quite a difficult operation by the audio viewers out of the quantum opportunity itself a logical unit shows very hastily.

[00:36:07] That's how it comprises the price of these goals on those but also that right there.

[00:36:13] That got our conjecture these got come make away costs.

[00:36:18] I mean no more forticus working on them, which is a Canadian startup.

[00:36:24] Come keep it in sure but back but I also believe that and fairly certain questions and we look for a moment design or a potential.

[00:36:36] That hardware it actually does have immediate application on the chemistry of the veterans control.

[00:36:42] And so there's actually a very good mac a from towel that machine was intrinsically forget the logical to the portion but it's actually not prepared for a.

[00:36:52] Or a specter confolutions and that's a component of my country's whole thing, but it is a component so it's like well you can at least that uncomfortably shouldn't be able to release the same of a newbie this after population.

[00:37:07] That's pretty impressive on this up.

[00:37:10] But if they these very high precision you know, evening on any units, the not to 50 to this are all fully connected and had near perfect fidelity fully or corrected that again up to really decide to real.

[00:37:24] Quite fish up in deciding to use.

[00:37:28] So I look at those very high stability.

[00:37:32] It was like just super conducting.

[00:37:36] Haven't seen the progress in the fidelity and I think it's that it's on a stage trajectory.

[00:37:43] The ground fire traps at the hour to slow.

[00:37:48] So you know, then some hours it's Microsoft were saying that it will you know it's not the 200 years to solve that although using an ion trap with the best possible.

[00:37:59] That's a long time.

[00:38:02] I think we could just make that material instead of test it right and so you know we.

[00:38:08] And how to use was those for random old side goes on curious right now.

[00:38:13] But I see anyone who is a cloud of the vendor, he said it's not just your clock appear on more than happy to chat more.

[00:38:21] Show me that show me day about on made plot of fear and I will definitely send some grace for you to simply.

[00:38:29] Yeah, it's great that we have a race because it's really pushing the different architecture stuff their game.

[00:38:36] Just turning over a little bit about your personal involvements in the company.

[00:38:42] With giving your background in chemical engineering and chemistry, how is your approach to materials discovery evolved well at OTI luminolids?

[00:38:52] I say that when you first saw in pharmaceuticals because there's I think it does the NN is a lot better.

[00:38:59] The end goal is much better.

[00:39:02] Like we are going to make a draw that targets this protein right and we know if we do this, we have a thing that goes into phase zero clinical trials and then possibly phase one.

[00:39:17] And then I will let's watch the digital and the fear.

[00:39:22] Maybe I'll sit down.

[00:39:25] It can pharmaceuticals and we have a.

[00:39:29] The end of this one is better to find that we want to design a small role, we want to design a peptide that will interfere in this known.

[00:39:42] And they know that if they are able to achieve that, you can you know it in case had it off the dental trials and I your good God was done right and that I survived a company.

[00:39:56] My cover my new job is to just design truly on a whole that the last clinical term.

[00:40:02] I had you know still any presentable fail.

[00:40:05] But that just really you know that's actually not that.

[00:40:08] This is just the same things to see you get and I think it I had this kind of.

[00:40:15] Nostra round forward particularly with toxicity.

[00:40:18] There's X 90 notices of one on toxicity results so in terms of understanding how tenables of toxicity and mice rats.

[00:40:27] Yeah, it's on a no standing doll.

[00:40:30] And in really sorry about the E or screen.

[00:40:34] All those that will probably fail that and the thing about it in a drug is that it's a very high markup.

[00:40:48] And then it like I said, it's kind of like the end is kind of better understood right the risks and rewards.

[00:40:56] But in all as that's not the case.

[00:41:01] In all as the end game is very murky and people don't really know what's going on but they know we have to meet by a very, very high cure standard for how.

[00:41:13] Like these chemicals.

[00:41:16] What made it feels most old like top that is just what we need to get easily right as it has been focused on this actually.

[00:41:23] It was actually talking the tools and equipment to validate and all they built with certain handles.

[00:41:31] There are a bunch of companies especially ones that spit out of universities.

[00:41:35] You know, professor this literature.

[00:41:37] They're going to do a conflict.

[00:41:39] If I have a time then they are like the correct words.

[00:41:43] It basically deals very quickly and then the company goals is that's a very common story.

[00:41:50] That's very common.

[00:41:52] The answer to say as less than 10% success rate where like even getting into that $30 million.

[00:41:58] I just with us.

[00:42:00] There was a guy has done local financial rates so we easily clear the hardest part.

[00:42:06] So I've been really focusing on you know this from to discovery is kind of actually approaching a family that's two sides right looking at me found it actually solidify what is the end goal.

[00:42:18] Right and is this the serious something that can be set up a manufacturer and chemical trust.

[00:42:24] So when I come up with an area background.

[00:42:27] You know, it helps us out still understand how I can't work.

[00:42:32] And how I suppose because I know all the models.

[00:42:36] I had many way to improve all the models to design those things.

[00:42:39] You know, I was trained on how to use his and super pro which is the worst thing for a software.

