Technology, Connected - The Quantum Chip That Looks Like Silicon

Episode Date: March 19, 2026

Conductor Quantum founder Brandon Severin joins Thinking on Paper to explain Google’s latest quantum breakthrough, the race to scale beyond today’s experimental systems, and why the future of computing may depend on controlling individual electrons. From spin qubits and trapped ions to semiconductor manufacturing, AI driven quantum control, drug discovery, and cryptography, the conversation maps the emerging architecture of the quantum industry.Have fun with this one. We did. --Brandon Severin: https://www.conductorquantum.com/--⁠⁠Listen to every podcast⁠⁠Follow us on⁠ ⁠Instagram⁠⁠Follow us on⁠ ⁠X⁠⁠Follow Mark on⁠ ⁠LinkedIn⁠⁠Follow Jeremy on⁠ ⁠LinkedIn⁠⁠Read our⁠ ⁠Substack⁠⁠Email: ⁠[e-mail]⁠--Timestamps(00:00) Introduction: spin qubits and the quantum scaling problem(03:47) Trapped ions vs spin qubits: fidelity, coherence, and tradeoffs(06:14) What qubit fidelity means and why it determines scaling limits(08:25) What is a spin qubit? Building from the transistor up(11:06) Semiconductor fabrication as quantum computing's manufacturing advantage(15:00) The quantum circus: superposition, measurement, Schrödinger's cat(17:17) Shuttling qubits — moving electrons across a chip(20:33) How AI automates quantum calibration (the control problem)(25:00) Quantum scaling vs AI scaling: the GPU parallel(29:08) Quantum startup culture and the AI generation gap(32:59) Building for a million qubits — rocket ships vs ladders(36:52) Why quantum is taking so long: talent, concentration, and meaning(39:43) What seems impossible now that will be routine in 20 years

Roll up, roll up. The Quantum Circus is in town. And today the ringleader is Brandon Severin, CEO and co-founder of Conductor Quantum, a Y-combinator-backed startup with one core bet. That spin-cubits in silicon can scale quantum computing faster than rival approaches. Stick with us today and you'll learn the difference between trapped irons and spin cubits. What's the difference and why it matters? We'll look at the existing semiconductor manufacturing industry and why that could be the key to scaling quantum computers. We'll break down AI and the automation and calibration problems that it's being used for to speed up quantum to find those algorithms, to tweak those algorithms to make them useful.

Finally, we'll get into the culture of the quantum industry and how quantum computers can help us all think clearer. Brandon is an unusually gifted explainer of quantum computing. Maybe that's his age, maybe that's his verb, maybe that's his passion for. for the subject. Either way, he uses vivid analogies throughout. It's perhaps one of the most insightful but also entertaining conversations we've ever had on quantum computing. Please enjoy the show. The bearish case for quantum, to be honest, is actually the lack of algorithms. You know, it's okay, I give you a million cubits today. How will you use them? You know, like no one really knows. But we're already seeing that. Okay, as we get more

cubits, you can develop better algorithms and test them and see how they work. What's the bottleneck in creating algorithms, quantum algorithms? Nature. Are they, oh, are they discovered? You discover an algorithm. Maybe you don't create an algorithm. You simply find, you discover where it exists in nature. Yeah, pretty much.

Is that a stupid comment? No, not really, because you're effectively trying, it's this kind of going back to this, you have this weird limitation of you want to simulate nature, but you also only have so many 2 bits. So then you kind of want to develop your algorithm for the computer you have access to, because then you can also verify that it works. The Google team put out this whole thing about that this is the first quantum verifiable algorithm. It's kind of true. They just get a number back, which means that, you know, if you had a quantum computer, you could run their algorithm, check the Google result. And actually, if, you know, nature itself could run the Google.

