The Nuts and Bolts of Quantum Computing

Rick Altherr
Before we start, I do need to say that while we will be discussing my employer, IonQ, any views or opinions expressed belong solely to me and do not reflect the views of my employer.

Parker Dillmann
Welcome to circuit break from Macrofab, a weekly show about all things engineering, DIY projects, manufacturing, industry news, and quantum computing. We're your hosts, electrical engineers, Parker Dillmann

Stephen Kraig
And Steven Kraig. This is episode 439.

Parker Dillmann
And this week, we welcome Rick Altherr on the podcast.

Stephen Kraig
Rick is a full stack engineer having worked on everything from ASIC design to user experience in embedded to hyperscale. Their career has kept them close to the hardware software boundary, primarily working on computer systems at Apple, Google, and Oxide Computer. After a detour through firmware security, Rick is now designing instruction sets, microarchitecture, and real time embedded control systems for trapped ion quantum computers at IonQ. Well, thank you so much, Rick, for joining us on this podcast.

Rick Altherr
Yeah. Thanks for having me.

Parker Dillmann
So before we jump right into your your current occupation of working on quantum computers, let's, I I I how do you start working on quantum computers? Let's just start with that.

Rick Altherr
In my case, it was definitely an accident. Right? Like, most folks that I work with, come to through quantum computing through academic academia. Right? Like, quantum computers are still very much an active, like, research and development type space.

Rick Altherr
And so you have a lot of physicists and a lot of, you know, specialist engineers working, you know, in, like, cryogenics and things like that. And they usually come with that academic path. In my case, I actually was applying to be director of security for IonQ. I had been doing, firmware security and, like, PC platform security for quite a quite a few years, and that's what I had been doing at Oxide Computer. And, it turned out that I applied, and they're, like, well, we're actually not gonna be hiring for that role right now, but we think you'd be a great fit for our embedded, you know, controls team.

Rick Altherr
And I'm, like, sure. Why not? Like, let's just talk. And, so that's how I got in. So I I literally came in with no understanding or of how quantum computers worked.

Rick Altherr
And they're just, like, don't worry about it. You know a little bit about control theory. You're probably good to go.

Stephen Kraig
Don't worry about it. We don't know either. Right?

Rick Altherr
You know, actually, like, that was honestly part of it. And and my manager, like, he he worked in radios for Collins and, like, GPS receivers and things. And he comes from a very strong math background from the control systems approach. And he's just like, this is the kind of stuff we're actually working on. Like, that's how the machine really works.

Rick Altherr
And so you don't really have to understand the physics to do a lot of the embedded system design, because it's actually classical control systems problems.

Stephen Kraig
So I'm curious, about getting into the nuts and bolts of of actual quantum computing. So not necessarily the software or the algorithm side, but more of the nitty gritty of what actually is involved in building and operating a quantum computer?

Rick Altherr
There's a lot and it's also kind of a hard topic to to get into because quantum computing these days is kinda where classical computing, as the quantum folks call it, right, like, digital computing, was back in the 4 early forties. You know, you're looking at folks knew it was kinda possible to build digital computers, like, store program digital computers, but they didn't really understand what the best way to implement memory was or how to encode instruction sets or any of that stuff. It was all new, and they just didn't know. And so there were a lot of different designs that were happening in parallel, different people taking different approaches, and just doing a lot of experimentation and refinement, and they were in competition with each other. And, eventually, you know, we stabilized on what we now think of as a computer, but that took a very long time.

Rick Altherr
Quantum computing is still in that very early stage where a lot of folks are trying a lot of different things. So, ultimately, the nuts and bolts of of quantum computers is you're using some physical element, to encode quantum state. And quantum state is kind of a a complicated concept, but, essentially, it's usually, it from a math perspective or, like, a physics perspective, it's a a 2 tuple where each element is a complex number. Right? You're encoding essentially, a real and imaginary component of, like, two parameters of something in in physics.

Rick Altherr
Could be the spin of the electron. Could be a a charge, electron charge state. It could be There's a lot of different options. But you're working in it at that level, and you're trying to use, essentially, individual ions or neutral atoms or or some sort of physical property, and then you're exciting it in some way to introduce or to perturb the state of it in a way that you can then do some other thing to detect an outcome. And so there's a lot of different approaches.

Rick Altherr
IonQ happens to work with trapped ion. More commonly, you hear you see, like, the big thing that looks like a a chandelier hanging down, and that those are superconducting. So, in that case, they're actually using, silicon lithography, just like making a a CPU die or or any other IC. But they're building, microwave resonators with a special area that they can manipulate at cryogenic temperatures to create, a synthetic, like, cubit. Right?

Rick Altherr
It's like a it's it's a non natural state that it gets kept in, where in a trapped ion computer, you're actually taking some sort of elemental source, like, Ytterbium, and you're, ablating that with a laser and then catching individual ions from that plume of of an atomic source, right, you're capturing it in a electromagnetic field, where you're actually holding it in position with a combination of r f and d c to hold it inside of the ion trap. There's a lot of other different ways of doing it. I mean, I could just probably I could probably name off 6 or 7 other ways, but you'll mostly hear about superconducting. That's the one that tends to get the most news, and trapped ion just happens to be what I actually work on.

Parker Dillmann
So is is your control theory that you work on, is that what's regulating the RF that's trapping these ions?

Rick Altherr
Not so much. So the the trapping part is really usually a pretty constant state. So effectively, you, you the way it's often described is you're taking the ion, and if you think of it like a ball, and you put the ball on, and you you take, like, a salad bowl, and you put the ball on the the bot you know, the top of this inverted salad bowl, it's on an unstable surface. Right? And so it's gonna roll off in any direction.

Rick Altherr
You don't know where it is. What you're doing with the RF and the the electromagnetic field is creating a a modulating pattern that's kind of like you took a a horse saddle and we're spinning it really fast. So it's an ever changing pattern, but it's repeating in a in a, like, merging complex sinusoidal pattern between the 2, and it's holding the ion in that neutral space in the middle. Right? Like, where the interference pattern is.

Rick Altherr
But, most of the time, what you do is you you're only manipulating that when you're trying to actually move the ion within the trap. So it happens during the initial loading part, where you've captured the ion from the plume and then moving it to the position where you want it in the trap. So, there is some aspects there and there are times where we would do wanna move it around for various reasons, But most of the control electronics is actually about the other part, which is, well, now I have this trapped ion. How do I actually influence its state and then also do measurement of it? And that is entirely done with lasers, in trapped ions well, in in in ion q's trapped ion system, it's entirely done with lasers.