[00:42:48] Not very helpful at all.

[00:42:50] But super pro is a process tolerance of re can be used to the other side how we can work.

[00:42:56] But I was understanding from the front act right like how do you go about screening a second.

[00:43:01] It's not a person for in general is the hands right and so one of the cheese fairly stuff that the thunds is off to all day.

[00:43:09] Yeah, I'm just I think it's those counter to what an AI can I'm set to do.

[00:43:13] We are calling it the railroad that we're basically actually use the very hands to build a track on this.

[00:43:20] We then send the J. I. and computational chemistry platforms down right so this is really the template.

[00:43:27] The lent bench tennis.

[00:43:29] They say these are really don't easily actions we can actually do and they are trying to do very well.

[00:43:37] In our case you know a tiny amount of incursions can just other than lead that contaminate actually gay.

[00:43:44] Sorry, they thought material has some incursions it can be like oh was it three of the fails in testing but it's I can.

[00:43:50] Can actually work it was actually through a fun part of me so really skill of canvas which is why we hire exceptionally talented candidates.

[00:43:58] There's some other things that we should spend a lot of time making sure it kind of so very well trained and incredibly well targeted.

[00:44:06] Exceptional tennis at O. T. I.

[00:44:09] Or a less than the Canada and they you know manual lead by the computational aim on these are the reactions we can actually execute very well.

[00:44:21] And you know that if something was wrong in this reaction we will know how to fix it in a way.

[00:44:27] And the matrix he cut it all out upon which then we build this you know the conversation pool was going to kind of send down.

[00:44:35] Well itself.

[00:44:38] So that's really kind of actually using the end understanding the life cycle of a chemical using the end keys first.

[00:44:47] And then we build actually initial in a track that the whole system runs down and nothing you know for us to throw it out.

[00:44:55] But I think that kind of is a bit of the after that I said or that sounds like I believe it's not a little bit on but when I say I'm meeting on the CEO for other so that he's.

[00:45:08] It's wrong.

[00:45:11] I didn't hear from me with the website.

[00:45:16] I didn't know me you know me a lot of the tools, a lot of processes.

[00:45:22] It's really or in which you actually have to take these and how do you have it for for feedback who really makes these methods on is other.

[00:45:31] The final we always you know because we understand where we're going so well.

[00:45:36] We don't really think about too much so how you know this chemistry this is making anyways in our field it would be like oh this is three chemistry sets that's two minutes.

[00:45:50] That's too many that's just someone has to do.

[00:45:54] We're not doing right and that's clearly because we know a set if we on a build a chemical plant that had to do 70 chemical reactions it would be on.

[00:46:04] And that's really a huge motivator in terms of life well I'm just not going to say this is all material is that.

[00:46:13] I know set all yeah one thing you mentioned is you know training up your scientist or chemists really well.

[00:46:19] And one of the reasons we started this podcast was really because you know one of the issues with quantum is also that education aspect right a big problem that I saw with early stage quantum startups is they go company and go.

[00:46:31] Give me a problem I can solve with the quantum computer and they go I don't they don't have that intuition of what problems can be used on quantum computer right.

[00:46:41] So what have you done what kind of innovation and training have you done in leading your teams towards building these solutions in material discovery.

[00:46:52] Oh that's a very good question we can talk about it from the answer or action perspective where you talk about thing the internal trade perspective in terms of internal training is pretty much been like you know we become with the thing and be like well I don't really how to method that can predict this.

[00:47:10] Property and material okay well less sour the literature let's figure out what is appropriate cool that we actually go into solfice right.

[00:47:21] And then you made that tool and then you kind of try to fit it into a flow pattern and you're like well this tool you know it can all process 20.

[00:47:30] You know see the time materials for gay whereas the other one is really too loud therefore the longest 100 concerns because it's serious.

[00:47:38] They really understand and feeding that you know the trading process for educating people on that is.

[00:47:48] You know I would say like when one of us internally is we do stand yeah I whenever we hired people I always tell them you know we expect that we're going to spend a year training you.

[00:48:01] Right I would actually you and how give a couple cluster theory works we're trying to train you in the last level of dynamics like what it's the first thing.

[00:48:11] Which is kind of ours is very specific so most be more trained more than X to build the type of system ones that you actually want to explore in our case so least time training there.

[00:48:22] These 90 that time training them I be of fundamentals particularly is toss lessons.

[00:48:27] I can speak to our credit that we're interested in which is the color that a new show a little yes it is toss lesson later.

[00:48:35] We trade it on this simulation for a moment you want to know that is.

[00:48:41] And these are the basis sets that you want to run if they have to enter and this or the other is.

[00:48:48] You know is it makes a means and a lot of traffic for you people on this and you can put a muscle and age here with the quantum use to say.

[00:48:56] We have been a little bit.

[00:49:01] I would actually say that early days in kind of less like oh ready you can stimulate this material but like still a lot.

[00:49:09] Right whenever you talk to the actual competition carol city like well you know let's pack you.

[00:49:16] Cool.

