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Asit Sharma: Sure, Mary so. The promise of quantum computing, I think is extremely simple. It's to solve intensely complex, large scale problems with a lot of variables and dependencies that classical computers would struggle to solve. While it sounds like it's all powerful, quantum computing isn't going to be applied to simple problems to solve them faster, but think really difficult things like predicting the weather. This is just one I'm sure we're going to talk about some more. This is something that classical computers have a lot of trouble with because they solve things linearly one step at a time, and a quantum computer promises to be able to process in parallel. This is the essential premise and the promise of this technology.

Mary Long: How then does the promise? If the promise is solving really, really complex problems fast, how is that different from the promise of really souped up artificial intelligence? Should we be thinking of quantum computing as a hyperextension of artificial intelligence, or is it doing something differently?

Asit Sharma: Mary, that's such an insightful question because if AI is so powerful, why couldn't we just use AI to eventually get to these large scale problems? The answer is pretty interesting. Even though, like, artificial intelligence, generative AI has its mysteries, we don't know exactly how the black box works when we're talking about generative AI and large language models, neural networks, etc, that is still based on classical computing concepts. Everything running through a computer, including AI computation is based on this very discrete binary system of zeros and ones added together. Everything we do on a computer, a classical computer is based on something that's totally deterministic. I get a zero and a one. I can understand that, that makes sense to me. Quantum computing is a little different in that it takes a concept from quantum mechanics, which is a little strange to us, and it's one of the mysteries that underlines our physical existence. It's a mystery of the universe.

But it's based on the idea that a single particle, no matter how small, exhibits both the properties of a particle and a wave at the same time. For those of you who are starting to remember your high school or college physics, yes, this is behind some of those principles like the Heisenberg Uncertainty Principle. If you've heard of Schrodinger's cat, this principle is also associated with that. But if a particle can exist as one discrete thing, but also a wave function at the same time, it means that we can trap that particle and have it sit in a state of what's called superposition. A state of superposition means that that particle will encompass all the possibilities or probabilities of being a zero or a one or somewhere in between at the same time. Now, when we actually measure the state of that particle, that waveform or wave function collapses, and it's either a zero or one. Quantum computing is a way to look at something that could be any number of possibilities, and the idea of a quantum computer is to start to direct the way that wave function collapses in a way that's going to sort of show us the results we want that will be more probabilistically aligned with what we want to see and this leads to the ability to look at many, many, many, many possibilities at one time. Instead of going in through this linear computation, we're going through multiple paths at once, and that's why it's so powerful.

Mary Long: What's an example of a problem that a quantum computer could theoretically solve, that a classical computer couldn't?

Asit Sharma: When we think about the healthcare industry, we've often heard in the past couple of years how AI is very suited to solve problems of drug discovery and searching for certain molecules. I think that's very true. I think that the transformer model of generative AI is very suited to looking for patterns in sequential pieces of data. But you can only do so much with that. There's a problem in drug discovery, which is how a drug molecule will bind to a protein's active site. It seems simple, but proteins are really flexible. They're not rigid, so you have so many possibilities of how what's called a protein ligand can bind to that site because they keep moving. Also, here you've got interactions at micro scale going on within a protein. Classical computing can only do so much when you're trying to figure out this one problem. But a quantum computer could really scale up and look at all the possibilities, and in that sense, we can get a better understanding of this binding from molecule to protein, much, much more extreme computation than we could ever do with a classical computer.

Mary Long: That's a helpful framework and illustration because I feel like often when we talk about quantum computing and its promise and what it could lead to, the answer so often lies in speed. Oh, it can solve these really complex problems really, really fast. I hear far less about the actual that might be very cool if you're a scientist or if you're a physicist and you live within this theoretical realm. But it can be difficult to connect that theoretical nuts and bolts piping to, like, actually what that means for consumers and everyday people that aren't using the technology, but are recipients of others using that technology. When you think about, like, practical goods that could come from quantum computing technology, what are those goods? Is it, Hey, better healthcare products, better healthcare technologies, or does the answer maybe extend beyond that industry as well?