Rick Altherr
There are other systems that use other ways of doing that. But in the ion q trapped ion systems, they're it's a jeez. I would have to go and count something like 14 or 15 different lasers that are actually being controlled, that are doing a variety of things because you're actually doing cooling, of the ion. So you're, you know, laser cooling, is a is a thing that happens, because as you're influencing the ion, you're also introducing energy into the ion. And, eventually, it will get too hot and, cause it to, like at that scale, heat is energy.

Rick Altherr
And so if you insert too much energy or the heat gets too hot, it actually will then potentially kick the ion out, you know, kick the electron out of the ion or or other problems. But by using lasers, you can actually reduce the amount of energy in the the ion, and and so you kind of are constantly doing operations and then recooling it. The detection mechanism used there is is what's called optical pumping. So, essentially, you're you hit the ion with a laser, which pushes the energy state up a certain amount. And depending upon where you started from, you when you then hit it with another laser, it will either fluoresce or it will not.

Rick Altherr
So that becomes your detection event. Right? So, like, when people talk about quantum computers at more of an an abstract level, they're often talking about there's this hidden state that you can't measure. Right? Like, when you measure it, it the state is gone.

Rick Altherr
And what we're really talking about, you know, from a physical construct, like, nuts and bolts piece is, it's very hard to keep track of the state inside the the the electron. Right? And you can't actually know it. That that is actually, you know, a fundamental part of quantum physics. But you do have to time your laser pulses that influence that state according to the rotation of the electron in the ion.

Rick Altherr
And so there's a whole complex piece of doing accurate timekeeping to know when to time your laser pulse so that you actually cause a rotation at the time you want and by the amount you want. And so a lot of the control system is actually doing modulated laser pulses at very precise timing, potentially of many lasers simultaneously, alignment and can only be tracked for so long. Like, we don't know what the actual natural phase precession, or, you know, like, the natural rotation freak speed is, but we estimate it closely. And so there's always some error. And, eventually, that error accrues too much to the point where you lose coherence, and then the state is also gone.

Rick Altherr
Right? So there's a lot there's, like, complicated things happening at the physics layer that translate to, I need to actually track the timing of my laser pulses at 4 nanosecond resolution, but I also have to have the precision and correlation of multiple lasers modulating within picoseconds of each other. And so that's where the real time control systems are are in there and and how it really works. Now, of course, that's all in the how it works at the, like, physics and electrical level. The what does that actually mean in terms of a higher level is a, you know, it's easier to think of it in a very different different model.

Rick Altherr
Right? Like

Parker Dillmann
So you're talking, you know, picoseconds, being able to like, real time system that is in that frequency range. Are you running, like, FPGAs or custom ASICs, or how does that work?

Rick Altherr
Yeah. So it's, it's many, many FPGAs. The, they're they're large FPGAs coupled with ADC or with DACs and and some ADCs. There's, servo loops involved and and other things. But, largely, it's you can think of it as a very an arbitrary waveform generator that has 60 plus channels.

Rick Altherr
Right? And you get to program every single channel individually, and each one of those channels can do, like, 2 tone modulation. And those are used to emit like, the output of those arbitrary waveform generators are coupled into other elements like electro acoustic modulators, where or, or sorry. Electro optical modulators or acousto optic modulators, which are, you know, devices that either based upon the sound pressure or based upon, electrical impulses act like shutters or modulators for a laser pulse. So you have, like, a a fixed laser going through it, through these AOMs or EOMs, and the the outputs from the AWGs are actually causing the modulation on that laser to that external device.

Rick Altherr
So you can kinda think of those devices as, like, upconverters. Right? Like

Stephen Kraig
And and every one of these hypothetical channels from this, function generator can all be synced or desynced or however you choose. Correct?

Rick Altherr
Yeah. They all operate off of a common clock, and so they're all running at 250 megahertz, you know, 4 nanosecond time base. But that time base is synchronized within picoseconds across the entire system. Okay. So you

Parker Dillmann
Yeah. That that answered my question. Now my other question is, I don't know if you you can answer this one. Oh, man. I just lost it because it was more about, I guess we just forget it.

Stephen Kraig
Well well well, I Okay.

Parker Dillmann
So come back to me.

Stephen Kraig
Yeah. I have I have one question. You were mentioning, basically, getting out of phase or out of sync with the ion itself over some period of time. Is that something that you have to regularly reset so you can be back in sync with it, or is that not even possible?

Rick Altherr
Yeah. It's, so we talk about it in terms of the coherence time. Right? So, essentially, the the ions are always moving at some phase precession, and that is influenced by its physical location inside the trap, the ion itself, and then what laser pulses have actually been imparted on it. So there's, like, a a multipart thing where you can kinda estimate what that rotation is, but it's always rotating.

Rick Altherr
Right? Like, every particle is always or every atom is always rotating that way. So, initially, you don't know where it is. So when you start, you basically do a a laser pulse across all of the ions simultaneously that establishes a baseline time. And then you're calculating your phase offsets from that point forward.

Rick Altherr
So you you kinda start with, I don't know absolute phase, but I establish a a common reference point for relative phase. And then it's that you can track that relative phase for some amount of time before your error gets too large, and then you've lost track of it. And that's the coherence window. And so when you run a quantum circuit, you're actually breaking it down into these chunks, which you call shots, which is, you know, doing that sequence. So it goes through what we call CPD, Cool Pump Detect.

Rick Altherr
So there's actually, you know, you cool the ions, you, run your actual circuit, then you optically pump them, then you do your detection. And that sequence runs. At the end of that, because you did the detection, you've already lost all of your quantum state. Right? Like, this this is that whole thing of, if you measure it, the quantum information is destroyed in the process.

Rick Altherr
So you're collapsing the quantum state down to a 0 or a 1. And so then at that point, it doesn't matter what the phase is, and so you start over at the beginning and you establish a new relative starting point, and that becomes a new shock. But you can reuse the ion many, many, many times. Right? Like, the ion remains in the trap for a very long time, but your coherence interval might be, like, one second.

Stephen Kraig
Can can you just leave an ion in the trap? You know, as long as you keep the system on and running, can you just do it basically indefinitely?

Rick Altherr
You can do for a very long time with the trap diagon approach. Right? And this is where, like, the different approaches come in play. Because in a superconducting approach, like I said, they actually sort of artificially construct their qubit. Right?