Google content algorithm and check the result. That's a beautiful thing about it. When you think of us, that's the level we're playing at with quantum computing. Let's talk about spin and let's pick like trapped ion. So let's look at spin and trapped ion. Can you break down both of those two and tell me about the difference between one and the other? So let's start with trapped ions. So they're probably one of the few cubits that actually just in when you hear and they describe what they actually are.

where you have literal ions, so calcium ions, cesium ions, whatever, well, actually that's more of the neutral item, but calcium ions. And you trap them in space. You have a column of ions trapped. How do you do that? Physical lasers, you literally, you know, use this laser cooling effect where you have ions floating

and you shine a very well, you know, focus laser on them and you trap them within that laser beam. Kind of like something out of style was like, you know, the force. And you effectively start to build, you know, I kind of describe it as like a Newton's cradle, but with physical ions. And the beautiful thing about ions is they are ions, you know, once you've trapped them, they have their kind of this well that they sit in and you can operate on them and the beautiful thing there is well-controlled, create environment. What's known as the key metric in terms of better cubits is very high fidelity, meaning that

when you tell your cubit or your ion to be in state one, it is in state one, you know, 99,000 times out of 100,000. Whereas other cubits don't necessarily have. such high fidelity, but they'll have maybe other benefits like long coherence times, meaning your cubit stays in this quantum state for a very long time. If I could just unpack fidelity a little bit, is that kind of a ratio or the combination of gate speed and coherence time together or no? It's its own thing. Okay.

It's almost, you're literally measuring the reliability of your cubit. Of it being in this state or this state. precisely or when you do an op and then that there's almost like more layers of fidelity so I perform an operation on my cubit and I expect this answer is it correct how many times you know out of a hundred is it correct basically and that could be let's say your single cubic gate fidelity and then you pair two cubits and you do a two cubit gain you get a two cubit got it got it got Getting down the rabbit hole here. Does the fidelity decrease as the more cubits you have?

Good question. From what we're seeing, yeah. So single cubic gate fidelity is typically always higher than two cubic gate fidelities. I think it's just by nature of the system, right? Probabilistically, it's harder to do a two cubic gate more reliably than a single cubic gate. And by that we mean like you're pulsing on one cubit,

you know, you're let's say rotating it from zero to one. And then you're doing the same thing on the other cubit and you're measuring out the combined state of those two. And there's just more things that can go wrong, right, as you add on that extra cubit. But, you know, with good quality single and two cubic gates, you can build a quantum computer. So once you've met a good threshold,

then you can argue okay with X number of cubits and this level of fidelity, I'm good to go. You know, we've ticked the boxes. But the more cubits you have gets difficult to manage. So then you need extra cubits to correct for these errors, basically of your cubit that you want to use for logical operations. That's the more qubit versus better cubit. Having one to back up the one that doesn't behave properly, right?

Exactly. And it's not just one you need to back up. It's like hundreds, if not thousands of cubits. you know, that you need for one logical qubit, you need like a thousand error correcting cubits. Thanks for diving into that stuff with us. Back to the trap, ions. You've got this thing suspended in a, I guess, a vacuum is kind of a, yeah, suspended in

the vacuum trap. Yeah. It's a little vacuum trap. It's kind of like Ghostbusters when they suck in the ghost. It's an ion trap. It's probably similar to that tech, right? Ghostbusters were quantum? Look, if that works, 100%.

And, yeah, you try it. You have it in this trap, like a Newton's cradle, then you fire different electromagnetic waves at it. So whether it's laser light or whatever your ion of your choosing depends on basically the frequency or wavelength of light you shine on. So red, blue, green, whatever. And that excites your eye and that excites basically the electrons in that eye into different levels. Einstein going back to the OG of quantum mechanics again is when that, that ion loses its energy, it emits light. And then you can measure that light.

The hard part is measuring the light because that light's going to come out as an individual frequency. It could get absorbed by anything in your huge vacuum. You need to collect it. You have all these different like efficiencies of detection. You need to collect the light. It needs to transmit down a fiber.

And then you need to be able to actually detect it with a photo detector. That being said, the iron trap teams that are working on this, oxydionics, now acquired by ion Q, contium, they're great at doing this stuff. You know, like really, really good. They have some of the highest cubit fidelitys in the world. Let's start talking about spin. There's like a silicon layer, and we're literally poking holes in the silicon layer and trying to convince electrons to jump in those holes and stay there. Like, let's talk me through that, man.