Asit Sharma: Sure, there are different industries that can benefit. One, surprisingly, is just, like, the delivery of goods, so let's stay on goods, products and stuff for a second. One of the hardest problems to solve is complicated logistics. If you're Amazon.com, and you've got all these warehouses across the United States and thousands of trucks, millions of people to make deliveries to optimizing that network so that each driver can make the most efficient delivery and also have the most fuel efficient route is incredibly hard. Then when you scale that problem up to goods being shipped into the country and then taken to these warehouses, etc, that becomes exponentially harder as the problem scales. One of the uses that quantum computing may be very good at in the future is quickly solving some of this stuff, so we optimize routes and we optimize energy usage. There's a way we get a double win, the double win being you get your packages faster, Mary, and it's also more fuel efficient so we're doing less harm to the planet. That's just one example. In my world in investing, there's the idea of trying to understand risk across many, many companies or even within a single company. The most common expression that we have is the idea of portfolio optimization. If you've got a portfolio of 100 stocks, which I'm sorry to say I do.

I'm not saying that I follow everyone to the Nth degree. I've got a handful that are most important to me, but I do have 100 or so companies that I own at least slices of being able to optimize the portfolio that I have for the best risk return proposition for myself is combinatorilly really difficult. A classical computer, again, just fails at doing a great job at this, but this is one of the things that quantum computing should be good at once we get to that desired state of this being very robust.

Mary Long: I'm going to hone in on a word you just use should be very good at. So much of this conversation has been focused on what might happen in the future, should we unlock the full potential of this technology. But there are companies today that are already experimenting with and trying to develop this. What is the state of quantum computing today right now as we're talking?

Asit Sharma: Right now, we have some fledgling quantum computers that have been built and are functional. They are mostly being used by research institutions, but we're starting to see Fortune 500 companies utilize these computers in very basic types of experimental computations, mostly through renting out on a model where you're getting some time on a company's quantum computer and running some quantum algorithms, some computations or even simulating a quantum algorithm. And we've got subsets of problems that are being solved. And this is because the quantum computers of today, while they show a lot of promise, they're subject to certain problems of the quantum realm. One of those is the problem of decoherence, so you have to have extremely stable states that are isolated from normal environments for quantum computing to work. It's very prone to error.

A lot of energy is going into solving for the errors in quantum computations. Because remember, here, we're not really dealing as much with just math and binary computation. We're actually in these computers isolating particles and using the particles to solve the problems. We have to keep those in a state that remains in superposition, meaning that it's got that state of lots of probabilities if we're trapping one ion and asking it to perform computations. We don't want it to interact with the environment. That can make that function collapse more quickly, and we won't get the desired results. This is a long way of saying that right now things are still at an experimental state, but we have reached a stage where companies are solving some smaller real world problems. And we've got some bio pharmaceutical companies that are experimenting around with this. We've got some industrial companies that are doing so as well.

Mary Long: Jensen Wong has predicted that a very useful quantum computer is about 15-30 years away, 15-30 is quite the range. You just outlined some of the problems that we have in scaling quantum computers. What needs to happen for a very useful quantum computer to become a reality? When we get that, is it just, Hey, rather than solving simple problems that today's quantum computers are solving, we can solve more complex problems, or is it, hey, we've answered all these questions about how do you avoid decoherence? We're using less energy to actually make these computers operate. Is the usefulness of these quantum computers that Jensen's predicting? Is it that they're more useful because they can solve more difficult problems or that they're more useful because we don't have to answer all these other questions that are tied into the realm now?

Asit Sharma: There's a lot to unpack with what Jensen Wong said, Mary. We're going to start on the personal level. I had just spent many weeks in a deep dive in quantum computing, bothering some of my friends who have a scientific background, talking a lot to large language models, trying to dust off old, like math from years gone by in my head. I was so angry when he said that. I'm like, Dude, I just spent all this time. Are you telling me this is 30 years away? Come to find that Jensen walked that back.