Rick Altherr
It's not a natural state. And their coherence times are, like, microseconds. So they have to do everything. All of their operations have to happen orders of magnitude faster, which is why they use microwaves instead of using laser assemblies. Right?

Rick Altherr
And it's, like, there's differences in the the physics itself around how fast your operations need to be and how long your coherence times are. And so there's different trade offs. Now, in the trapped ion approach, you can hold on to the ion in the trap for a very, very long time, but your coherence interval is gonna be, you know, 1 to 2 seconds, which is nice, but using lasers is slow. So your gate time, you know, your operation time, the way quantum circuits are represented is is thinking they're they talk about as quantum gates in a quantum circuit. It's not really like an electrical circuit diagram.

Rick Altherr
It's more like a a mix of, like, a circuit that progresses over time. Right? Like, if you had a schematic that changed or that showed a progression over time. I know that's probably not terribly helpful.

Stephen Kraig
4 d schematics.

Rick Altherr
Yeah. I mean, they're a lot simpler, though. It's, like, you essentially have all your cubits running left to right, like, you would, you know, on a on a musical, scale. Right? But then you're showing operations as it's between you know, it's this type of gate happens at this time across these cubits.

Rick Altherr
So you're showing connections across it, and they just call that a a quantum circuit. But those individual operations, how long they take also varies with which type of system you're using. So, like, you you know, your gate time on a trapped ion computer might be microseconds, whereas your gate time on a superconducting one has to be down in the, like, you know, nanosecond time range.

Parker Dillmann
Okay. I I do remember what I was gonna ask about, is because you're using a it's all lasers on the system that you're on. How does a laser cool an ion or anything? Because it's it's it's energy. How's adding energy to a system enable it to cool down or lose energy?

Rick Altherr
So I'm gonna piss off I'm gonna piss off all the physicists because I know that I'm gonna completely botch this from accuracy. But think of it this way. The idea is that you're removing energy from the the electron or not from the electron, electron. But you're removing energy from the the ion itself. Right?

Rick Altherr
Now, there's a lot of ways that energy can be re related, but part of that is, like, the speed of rotation, part of that's, like, how high up in the energy shell it is. And, essentially, the idea is, like, using the laser, you can time where and when it's going to hit, and you can do your modulations based, like, around the expected rotational speed of the the the electrons around the in in the atom. So if you time it right, it's equivalent to breaking. Right? Just like you think of acceleration in a car.

Rick Altherr
Right? Like, acceleration can be either actually increasing speed or slowing speed. Right? You're you're doing so by applying forces in different ways. It's the same thing happening at the quantum level with lasers.

Rick Altherr
It's just that you're actually, by hitting it with the photons, you're you are imparting energy, but you can do so against the natural procession of it, which removes energy from the system.

Parker Dillmann
Okay. Okay. That makes sense.

Stephen Kraig
You're giving it a nudge in the opposite direction.

Rick Altherr
Yeah. And there's other things you can do, like, you can take an ion next to it and actually cool the ion next to it and have it'll actually, you know, draw down heat because it's it's adjacent. So you can have what they call sympathetic cooling where, you know, neighboring ions are used to like, one ion was actually used to be the cubit and the next ion over is just a coolant. And you have

Parker Dillmann
to and you can do that after you've synced all the cubits together?

Rick Altherr
Yeah. You can do the cooling, you can do pretty much whenever you want, and it's not a particularly, dangerous operation or or anything like that. The the main thing is, like, when you've established that relative phase time, right, where where you you begin your shot, your shot runs until either your error has increased too much for the result the the quantum information, and it doesn't you know, it's basically gone back to a neutral state. Right? And this is probably where getting into a little bit more the abstract model makes sense, because the the physics side gets really tricky at that point.

Rick Altherr
But, like, from the the abstract model of quantum computing is, imagine you have a sphere. Right? And and what folks talk about in the the quantum computing world is is called the Bloch sphere, and think of it as the North Pole of the sphere is a binary one and the South Pole is a binary 0. So, and it doesn't really matter. It's convention.

Rick Altherr
You could flip it, you know, whatever. But the point is is that a qubit can represent can be in a state of any point on the surface of the sphere. The sphere is a unit sphere. Right? And, essentially, what you're doing with all the laser or the gate operations is you're moving the point around on the surface.

Rick Altherr
So you're picking some point and then you're rotating it, you know, around one of the axes or multiple axes, and you're picking a point on the surface of that sphere. When you do the detection, what you're doing is an operation where the location on the sphere determines the probability of whether it will be a 1 or a 0. Essentially, the closer you are to one of the poles, the higher the probability that your measurement will will be at that pole or at the representation of that pole. So all quantum computations are really working on this basis of you can move around in an essentially infinite space around the sphere. Right?

Rick Altherr
It's kinda like analog computing or like an analog voltage. In in theory, it's infinite. Right? The problem becomes noise and an error that gets introduced. But, ultimately, you can move anywhere on the sphere, and then at the end when you measure it, it collapses to a 1 or a 0.

Stephen Kraig
With some probability. Right?

Rick Altherr
Right. Which also means that you don't just run the computation once. Usually, you run the same circuit for, like, a 100 shots and look at a histogram of the outputs, because you actually have to analyze the the probability over, at least some relevant number of shots to know where the the majority fall to know what where your competition ended up.

Parker Dillmann
Well, that's fascinating. I didn't even think about that. So

Stephen Kraig
so if if the 2 hemispheres represent 1 and 0 and then all all other points on that sphere represent some kind of probability towards 1 or the other. Does does the equator of this sphere equal 50% no matter where you're at on this equator?

Rick Altherr
Pretty much. And there there's actually a special gate that that kind of like, you start with the the 2 poll locations, the the one and the zero, they call them the basis states. So there's, like, the zero basis state and the one basis state. And you'll see in the notation, there's there's a gate, and I probably would get the pronunciation wrong, but, there's a specific gate that actually moves you halfway between. Right?

Rick Altherr
It puts you at the equator. And that's because it is incredibly useful to be at this point where you have a 5050 probability distribution to start from. So that's often what happens at, like, state initialization. So you establish that, reference point where you start your relative phase measurement. And the first couple of things you do is actually run that that gate to set all of your qubits at the 50%.

Rick Altherr
Right? Put them all at the equator. Then you're running the operations that are maybe that would ultimately be data dependent. So you have input data where you're, like, you know, I'm gonna run the sequence of gates and and some of the gates actually can be, you know, single qubit operations, like, what and they call them 1 q gates, where it's just like, this rotates you, halfway around, the sphere on the z axis. Right?