Yeah, what is a spin cubit, I think is the question. There's two ways to approach this. There's the very bottom, bottom up way where you start with the physical spin cubit, or there's a top down way, which is you start with a silicon chip. Let's start with the bottom up way, because that's the fundamental question. What is the spin cubit? So a spin cubit is an electron orientated in a magnetic field. And you can view that electron similar to this NMR nuclear magnetic resonance.

that electron behaves as like a tiny bar magnet. And that bar magnet, similar to the magnets or a compass needle, you know, on planet Earth, it wants to align with the magnetic field, right? Your compass needle wants to point most. Kind of similar feel with your electron. You needn either wants to be spin pointing up or spin pointing down and up, let's say, aligned with the magnetic field or down against it. And those are your two quantum states.

Those are your two bits. Spin up, spin down. Zero, one. Now the hard part is, well, okay, great. And I think, like, arguably, you could say this is probably, like, one of the nature's most fundamental cubit. You can't get smaller than an electron spin cubic. Then the hard part is, okay, how do I isolate individual electron? Good luck.

And that's where the silicon comes in. That's where we come in as a company, which is you look at the transistors on your desk, the transistors in your phone. Those are basically tiny, tiny switches. You've got like 50 billion transistors in your pocket right now that allow you to control billions of electrons flowing across them per second. You know, and that's on or off. The current's flowing, the electrons are flowing,

the transistors on, current's not flowing, electrons stop flowing, off. And it's like, okay, can I turn that into a quantum bit? I got billions of electrons flowing, but I only need one electron. And the genius part of spin cubits is if I just slightly modify my transistors, to give me a bit more control over the flow of electrons.

And still keep the same semiconductor process that prints trillions of these transistors every day. But just say, look, let's just kind of tune this transistor. Kind of like fast and furious, you tune up a car, add a bit of a Noss or whatever. Yeah, but very small, very small. Can I start isolating individual electrons? The answer is...

My cubit. Yeah, literally. The answer is literally, yeah, you can. And the trick is, is basically, a transistor works on having what's known as a gate electrode. It gates the flow of electrons going from one side of the transistor to the other. If you add a few more gates, you can basically start to isolate electrons between these gates. It's all between, physically underneath these gates. All of these gates, it's all controlled by standard voltages, classical electronics.

So no lasers, no, no vacuum. Same way your electronics work now. And if you finally tune the voltages you apply to those gates, you can isolate an electron. You can make it jump to the left, jump to the right. You can have it sit there for, you know, what seems like an eternity in the quantum world. In reality, it's not that long. I mean, we're talking like milliseconds. But because going back to, you know, your point on operation speed,

you can get that electron to flip between state zero and one on the order of nanoseconds. Your gate speed is on the other nanoseconds. So if you have milliseconds of your electron living in this quantum world that you've built for it, you know, you can do thousands and thousands of operations within that time. And that's just thinking an existing chip and using what's happening on the transistors and going down to the quantum level and controlling the electrons which are already passing in their billions through these gates. Exactly that. So it was like, I think the late 2000s, you know, it's like, oh, we reach peak semiconductor, you know, the node size can't get any smaller.

The transistors are so small. You used to say like electrons could tunnel from one side to the other. We're like, that's not a bug. That's a feature. Great. Well, that's the next step of evolution, technological evolution. Yeah, it's incredible.

So the Google algorithm, you said if nature could run that algorithm, any quantum computer can run that algorithm. So if you have a trapped ion quantum computer is superconducting or one of your spin cubits, it can run that algorithm. So there's no algorithms are qubit agnostic. They can run on any system.

Ideally, yes. I mean, you know, there are certain, algorithms that will pertain themselves better to certain systems. Like it's just more easy to visualize and physically understand how that interact with the underlying hardware. But that's the whole point of these algorithms, is that, you know, regardless of your quantum sort of basis set or modality, you know, they work because algorithms are effectively gates, you know, the same way that your, the algorithm, let's say, we run on a laptop should also work on a quantum, or sorry, a class. classical computer made out of whole punch cards or buckets of water. You know, it's just operations on zeros and months, effectively. Whenever we talk to a quantum guest, I always have this, have this picture in my mind of, like, a ringleader taming a lion under the big top.