[00:49:19] And now we're done it another like well whatever we actually like did a material design campaign where we simulated a bunch of things and I'm going to generate the correlation block for it.

[00:49:30] So people they could use the results actually forecast what is through this and then what oh now I understand what you're trying to do right and so often you actually have to need a lot for it.

[00:50:13] And you know come back when you actually have shown that this simulation previous this experimental property and use each there and a result to confirm it then they go oh now I see.

[00:50:29] I it's like you know what I believe.

[00:50:33] The job said is that people don't know what they want please.

[00:50:38] And so you have to really show them some sort of test case that is concrete and rigid and very well done before any allow now I see right now like until you were going south to where I want I need to take this to that but until you show them there like oh that's cute.

[00:50:57] Oh nice nice scientific.

[00:51:00] I think you've touched on something that's actually an issue in all of quantum technologies including sensing and communications.

[00:51:12] There are these in principle things that are exciting particularly from the academic perspective but until you see a real context where there's something some some new capability becomes out of it so the use case is not so obvious.

[00:51:29] Yeah that's that's one of the you know one of them in derivatives right is.

[00:51:34] You know it's I still think that I haven't provided very valuable especially in the quantum these are there send up the charity quality ideas and you know it's just that like.

[00:51:49] The goal of the academic is you know the public public public public public public.

[00:51:59] The social stuff is not a kind of the security and it is an isn't operation because the material doesn't exist until it's in products right until the seniors have it in their hand and they don't know about it.

[00:52:14] The social system is not only that it's in the block because it's a paper right and that is you know and that's not a that doesn't product and consumers and doesn't get good paper because that takes more whole years.

[00:52:27] You know they heard because you have to be harsh about it this interview manufacturer does not want anyone to know and until the door will then and that was the early papers so just this this is possible.

[00:52:40] It's going to start is you know surely a business pursuit because the incentives there align was happening at the goal by the at least in quantum computing is current stage.

[00:52:53] There's a pretty decent alignment is the technology is advancing very quickly is very interesting is producing along really good pay so that's just surprising how.

[00:53:03] We can't double is really anything of our children material we can't publish anything about our work.

[00:53:10] I can't really want to tell you those are like the most such a stand I can tell you what you know how we you know a second general regular I can tell you every little stuff of it but my point of view so we can talk about all the intrinsic theories and all the in kind of a face and it's like oh this is okay so it's like indeed.

[00:53:30] The talk is actually very I think in the long run it's the position because there's such great industry have a government alignment he tried to get the technology to be very vital my biggest year of how it is that it will not be the fish.

[00:53:50] It's double right now we're deliver now I think people are in for the long haul and how couple of CBI of all I think is you know the AI journey is pretty is three.

[00:54:04] There is about a lot of stuff on top and it can be very successful I didn't think that you know 10 20 years in that quantum computing will be turning on low but I would actually.

[00:54:17] But that will be very rare because there's a watch academic you just look a lot like a really honest father she stayed ahead yeah so just finishing up I don't need upcoming projects or collaborations that you could talk about that you're particularly excited about.

[00:54:40] I mean we have a collaboration with more quantum to do doing that like it's doctor calculations on their words you know it was great actually fun and manage and so and then it's funny to the collaboration before.

[00:54:55] So we're very excited about it because it one of the challenges that we always faced is that unless you have really deep access to the hardware itself from the user's standpoint white will results that are already system aside from the area.

[00:55:13] The results are going to be I don't people like to complain about doing a lot I look a little bit about it is everything how long those in and it's just call out.

[00:55:52] This is probably a less appreciated aspects of quantum chemistry so we're excited to see what you know about.

[00:56:00] Awesome well thank you Scott so much for your insights and your time coming on this podcast so where can we find more info about OTI about the work that you're doing and where people can ask more questions about it.

[00:56:13] I mean our website is a great place to post all of our academic papers that are really important to you.

[00:56:21] You can see our corporate line that conducts or on what is our achieve and so this is some of the human desire.

[00:56:29] So what we're going to do is.

[00:56:32] These are the best part of the best space you can also catch the generally a lot of references either myself, my family under or was then.

[00:56:42] We're really active I think.

[00:56:45] This idea is daily just calling out in May so it.

[00:56:49] The oldest of the bypasser and be there you probably run into a little bit of the letters has been there.

[00:56:56] I would be at action key in a new for this year and so if I'm sure that's why we're more people who listen to your podcast one dot.

[00:57:05] But it can't solve so a place there you know this chance to talk about from OTI.

[00:57:10] Well you learn more but I'd say out of what site you're very interested in about our on competing applications and the party is that they have to push so.

[00:57:19] They're all there for anyone to read so I start there and if you have any other questions go with them.

[00:57:25] Perfect and for listeners thank you so much for listening if you are on YouTube all these links will be in the description below.

[00:57:33] And if you have any comments make sure to comment down there and always you can listen to this podcast wherever you find your podcasts Spotify, Apple and thank you so much again Scott for your time and Gavin for co hosting with me today.

[00:57:46] Thanks.

[00:57:47] Thank you so much.