Rick Altherr
That's an r z gate. But then you can also have 2 queue gates where it's essentially you will do the gate, but it's conditional on the state of another gate. But it's not it's it's kinda like well, the gate that we talk about is the conditional NOT or the CNOT gate, And it is kind of like a not, in that it essentially flips the probability. It inverts where you are on the on the on the sphere. The conditional aspect though, it's it's not like that immediately takes effect.

Rick Altherr
Right? It's not inherently a point in time operation that persists where they become entangled, and future changes on the control qubit will actually adjust what happens to, whether or not it gets inverted.

Stephen Kraig
Why? So

Rick Altherr
And this is about the point where I start walking away from it because it gets into algorithm development and I don't understand how you turn this into useful operation.

Stephen Kraig
It's funny because I was gonna ask something very similar to that. It's like, you know, to to also potentially sound somewhat ignorant, what what's going through my head is, so what? Like, we can we can make something possibly be 1 of 2 states. How is that useful?

Rick Altherr
There I mean, this is a thing where there there is a lot of research that's been going into this for decades on how to turn these primitives into useful operations. And there are useful algorithms. They often fall into the world of, like, eigenvector val calculations or, you know, and and they often end up being about looking at energy level states in physics simulations or chemical simulations. So there are certain like, one of the top level things of quantum computing is it is not a general purpose computation device like we think of a computer. There's a lot of folks who who talk about quantum computers gonna be a revolution.

Rick Altherr
Everything's gonna be a quantum computer. A a more conservative outlook is a quantum computer is really an accelerator like a GPU. It's really good at specific types of operations that classical computers are not particularly good at.

Parker Dillmann
But my question is, how so you're talking about all these gates. How are the gates implemented in this kind of computer? Because you've got a bunch of lasers. How's that play?

Rick Altherr
Well, so that's that's exactly it. Right? There's, there's a comp a compilation stage where we take that list of gates, and each gate turns into a series of modulated laser pulses. So each gate has a sequence where it you know, on a on a 2q gate, it's actually gonna be a set of lasers because, at least in our system, like, one of the one of the key differentiators in a lot of systems is the connectivity between cubits. So in a lot of superconducting systems, because of the way they're constructed, you have, like, a 4 corner system.

Rick Altherr
Right? You have a a grid, and so you can talk like, each qubit can interact with its adjacent neighbors in any of the four directions. But that's it. Right? If you can't skip over 3 and do that.

Rick Altherr
So you have to optimize your you have your compiler and your optimizers have to rearrange your algorithm to work through, I can only talk to my nearest neighbor. In the trapped ion laser or, like, Raman laser approach has all to all connectivity within within the trap. So, essentially, you can steer the lasers to aim at any particular 2 ions. And so part of the laser controls is actually the steering mechanisms. Right?

Rick Altherr
Like, part of these AWG channels are actually controlling steering of the lasers. Then you also have to have background lasers that are just, sort of, constantly on across all of the ions. And then you have the actual laser pulses that are going to those 2 target ions for the 2q gate, and each one and those are modulating the actual effect that you want. The details of exactly what that pulse looks like gets into a level of the physics that I just don't really understand.

Parker Dillmann
Because I'm I mean, that this is what's going through my brain is, like, because you were talking about, like, the knot operation. Mhmm. I'm assuming you don't you your when your laser does what it needs to do, I'm not going to pretend to know what that is, Hits it with some kind of modulated waveform, or energy pulse, but it doesn't it's not like a inverter gate that we have in silicon where a one becomes a 0, a 0 becomes a 1, it's more of a I'm just going to make sure I'm going to hit it in a way that I know with probability it will flip its state, and you don't really know what that is until the end. Right? Well, so in the case

Rick Altherr
where you think of a a a digital not gate, you're having or a digital inverter, you're you're flipping the state from 0 to 1 or 1 to 0. But if you think of that as, like, a vector operation, right, you're doing an in you're you're taking if you have a vector pointing from 0 to 1 and you invert it, you go to negative one. Right? Well, that's closer to what's happening because you're dealing in a complex two dimensional space. So the the point on the sphere that you have when you'd run the the not gate or the c not gate, when it actually indicates that it should flip, you're actually going to the, like, polar opposite location.

Rick Altherr
Right? So you you would, like, figure out the point on the surface that is directly opposite on and that's where you're gonna try rotate to. And, really, the laser pulses are not literally rotating electrons around. What they're doing like, the the point on the sphere represents some notion of the rate of spin or the particular charge or, you know, some other physical property, and that's what the lasers are actually modifying. They're either introducing energy or they're adjusting, like, the rate of spin or, you know, a variety of things.

Rick Altherr
And and it depends on the system that you're using and the exact configuration as to what exactly you're controlling, and that's where every system is different and every, you know, every company that's making a quantum computer is taking a different approach.

Stephen Kraig
Because you don't have to use the same parameter. Right? There's multiple Right.

Rick Altherr
And there's a totally different trade offs for each one of

Stephen Kraig
them. So so I I wanna rewind, for a second. There there something came up in my mind. The so so you mentioned earlier that the the actual apparatus or mechanism that that you use to trap these ions. You have a material.

Stephen Kraig
You ablate it. It basically you create, like, a cloud, right, of of ions. And then you capture one of those ions and move it over to the trap. Right? Or Mhmm.

Stephen Kraig
Correct me if I'm saying any of this wrong. But how do you know you actually got one? It's not like you can lift the lid and be like, oh, yep. It's right there. I see a cubit.

Stephen Kraig
Like, what is the indicator or what's the actual mechanism for you saying, yep. We got one?

Rick Altherr
So remember that we the detection that we do is we cause the ion to fluoresce. So what you actually end up doing is putting a camera with an optical path into the trap, and you actually run a laser sequence that causes all the ions to fluoresce. And you're then, essentially, using the camera as a photon counter, and you're looking to see whether or not you got counts.

Stephen Kraig
So are you is there just one ion on there? Or is it you you grabbed a chunk of them, some cloud of them? So you

Rick Altherr
load them individually. Right? You do capture 1 at a time, but you can think of the the trap as, at least the traps that we work with are are linear trap. Right? So it's essentially you're constraining the ion in 2 out of 3 dimensions or 2 out of 3 axes, and the the 3rd axis is is the one that you can move it.