The lion's the cubit, and we're trying to, like, tame the lion in the corner. And then, like, the split second, the lion's tamed. Mark's got to come over and whisper a calculation in its head or a data set for it to rip on. Like, is it, yeah, that, does that, a quantum circus, Jeremy. Oh, there it is. A quantum circus. Trademark, you heard it first on thinking on paper, the quantum circus. Okay, in the quantum circus where you have all of the different systems entertaining the crowd,

what can you do with a spin cubit that you can't do with other cubits? Ooh. The big thing for SpinCubits is that the manufacturing chains are already in place. And it's not necessarily what we can do with them, right? That's not, you know, that's not answering the question, but it gives you an idea of the sense of scale that SpinCubits can approach without having to like kind of reinvent, you know, a whole new manufacturing paradigm. because we are really trying to leverage a trillion dollar industry, a multi-trillion dollar industry that's been perfected by humankind for the past 50 years. When you think about, you know, when I think about like the most impressive technology ever, it's definitely just what we can do with, you know, an EUV machine, you know, or whatever you use, basically, but to pattern these silicon wafers into transistors

and, you know, push humanity forward in ways we haven't seen before. Span cubits are unique in that. Photonics come close, and you can also make those an industrial fab, but you kind of need to really change the recipe. Physically what makes a spin cubit unique is you can move them. So there's a lot of work in the spin cubit community now

where people are literally figuring out how to what's known as shuttle, move these cubits across. So we talked about isolating an electric. on, you know, in your transistor. But let's say you want to like cubit here to talk to cubit here on the other side of the chip. How do you do that?

Like, do you use some quantum entanglement at a distance sort of effect? It's too far for that to happen. You actually can move, shuttle these cubits across the chip, get them to chat, move them back. Get them to chat. So the lion tamer thing is real. The lion tamer thing is real. And the amazing thing is it's such a, you've picked the. perfect animal, which is it's basically part of the cat family.

And you can go all the way back to Schrodinger's cat, right? You know, you're born natural. And that's effectively what a quantum computer is. You've got Schrodinger's cat, which is this, you know, old thought experiment of what a cube is and measurement and collapse and whatever. But you've got a cat in a box, is it alive or dead? Well, quantum mechanics basically says once you open the box, you know, the cat used to be in superposition, i.e. being both alive and dead or neither nor at the same time. But once you open the box,

you measure it and it collapses into one of those states in being either alive or dead. With a quantum computer, you're basically trying to keep the box closed while you manipulate the cat into the likelihood of being alive or dead when you open it without killing. cap or keeping it alive. So literally exactly your line tamer circus is very actor. The biggest hump for me, hurdle for me to overcome in understanding all of this when I first got into it was how literal I took the observation. You picture someone standing there in a lab coat with a clipboard observing and nothing quantum can happen until this observation takes place.

And then who's observing the observer, observing the observer? Like, how do you talk someone out of that theoretical place into, let's land the plane and get tangible with it? Oh, it's tough. I mean, because it's hard, right? Yeah, because the deeper you think about it, you know, it's very philosophical. And I mean, there's still two camps of interpretation of this, right? There's known as what's the many worlds theory.

and there's the Copenhagen interpretation. And even that's not necessarily just the idea of the observer, but it's related. And, you know, there's this stroke. A great way of understanding the difference between the two is the stroke. It's just like, if I'm in Copenhagen and you see me there, that means I'm there. Right. Whereas in many worlds theory says, well, I could be in Copenhagen.

I could be somewhere else in a different universe. You're just not part of that universe. what you believe is up to you. I prefer the many worlds. I think it's a lot more, a much more interesting way to live your life, knowing that in some other universe, your lion could be still alive or dead,

depending on what world you want to live in. Look, the sort of ceremonies go on and on and on and some have actually never been solved. You know, Wigner's friend and something like that. If we're going to continue the quantum circus analogy and wrangling these cats. How are you wrangling the cats? So you're doing something different, aren't you?