Rick Altherr
You can move it along that axis. And then along that axis, there's a multiple zones, and one of those zones is the loading zone, and that's where it actually has the hole for the the plume to come from the ablation area up into the trap and where you do the initial capture. But then you have a separate area that's a little bit cleaner, where you actually move it along into that area and you line them all up in a row. And so you can hold, like, how many you can actually load in there becomes a function of, the size of your trap, the number of electrodes that you actually have in that space to for how fine a resolution you can actually hold the positions, and then also how well you can detect the fluorescence. So in some systems, you might use a fiber optic array where you actually have a fixed number of fiber endpoints and they're pointed at very specific locations along the trap.

Rick Altherr
That's gonna set the maximum number that you can actually effectively use. You could probably load more, You just can't see them. But if you use a camera, you can see everything more, but there's trade again, it comes back to trade

Stephen Kraig
So in in the trap area, you you're not just capturing one ion here. Right? Is it is it is it an array of ions? Basically, are you doing a bunch of different cups that hold them? Or is it literally one trap?

Rick Altherr
Well, so the loading area you catch one ion at a time from the ablation And then you move it along and you you merge it with the line of them. And so you build up a a line along the length of the trap. Okay. And so you like, the current systems we have, we talk about being, AQ and some number. Qbits.

Rick Altherr
It's more of a a a top level measurement of performance of the system, but it's also, you know, you can think of it as we get 36 ions lined up. Right? There's probably more than 36. I'm not gonna give you an exact number, but 36 of them are usable as qubits in that space.

Stephen Kraig
Is is this environment, in a in a vacuum? Is it, is it is the entire environment cold or are you just trying to reduce the temperature of the ion itself?

Rick Altherr
So, again, this is where, like, different systems do different approaches. So the trapped ion is is nice and that you really only have to keep the trap at cryo temperatures. So the actual cryo chamber is quite small. I would say, think basketball sized. Mhmm.

Rick Altherr
Right? Roughly. In a superconducting system, there's you have to actually keep a lot more of the system at cryo and at vacuum, or sorry. They don't necessarily have vacuum, but they have more cryo. So they have to keep it at a colder temperature, and they have more of it.

Rick Altherr
So they have to build a much larger they literally call it the fridge. Mhmm. Right, and that's what the chandelier hangs inside of. But in a trapped ion system, it's a much smaller package that does have to be kept at vacuum, relatively high vacuum, and then also at at cryo temperatures. But not, like, like, there's different grades of cryo.

Rick Altherr
Right? Like, you it's, like, how cold are you really gonna get? Are you talking 30 Kelvin or are you talking 5 Kelvin? Like, like, that's

Stephen Kraig
That's fascinating. So how, how was parallel computing done? You said you had, what, 14, 15 different lasers, but they all have different functions. Right? Could and and and you have the ability to steer some of these lasers.

Stephen Kraig
Are you interacting with multiple qubits at the same time or is it just one at a time sequential?

Rick Altherr
Well, it it depends on if you're running a 1 q gate or a 2 q gate. You know, in a 2 q gate, like, you potentially could do more, like, you could do a 3 or 4 q gate, but from there's been a lot of research that shown you can just like in digital computing, you can build everything out of NAND gates. Right? There's a subset of gates that are necessary that allow you to build all possible quantum gates. So you don't have to build a 3q or 4q gate.

Rick Altherr
It might be advantageous for performance or something, but you really only need cert a couple of 1q and 2q gates. And so the systems are often designed their native gate set, like, the actual operations they can do, is a set that allows them to emulate what's called, like, the common gate set. There's a specific name for it that escapes me at the moment, but there's like a and that and that's what most people program in is is this, like, common gate set, and then it gets translated to the actual native system. So just like in a modern computer, you have the instruction set architecture, which is what everybody writes their programs in. But internally, in the microarchitecture, it's something completely different, and there's a translation that happens.

Parker Dillmann
Oh, so

Stephen Kraig
I could write an algorithm and give it to 2 companies with quantum computers and they could just translate it to theirs and and give me a result Yes. Back.

Rick Altherr
Okay. Yep. Exactly. Interesting. But it does mean that you are controlling, you know, multiple qubits simultaneously.

Rick Altherr
And the whole thing is that you want to you utilize that quantum entanglement, like, that's a key aspect of writing quantum algorithms. If you only interacted with 1 qubit at a time, you would only get results from 1 qubit behavior, and you actually want those 2 queue gates for that entanglement behavior. Yeah. And there there's a really good website that goes through a lot more of how the algorithms work. It's, quantum dot country.

Rick Altherr
It's a little long, and it might, you know but it does go through, for example, how you do what they call, like, the quantum search algorithm and and how it works. It's not it it takes a lot to of building up, you know, the the knowledge base to get to the point where it it kinda makes sense, But it goes through how this would be faster. Now in practice, real quantum computers can't actually run the quantum search algorithm currently. And and that comes around to, you know, some of the constraints of, like, why are we still in the r and d phase? Why aren't we just using these?

Rick Altherr
Like, the you know, and they're complicated, but we've been doing all this research. And the thing is it comes back to those coherence times. Right? There's 2 major parameters that quantum computers get evaluated on. 1 is the number of cubits that you actually have.

Rick Altherr
Right? Because each cubit ultimately turns into a 1 or 0 representation at the end. So it defines how many how big your data is that you're working on. Right? Like, how many bits wide it is at the end.

Rick Altherr
The other parameter is how many gates can I operate? And that's purely a function of how long I have before my error adds up and I've lost coherence. And so, different algorithms require a different number of gates. So if I need to run an algorithm that uses 8 qubits for a 102q gates, like, sure, that fits on most systems. But something like a search algorithm might need 4000 cubits and a 100,002qgates, and that doesn't fit on any quantum computer that exists today.

Rick Altherr
So there's a lot of the interesting applications that would be advantageous just are too big, either in terms of the number of cubits required or the number the gate depth required to actually operate on the physical machines. Now the other side of that coin, is, you know, when does that trade off happen? Like, when when does this become useful to run them on physical quantum computers? And there's this whole aspect of you'll hear folks talk about quantum supremacy. I don't really like the term, or quantum advantage is a slightly better term for it.

Rick Altherr
But it's like, when will quantum computers be better at running an algorithm than something else? Right? Where a classical computer can't actually achieve it in a reasonable amount of time. That's what the benchmark is. And so the problem is is that as quantum computers get bigger and better, so do classical computers.

Rick Altherr
And so you can simulate a quantum computer on a whole crapload of GPUs. So GPUs keep getting faster, and the quantum computers keep getting faster, and so the point at which that crossover point would happen keeps moving. But we haven't gotten to a point where it actually crosses over. Now, in the nearer term, there's also a thing where you might talk about might hear some folks talk about, commercial advantage, which is it's not that you can't simulate it. It's just that it becomes more cost effective to actually run it on a on a physical quantum computer for specific applications.

Rick Altherr
Right? It's not a universal, like, you can't possibly simulate the quantum computer anymore with classical and reasonable time frame. But for specific applications, the quantum computer will be faster, or more power efficient, or less expensive.

Parker Dillmann
I I got a I got 2 questions. The first one is alright. So in in programming, this is the hello world. What's the hello world for quantum computing quantum computer? Because, you know, in in programming, it's print to the console or print to serial, and then you got blinking LEDs and, you know, you know, embedded embedded hardware.

Parker Dillmann
What what's the hello world or blinky for Quantum? Like, how do you know that you're, like, yes. It's working finally.

Rick Altherr
Well, okay. That that's that's interesting that you put it that way because there's really 2 different things. One is, is the machine working? Which is which is very different from is my algorithm correct?

Parker Dillmann
That is true. Like, your your your your investor stakeholder shows up and is like, I wanna see this thing working. Like, how do you how do you do

Rick Altherr
that? Right. And and a lot of that is actually running things that are not necessarily meaningful quantum algorithms. Like, one of the ways that quantum computers get evaluated is by literally running random circuits and recording the performance of it in terms of what what they call the fidelity. Right?

Rick Altherr
Like, how much error did you accrue or or, actually, the the inverse of the error rate? How how accurate were you, as well as the number of cubits that you ran and how many gates you were able to run? There's advantages and disadvantages to that approach, but there a lot of the calibration and, like, proving that the machine is actually working is running very specific test patterns, you know, just like you would do on any other system, and validating that, for example, the laser is pulsing at the correct time and, you know, doing a and there's a lot of, like, looking at scopes and, you know, running plots of different patterns and looking at the what the the actual fluorescence rates are and all that kind of stuff. When you get to the algorithm stage though, you you essentially just think the machine is working. Right?

Rick Altherr
For, like, people writing algorithms, they don't they're not particularly concerned about whether or not the machine is working. That is the machine op owner operator's problem. Now, the Hello World, like, what do you actually write? I'll be honest, I've not written a quantum circuit. I look at them because of the work I'm doing, but I'm more concerned with how do I run them rather than how do I write them, which is kind of an odd thing, but, you know, it happens in the computing world too, in the classical computing world.

Rick Altherr
Now there is the the one thing that's probably a bit surprising to folks is that most quantum programs are actually written in Python. So a lot of the frameworks, like Qiskit is one of the more popular ones. There's also, CUDA Quantum Quantum. Yeah. CUDA Quantum.

Rick Altherr
There's a couple of others. They they're Python frameworks that let you write out, like, here's the circuit that I wanna run. I I need this many cubits, and then I wanna do this. And then they have the plug ins for the different back ends for different, computers. So it it's, in a lot of ways, like writing a GPU or program.

Rick Altherr
Right? You're writing it in sort of a neutral language, and then you have these back ends that actually compile it down to the specific machines that it's gonna run on. But a lot of the examples are, like, just draw up something that doesn't necessarily have to be meaningful, but that you can actually figure out what the output should be. You know, so, maybe, it's allocate 2 qubits, run the thing that puts them at the 50%, and then run a rotation that should force them to be, you know, 1 to be a 1 and the other one to be a 0. And you run that and you just see, does it actually come out to be a 1 or a 0?

Rick Altherr
Right? And then you get to look at the probability distribution of how often did that actually occur, and that tells you a little bit about the accuracy of the machine that running on. A simulation might show that as perfect, but a real machine is gonna have error in it.

Stephen Kraig
You know, errors brings up one of the questions that I I had. You you mentioned that, the the coherence time is that correct? Coherence time?

Rick Altherr
Mhmm.

Stephen Kraig
It is or one of the ways you identify that you're nearing the end of your coherence time is is an increase in error. But how do you know what your error is? If how do you know that the system is becoming or or there are more errors in the system? Is it just that the histogram starts to smear out more and and everything looks more random?

Rick Altherr
There's definitely that is one of approach, and and it is one of the, like, one of the key ways of doing it is that you actually look at the histogram output of an algorithm that you know should behave in a specific way for these inputs, and you see whether or not the sharpness of that histogram. Right? Is it actually starting to to distribute the probability further out? There are other techniques that are more based around trying to isolate where the error is coming from, because the the you know, looking at just the histogram output is telling you about the fidelity. Mhmm.

Rick Altherr
It's telling you how accurately am I getting to the the result. But the source of the error can come from many different places. And so that's where you start having different tools where you might run specific algorithms and look at the output, and it's not meaningful in terms of what the result would be, but where the distribution of that, that it clusters around might tell you a source of error. Or you might look at actual output timing data from the control system and see, for example, oh, I was off by one clock cycle, and that actually causes all of my phase estimations to be wrong. So, therefore, I, you know, incurred extra phase there.

Rick Altherr
Or, you know, my phase estimation uses a fixed point number with which is a finite precision. So that's inherently gonna have some noise characteristics that come in just by virtue of, having reduced precision.

Stephen Kraig
So so not only are you looking at the system itself, the, the output of the algorithm or the output of the cubits, you're also monitoring all of your inputs to it. And if any of that gets off, then there's error.

Rick Altherr
Yeah. You could think of it like in a modern computer system, there's a whole bunch background processes that are running, like, ECC is running. Right? Like, you you have things that are telling you if it's misbehaving or, like, your your modern PHYs are constantly looking at signal integrity and adjusting parameters. In a quantum computer, it's so large scale that there aren't a whole lot of those automated systems happening in the background all the time.

Rick Altherr
Instead, what you do is you stop running customer workloads and you go and run a bunch of programs and and calibration sequences and, basically, have automated systems that look at the results of that and then make a decision about what to do. And maybe that means, like, oh, it's time for me to to dump this ion chain and reload. Or maybe I need to actually, you know, run this analysis of it, and I come back with corrections for some of the other print like, calibration parameters. And so you run those calibration sequences periodically, and then start running jobs again. Right?

Rick Altherr
So there's, like, periodic checks of is the machine still in good state and returning the fidelity that we guarantee.

Stephen Kraig
Is is a lot of that found empirically? Like, how often you have to recalibrate?