Because you're using artificial intelligence to wrangle the cats, to wrangle the cubits, to control the cubits. Is that correct? Can you bring artificial intelligence into this conversation and how you're using it? So this cat is an annoying cat.

We're just trying to get the cat into the cage to start with. When we think about our billions of electrons, it's like, okay, well, I need to isolate one. So basically I need to kind of form my cubit. I need to get my cat into the circus. And that's where we're coming right from the beginning. And so the AI comes in where it's okay, I'm applying these voltages to isolate my electron or puddles of electrons, you know, let's say 10 or so then I can slowly deplete that puddle so one's left. And that's a really hard thing to do. But the joke is

What you wouldn't believe is you learn how to do this as part of your PhD if you work in spin qubits. You learn how to manipulate those voltages, let's say five, ten different voltages or so by hand so you can isolate one or two electrons next to each other. I did it myself. At oxygen, the first two weeks, my PhD, it took me weeks. I didn't even get close. I got to maybe 50, 100 electrons or so. I said, like, no way. If we want a billion cubits, how's this going to? You can can't have a billion brandons, right? And that's where the, that's where automation AI comes. That's a different thought experiment.

Well, one in each of the worlds, right? Literally. Yeah. One in each of the worlds, you're fine. And that's where the AI comes in, which is basically it's your, this is like loading the cat into the circus. You can think about it as like the bootloader for your operating system on a classical instrument. You know, you press that power button, it fires off a whole sequence of instructions as to what voltage is to set each transistor and to load what software from where. That's what we're building from the ground up. So it's controlling those voltages

such that we can first form that cubit. And once we've got it, it's okay, great. Let's do operations on that cubit. Uh-oh, you know, the cubit's gone out of whack. The electrons disappeared. Let's get it back. Let's recalibrate it, things like that. And to go all the way back to the Google result, I think that's, you know, it's, that's why it's so hard. You, you know, you these cubits don't come for granted. Even when you have them printed on a chip, you really need to calibrate them and fine-tune them to start doing interesting things with them.

Who's this for? Who should be listening to this going, okay, I want to learn about these different AI models that you have? Yeah, yeah. So first of all, it's for ourselves. We are our own users, as well as other people in the spin cubit

in quantum community. The models are effectively analyzing the signals you get from current flowing through that transistor saying, okay, based on, let's say, these certain changes in current, what's the next best voltage I should apply to get me closer to getting a good performing qubit?

Just like the word predictor route of what LLMs are doing anyway, right? Yeah, exactly. We're just voltage predicting. Yeah. Interesting. Okay. So, all right, well, talk about like the users of this, right? So say, say Mark is working on, I mean, I understand the idea that, you know, if Mark is making spin cubits and wants to make more of them, he could probably come to you and say, help me automate

this process. I want to make more spin cubits. But say I've got a pile of wrangled cats back there, I've got a lot of spin cubits, but they aren't behaving properly. Can you help me understand how to make those work better together? Is there an efficiency angle to what you're doing as well? Optimization? 100%. Software can work at night. It can work at all times of the day. PhD students and researchers get tired.

And I think the important thing to understand is this is basically the precursor of an operating system. And I think that's where Mark one day will interact with this software without even realizing. is, you know, it's okay, I want to run my quantum algorithm. I want to design a new drug. You're doing that through conductor, you know, conductor OS or whatever it's called at that point in time. And that goes and compiles all the way down to basically these voltages pulsing on individual qubits, individual electrons. Since we're in this AI quantum middle ground discussion, we just read the, so we have a book club and we just finished the Impost. of AI, Karen Howes, look at the start of Open AI and a lot of the behind the scenes

things that a lot of people are really fascinating read. But we, you know, break it down chapter by chapter. But the reason why I bring that up is the way, kind of the rules of the road, as we called it in book club for AI was like, hey, we just got to get more GPUs and we just got to scale, scale, scale, scale, scale. And then we'll figure out all the shit when it comes down and what to do with that and how to manage it. Are we doing it the right way for the right reasons? Talk to me about what the quantum industry is doing, right? Is there a big push to drive and scale cubits like there is GPUs? Is there any correlation there? Are you seeing a different approach, a better approach, a worse approach? The industry basically splits up into two counts,