Rick Altherr
It's, I I would definitely characterize it as there's a lot of research and simulation that happens when designing and in the early stages of of getting the machine commissioned. But once it goes into actual operation, it's much more empirical. Mhmm. Right? Like, going through the commissioning phase into the the actual operation, it's, like, there's a point where the simulations can only take you so far because each machine has its own its own quirks.

Rick Altherr
Right? Its own little tolerances. And, like, if you take a machine down to swap the ablation target out. Right? Because that's I mean, you're literally ablating a material.

Rick Altherr
Eventually, you wear it out. So if I replace that, well, I had to potentially warm the machine up, come back up to to atmospheric pressure to be able to change that, and then I have to bring it back down to vacuum and back down to temperature, things shift. So now you have to run all the calibration sequences again. So it's, like, it's very time dependent. It's, you know, so you you can only do so much of where you expect it to be, but you have, like simulation gets you into a here's the bounds of, like, what the operating area should be.

Rick Altherr
And then then you have to spend a lot of time running calibration scripts to find out, where does this particular machine at this moment actually behave?

Parker Dillmann
So my my second question is, so electrical engineering is already like, when when you're in school and you're talking to a bunch of other engineers and everyone keeps saying, like, electrical engineering is, like, the hardest. And the main reason why people say that it's because you're dealing with electrons a lot of times and you don't really see this is the hardware electronics, I guess, But you don't really see what's really going on. Right? This is even further down that hole of now you can't even really see what you're working on, especially now, Rick, that you worked, before the podcast we were talking, he works fully remote. I can't even imagine trying to work on something like this, fully remote and also not even kinda hard for me to say this, I guess.

Rick Altherr
Well, I I mean, you're right. It it is very abstract. Right? Like, even if I wanted to know what was actually happening inside the machine, I can't. Like, that's that's fundamentally impossible.

Rick Altherr
Right?

Parker Dillmann
There's no it's not like a register where you can go, okay, if you put these inputs into this ALU, it's gonna do this thing with the bits.

Rick Altherr
Yeah. Debugging gets really hard.

Parker Dillmann
Yep. Once once you, Lisa embedded, like, once you figure out a debugger, like, those those exist, then that this that process of writing like assembly code and stuff starts making more sense. Whereas this there's not that yet or maybe not impossible with Quantum.

Rick Altherr
I mean, you can do a lot of work at the machine level. Like, for for the people designing the machines, there's a lot of things we can look at, because a lot of it is actually classical systems that are that are operating the machine. And we often separate the machine into, the the sort of physics part of it, the the actual trap, cryo chambers, all that kind of stuff, where the actual quantum operations are happening. And you can't really know what's happening inside that space without like, you have to run literal physics experiments to figure out what's happening, where all of the control systems in front of it is, like, all the test equipment that they're actually using for it to run those experiments. And those, of course, we can apply all the standard techniques to.

Rick Altherr
Right? I can hook up a scope and a logic analyzer, and I can use, you know, integrated logic analyzers and FPGAs, and I can run regression tests on software and all sorts of things. But you're right. Like, when I'm developing an algorithm, a quantum algorithm, or I'm working through, tracking down error in a quantum computer, there is a certain point where you're just getting down to, how do I run these experiments where I can't actually see what's happening at the moment? I can't stop midway through and inspect it.

Rick Altherr
I can change the algorithm to end at that point, but that won't necessarily tell me what's going on. So you end up having to construct these different scenarios to kind of induce behavior that you wanna see and then think about it and come back with this analysis of, well, when I did this and I did this and I did this, then these were the behaviors, and that means that it probably is this. And then we go run another experiment to test that hypothesis and figure out whether we actually found the real real cause or not.

Stephen Kraig
I can totally imagine a boss, you know, something's going in incorrect or or or wrong and a boss comes up and says, I need to know exactly what's going wrong and it and you're like, it's physically impossible for me to tell you what's going wrong here.

Rick Altherr
I I actually have some stickers on my work laptop that I got from a a friend of mine. And I have one that says, uncertainty, do not attempt to measure. Right. Like Love it. So

Parker Dillmann
this this is a this is a statement from you when we were we were talking about this podcast, and Steven put this in here and I love this like just touch on it a little bit and it's a quantum computers aren't useful for anything except R and D today and that likely won't change for at least a few years. What needs to change?

Rick Altherr
Well, that comes back to that that commercial advantage thing. It's, you know, on one hand, you can look at it as as, like, the quantum advantage, where you actually get the machines good enough in terms of number of qubits that are there, with sufficient connectivity between those cubits and good enough fidelity, which also means good enough, you know, fidelity after some number of gates. Right? Usually, it's, like, your your fidelity is a target number and you're, like, I'm 99.6% fidelity after so many gates. Right?

Rick Altherr
And, like, quantum advantage is when you get those 2 parameters to a point where the time to run the same algorithm on GPUs would take much longer than running it on a quantum computer. We're a long ways off from that. Like, scaling up these systems, you'll see again, this comes into the trade offs of the different systems. Like, superconducting systems, it's actually fairly easy to scale up the number of cubits. And so if you look at IBM's systems, though, you'll see the cubit numbers keep climbing.

Rick Altherr
And that's because, like, they're literally just making wafers. I I I mean, I say just making wafers, like, that's an easy thing to do. You know, it's it's complicated, but it's also it's a known simpler process. Right? It's it's using a a lot of existing practices around silicon production to create those qubits.

Rick Altherr
But they have the problem of the nearest neighbor connectivity and their their, fidelity times are pretty limited. Right? They they kinda have so you get end up with a a lot of not so good qubits. So you end up having to use a lot of those qubits as error mitigation. Right?

Rick Altherr
You might have to have Whereas, in a trapped ion system, you have a lot of Whereas in a trapped ion system, you have a lot fewer qubits, because it's a lot harder to hold on to all those ions, but the fidelity and the coherence signs were a lot longer, but it's also slower. Right? So there's just, like, different trade offs in the space around these different approaches, but the commercial advantage piece is really looking at when do we get an algorithm that is advantageous. It makes sense to buy the quantum computer to solve that problem versus doing it all with GPUs. And that's a trade off of not only how long does the algorithm take to run, but also how much power it consumes to do that and, the actual cost of the machine.

Rick Altherr
Because one way of scaling up some of these problems, if you have a problem that scales to, like, n log n cubits, for the size of the problem input, Well, like, that that's growing, you know, more than linear. Your you can scale that out with GPUs by just keep adding more more servers and more GPUs. But your power increase per system that you add is pretty large. And so, eventually, you get to a point where you're, like, the amount number of servers that I would have to buy, I'm buying, like, whole data centers worth of machines in order to actually run my computation. Whereas, if I can fit that in a quantum computer, the quantum computer may cost orders of magnitude less and be about the same amount of time to do the computation.