which is I can build my quantum computer one cubit at a time. So start with one. cubic and work for two, then three, then 10, then maybe 100, then maybe 200,000. Or I can think about, okay, we're going to need at least a million cubits to do anything useful. Like and by cubits, I mean physical cubits due to this error correcting problem we have. So how am I going to build a foundation in a way such that I can build for a million cubits. It's in a way it's like, I want to get to the moon, do I take a, do I try and build a rocket ship or am I there, you know, assembling ladders in the sense? What camp you sit in, I think heavily depends on who you are actually as a person, in my opinion, and your your thoughts of what's

possible in terms of scale. There's a typical sort of thing where like often the people in the first camp of one cubit at a time is often very much like a university spin-out sort of mindset, where, and this is really how quantum started, you know, it's like at least physical quantum computing, which is, I'm doing an experiment in my lab. Oh, it just so happens. You can actually build a quantum or a cubit out of this. Maybe we could turn this into a quantum computer. And that's really how a lot of the early quantum computing startup came to fruition. And I was like, okay but hold on we need millions of these qubits just how do we get there what platform will actually get us to millions and millions of cubits how will we make them how will we have the power

to cool those chips down where will that power come from where will we have enough of water to keep the these chill um basically your oh gosh these cryomech pumps to keep your pulse tubes cold all these things you need to think about when you're thinking about like million cubits sort of scale is very different to, oh, I just want to get three cubits next year, then four, then five, and so on. And I think that's really what makes being qubits very unique. It's manufacturing-wise, we know we can make chips with the potential to house millions of cubits. But then at the initial Lego block stage of, well, okay, but how can we get our one, two cubic gates? Fantastic. How can we deal with this controlling calibration problem that we're solving?

which is just a huge bottleneck to scaling because we know we can make excellent one, two cubit gates and silicon. We've broken the required thresholds and records for that. The problem is well, okay, if I give someone a four-cubit system, which is a spin cube, that's like, you know, 20 individual parameters they need to optimize to just get the thing working and on. And it's like, how does that scale to millions of cubits? So we're very much in that million cubit thought process of that's where you want to go.

Let's bill for that. You went to Ycombinator. You work in Silicon Valley in a startup. You are living in this world. Is it a different kind of person who works in quantum that works in AI? But now you're bridging those two worlds. Do the two groups of people come together? What's the what's the vibe?

What's the culture of quantum? What's the vibes? I think that the vibe, honestly, the vibe is. very, you know, I'd love to know what the vibe of AI was like, let's say, 10 years ago, because it very feels, it feels like quantum's kind of like at that stage, where we have like these amazing demonstrations and we can kind of sort of see this pathway to, oh my gosh, this is going to change everybody's lives, but we're not there yet, you know, because it's AI in 2013, 2012 and cats and dogs and captioning images and things like that.

Obviously, there are always going to be personalities, but the culture was very, very, very, collaborative and supportive. And to give you an example, during my undergrad, I mean, I did at Oxford and so like just across the street from my building, which was in material science, there was a physics department. And in the basement somewhere in that building was Chris Ballet, who then went on to found Oxford Ionics two years later that recently got acquired by I&Q for like, you know, one billion or something like that.

I met Chris Ballance because I was working on a quantum project for like, like a few weeks, which was what would a quantum computer server farm look like? Who was the best person to talk to? Chris Valence came across the road. Hey, Chris, you know, what do you think this will look like? Because, well, you know, like it's going to be tricky. We have all these problems. You know, we started chatting and me in another group of soon.

So just to give you an idea, you know, like, and I think we're kind of entering this new wave where before it was very much like professor of X group founded quantum company and academia, there's always going to be rivalries, right? Because effectively, you know, what defines you as an academic is you being first to do something, your publication record and so on and so forth. But I think now, you know, the students of those professors and the students of those students are now forming quantum companies. And we're seeing like this huge community sort of grow and, you know, everybody support

each other in different ways. You know, there's many quantum founders in regular touch with. And I'm like, you're just seeing like the age drop and the community build at the same time as well. It's very small. I'm always surprised at how small it is. But because of that, it's very beneficial and very supportive. But, you know, everybody kind of knows, right? Like, is Iron Traps going to win or superconducting Q is going to win or Spring Qip is going to win?

I think, though, the smart ones among us also know, like, look, there's probably not going to. it just be one modality. And we're already seeing that. You know, Google Quantum, for example, are doing great stuff with superconducting cubits. But so's atom computing and quera with neutral atoms, you know. And you can use their computer on Amazon AWS. Literally, you can log on and start running quantum albums.

And arguably, they have more qubits. So we're already kind of seeing that, okay, it's not really winner takes all. And I think because of that, the community is still very supportive. So our last question, let's which asks, all our guests from Kevin Kelly, what should humans be and how does technology help us get there? Oh. Honestly, humans should just be like obey the golden rule.

You know, treat others as you want to be treated. I think having that role is very human in itself because it gives an idea of, long-term thinking. It definitely leads to it. How do we get there? I think technology helps us get there by providing insights to other people's experiences.

So whether that's true, literally like media, discussions, podcasts like this, or understanding of suffering and how to cure that suffering, quantum computers, classical, whatever, you know, solving technological challenges.

Like, honestly, I really see it. Technology is kind of like this eye-opener for humankind. Because you kind of see it already, right? Like, you can know what's going on on the other side of the world where you weren't able to do that where you're just, you know,

like hunter-gatherer tribe in South. American jungle. But then it's like, okay, what do you do about that? Does it affect you? Do you care? And I think, yeah, as technology progresses, you're actually able to impact other people in ways that you just weren't able to before by flicking switches and playing with transistors and whatever.

That would be, yeah, that'd be ideal. Literally like treat others as you want to be treated and leverage technology to do that, you know, whether it's through spreading well, education, whatever. The next best startup that will help that is an empathy activator. I think quantum powered. Quantum powered empathy activator presented by your friends at Thinking on Paper. No, that's great.

That's great. What about a carryover question for the next guest? Can be about anything. Kind of related to the first question, we're just like, do you really care about what you're doing? particularly like other technologists and founders. And also related to the previous discussion of like the community of scientists building this technology. It's like, yeah, like, why are you doing it? Why are you doing what you're doing and do you actually care?

I mean, I know for us, a conductor is like I couldn't think of doing anything else with my life. I really see a world a way that this is just how we have such a huge impact on the world. but I've also worked on projects and I'm just like, geez, man, like can this be over already? Like, why? Why? It's a powerful question. Powerful question to ask

and get people thinking about alignment and meaning and purpose and all the bigger things that give us unending fuel source, right? Because if you're working on something that you couldn't work on anything else, you're powered differently than someone that's working on something less meaningful. So, yeah.

Unfortunately, though, Brandon, this is thinking on paper and all our guests care. Good. Wonderful. Honestly, wonderful. What a conversation. Brandon, thank you so much for chatting with us. Anything you wanted us to ask that we didn't. I think one thing I definitely wanted to clarify is the Palm Pilot thing.

So spanky but still need to be cold. They're still in this big fridge, but you can fit way more in the smaller space. Very important. I don't want people to misunderstand that. Anything to ask, why is it taking so long? Actually, I think is a good question. Why is it taking so long, Brandon? Honestly, I think it's concentration of human beings and resources.

I think it's this approach where either you want to build a rocket ship or you want to build ladders. And we don't have enough people building rocket ships for a quantum computing, in my opinion. There's a finite supply of smart people. It was quite funny earlier you said the smart ones among us. And I was thinking, hold on, aren't you all incredibly smart? And so it just feels maybe with quantum, they're just, that there just isn't enough really, really smart people. I mean, look, even really smart, you can only know so much about like your particular, like, I know a spin cubits, but it's like, I'm like, can I design a quantum algorithm from scratch right now? Probably not. You know, the layers of the stack goes on and on, you know, could I run in. You said probably not. We would say definitely not on our side. Well, I know it's possible, you know, because I've seen other people do it, you know,

a few years of hard study, right? But it's, that's the thing. It's a few years of, you know, like building the tool set and exercising that part of the brain. I don't know. Look, it's different strokes for different folks. I do think, though, like the progress we've made as a communities, like just comical in terms of how good it's been. Like, you know, 20 years ago, people just didn't think it was possible to have a cubit, you know, and they also didn't believe you could rotate the spin of an individual electron, you know, during my PhD, I saw, like, teenagers do it in their lunch break as part of their labs, right?