Rick Altherr
So it's this is something the whole industry is trying to figure out is, what are those algorithms, How many cubits do you need? What fidelity do you need? How do you get your system to do that? And the thing is that the trade off space between all these different approaches means the algorithm that's gonna be commercially advantageous for each approach might be different. Because a system with a lot of a lot of noisy cubits might be better for some algorithms than one that has super high fidelity but few cubits.

Rick Altherr
I think those are, you know Mhmm. Being a two dimensional space, it's there's a lot of place to play. But even in those cases, again, it's a it's a race with GPUs keep getting faster. So it's just gonna be, you know, no one has a good answer for this right now. There's some candidate, algorithms that seem like they would be viable when you get out far enough, but you also then look at, well, that's 4 or 5 times the number of qubits that we currently have.

Rick Altherr
How easy is it gonna be for me to get there?

Stephen Kraig
So in your opinion, you think we're pretty far off from quantum computing being in our everyday life?

Rick Altherr
Oh, I don't know how much we will ever get to everyday life. Like I said, it's kinda like g if you think of a quantum computer as more like a quantum accelerator. Right? It's similar to a GPU and then it's only useful for certain types of problems. How often are those problems gonna show up in your day to day life?

Rick Altherr
For everybody, I kinda doubt we're gonna be doing, you know, physics simulations constantly. And and I don't mean, like, the the video game physics simulations. Right? Like

Parker Dillmann
I want my bridge simulator to be perfect. Right. It's so incredible. Space program running with physics simulations on a quantum computer?

Rick Altherr
We gotta make doom run on the quantum computer. Right? That's the whole

Parker Dillmann
That's that's the first step. Right?

Rick Altherr
Right. But the like, the question about when does it get to everybody needs a quantum computer, part of the problem is, is there a problem that everyone faces that would even be in where are quantum computers even relevant? And we don't know. We just don't know.

Stephen Kraig
Pretty early on in the r and d phase, it seems like. And and and it seems like seems like this technology is moving quickly but in the grand scheme of things on like a consumer level, quite slow.

Rick Altherr
Well, with any sort of technology development like this, I mean, if you go back and we we look at the development of digital computers through the the lens of history. Right? And if you go back to that time, did anyone know that a stored program computer was gonna be possible? And, like, how fast was it gonna take you know, were they gonna be able to develop such a thing? It's that same level of uncertainty.

Rick Altherr
There's gonna be some series of breakthroughs and developments that make it, you know, not only possible to build the machine, but that it actually becomes commercially viable? How long did it take to go from those very early systems to buying one to put in your business to run,

Parker Dillmann
you know Point of sales.

Rick Altherr
Yeah. Well, I mean, even before that, right, where they were, like, specialist applications that needed that computation power. Right? Like, it went through phases, and there's no reason to to expect that it'll be any different for quantum computing. But it also is we can't expect that the time of development is gonna be the same either, because it relies upon fundamental science breakthroughs.

Stephen Kraig
Yeah. It seems like there's a lot of, a lot of the environmental issues would have to be resolved before it becomes something that is in everyday use. In other words, people are not gonna have a vacuum tank that gets to cryo temperatures just to have quantum computing in their kitchen.

Rick Altherr
Right? Right. It's not gonna be on a wristwatch.

Parker Dillmann
Right.

Rick Altherr
You know, it's like but, we are moving to the point where there are multiple quantum computer manufacturers who are selling the machines that are able to be deployed in slightly modified data center locations. Right? Like, it's it's getting to a point where they're more self contained systems. Certainly, we're talking, like, 6, 7, 8, 19 inch racks. Right?

Rick Altherr
Like, these are not small systems. But if you again, go back to classical computing when he was, like, how big was UNIVAC? Right? So there's definitely gonna be a question of how fast does this progress in terms of building up scaling up the capability of the system and also the the miniaturization of the system. And we just don't know.

Rick Altherr
I mean, has there ever been a real demand for microscopic cryogenic systems? I I can't think of 1.

Stephen Kraig
Yeah. There there is a there is a manufacturer here in town in Denver that, that that makes them and they're they're doing the fridge style, and I think their smallest one is is, you know, generally the size of a of a small car. Yeah. So we're at that stage.

Rick Altherr
Well, in in a couple of years ago for a trapped ion system, it would have been well, first, you start with 2, 4 by 10 foot optical benches. Right? And then your cryo system, and then and so yeah. And these already have been making a lot of strides in terms of miniaturization, but there's a long way to go.

Parker Dillmann
Do we wanna wrap up? Yeah.

Stephen Kraig
I think so. This has been really, really fascinating, and I'm sure we could go for quite a while longer but we really appreciate you coming on, Rick.

Rick Altherr
Yeah, it's been my pleasure. So so

Parker Dillmann
Rick, where can our listeners get in touch you if you, if they want to talk about quantum computers?

Rick Altherr
So I am on mastodon@mxshift@social.treehouse dot system, I think. Yeah. It's dot systems. That's, where I'm usually at on the social media. I also actually offer, free mentoring and resume review and and other things.

Rick Altherr
And so I have a a Calendly, calendly.com/mxshift, where you can sign up for a slot for mock interviews or have me look through your resume and give feedback and those kinds of things. Very cool.

Parker Dillmann
Awesome. We'll put that in the show notes. So thank you so much Rick for, talking about more of the quantum computing actually like physically works versus, I guess we did talk about some theory, but not too much.

Rick Altherr
Well, I mean, you wanna know about the electronic side. Right? That's the whole point.

Parker Dillmann
Yeah. Yeah. I I'm just so fascinated that it's like another level down the I can't see stuff, hole. So, so thank you so much everyone for listening to circuit break from MacroFab. We are your hosts Parker Dolman.

Stephen Kraig
And Stephen Craig. Thank you so much Rick.

Rick Altherr
You're quite welcome.

Parker Dillmann
Alright. I'm gonna do the outro. Breaker for downloading our podcast. Tell your friends and coworkers about circuit break podcast from MacroFab. If you have a cool idea, project, or topic, or you want to talk about quantum computers, because apparently that's what we do now, let Steven and I and the community of Breakers know and then talk to Rick as well.

Parker Dillmann
Our community where you can find personal projects, discussions about the podcast, and engineering topics and news is located atform.macfab.com.