Okay, so on that, what do people think's impossible now that in 20 years? is your look back and go, well, they're doing it now and their lunch break. Yeah, the scale, the scaling argument of getting to millions of qubits. I think every quantum modality kind of has that, like, you know, we can make one, we can make two, maybe we can make a hundred, but how do we really get to a million cubits? What does that actually look like? Everybody's like, oh, and some people generally believe it's just impossible in certain modalities. Because they're like, oh, the cooling requirements, the power.

But I know, I do think, and, you know, within 20 years, you will have millions of cubits. And it will seem like, okay, cool, one to, one, one billions of cubits. What's the, okay, all right, what's the likelihood, I don't know, seven years from now, someone figures out how to make one cubit so freaking magical and so useful that they debunk this whole scale. No, no, no, no, no, no, no, no, no, no, no way. And the reason being is the numbers required are so ridiculous. You know, it's, and there's a great, there's a great grot and study done. I literally buy like Darko, the US quantum benchmarking initiative where they basically say like, okay, if you get the cubic fidelity to like, you know, like 10-9s of fidelity or whatever it is, sorry, you will still need like a million cubits to run RSA or whatever, you know, to correct for those.

Yes. Okay. You're like and because to really give you an idea, right, like in terms of fidelity, the bits in your computer, they might, you know, make a mistake like one in like 10 to like the 23 times, you know, like it just never happens. Whereas like the threshold for quantum computers is one mistake in a thousand, you know, like 99.9.9% fidelity. Big difference. And so you really have to be able to. to take out more errors than you are putting into the system. What could change is someone comes up with an algorithm that can take advantage of, let's say, lower fidelity qubits. And, you know, Google's done work saying, okay, well, in theory you could run RSA if you have like this fidelity qubits

and with a thousand and whatever. But implementing that physically and experimentally, or good luck, you know, IBM have the tort codes. It's, look, it's once a good. Again, it's that rocket ship. It's like, if you can make many cubits, you don't need to worry about that problem anymore. And you're going to need that number of cubits to do error correction in the first place. And that's why, yeah, we're like very, very focused on leveraging silicon technology

because we just know you can print these things in the masses. A lot to think on. Thank you, Brandon. It's been an incredible conversation. More layers of quantum for us to unpack at a later date. anytime. I love talking about quantum. I mean, we, we went down the rabbit hole, guys, thinking on paper. We speak about a lot of technologies, but it just seems there's something about quantum that is different to the other technologies we speak about. And when we have quantum,

maybe it's just because we know so little and it's just so, nothing spikes our curiosity like this. And because it's so foreign, really, to us. But at the same time, it is us. I think this is one thing I want to do as a company's really like demystify this, this fairness. Because, you know, there are some ways of thinking about the quantum world that actually do make sense. You know, it's like, you know, you have a kid and you offer it two Mars bars, right? One in the left hand, one in the right hand.

It's like, okay, which Mars bar does the kid choose? Kid wants both of them, right? That's effectively your electron in super position. It wants to be around this positive hydrogen atom as well as the other one, right? It wants to be in two places at once. And it sounds ridiculous, but hey, it's normal. I love that. Kids are just every time you speak to a kid, they're literally running through Schrodinger's

cat experiments in their head because they want it, they want it all. 100%. Thank you. If you've got here, you're a real, true, and I mean it this time more than ever a curious mind because some of that was deep, some of it was complicated, but all worthwhile. So thank you for staying with us to the end. If you have any questions on that episode, you want to follow it up, email us at hello at thinking on paper.xyZ. Otherwise, comment, subscribe where you're listening to this. And remember, keep thinking on paper.