Considerably. He actually said at Nvidia's GTC conference in March, they had a quantum day that when he talked to his senior engineers, they said, probabilistically, it's going to happen sooner. It could be less than 20 years. It could be less than 15 years, and he invited some of the CEOs of the small companies that have built quantum computers onto the stage to explain to the audience why he was wrong to say that. In fact, when Jensen Huang made his prediction, he confessed later he didn't realize that there were publicly traded companies that were building quantum computers. Part of this is a very brilliant guy who's thought a lot about the quantum realm and the physics of how we compute for a long time, just being too busy to surface and look at what the state of things where it stands today. But he's right in that the timescale is not going to be 2026. I personally think it'll be somewhere between 5-10 years from today to reach what he's talking about.

What Jensen Huang and other people are pointing at are a few things. I think the most useful place we can reach is something called quantum advantage. That's when a quantum computer is going to be able to perform a task better, so more efficiently, more accurately than the best alternative we have that exists in a classical computer for a useful problem, for a practical problem, and they can do that with regularity. When that occurs, I think many people will say we've reached a point where we have an inflection or a tipping point, maybe like a ChatGPT moment. There's also something else people talk about which is quantum supremacy, and this is the point at which a quantum computer is going to perform computations that any classical computer couldn't solve in a very reasonable point in time. We have some theoretical examples already. There's a company called Rigetti Computing, which claims to have done so. We have IBM and Alphabet, which also have their versions of reaching quantum supremacy. But these aren't really large-scale examples or even easy-to-understand examples.

They're in each case, very abstruse. What we need to happen, really, the thrust of your question is, it's cool. When we think about our computers, we've all heard about bits and bytes. The primary computational unit in quantum computing is called a qubit, and that often is where you are using one particle and you are performing operations on that particle to get that probabilistic result. This is how the problem gets solved. You're doing it at, again, a state which nature can't really interfere. You're freezing this to near absolute zero in this big cryogenic machine. We have to be able to have qubits scale up. Many qubits, which are already prone to errors to be able to have those solve problems and then communicate with each other through a process called entanglement. Entanglement itself is really crazy. This is a way that certain particles are connected to each other with information. No matter how far you separate them, one could be here where I am.

Mary, one could be across the country where you're sitting as we're recording, and they are simultaneously exchanging information. This is a property of the particle realm. Einstein called this spooky action at a distance. Entanglement is cool, but we can use entanglement to do computation. We can influence through certain quantum algorithms. When we take one qubit and then add another and add another and then start scaling that system up and also solve for error correction, that's what needs to happen to reach these stages, what we call quantum advantage or quantum supremacy, where we'll hit that inflection point and suddenly we're going to see the meme stocks, and we're going to see a lot of, I'm going to call them quantum bros in advance, talking about where you should be investing.

Mary Long: Let's talk about some of the companies that are already working on this technology where investors that are interested in this might look if they want to ride this wave. On one side, we'll start here, you've got what I'm going to call the big dogs, and there's probably nobody that's going to dispute that description. They're basically the big-pocketed names that listeners are already familiar with. You've got IBM, Microsoft, and Google. These companies are building their own quantum computers from the ground up. They're also building out all the supporting technology that powers these systems. Asit, how different are the systems that these three companies are building? What distinguishes them from each other?

Asit Sharma: I think Alphabet, parent of Google, so I'll just stick with Alphabet for now, has a pretty similar approach to IBM. Alphabet has what's called a superconducting qubit. Basically, it's a way that you can do quantum calculations in a manner that's similar or analogous to how semiconductors operate. You're taking a superconducting chip, basically and using circuit elements that are very small, decreasing that temperature to near zero, and then having operations performed on it. You've got minuscule electric circuits that are etched into silicon, and their latest chip is called Willow. Willow had some breakthroughs in reduction of error rates, and it was interesting because Alphabet was showing that it was exponentially decreasing errors as the system scaled up. There's a lot of promise there. They're also developing the software that goes along with this. They have their own version of the QPU, or the quantum processing unit. Basically a full stack under development at Alphabet, and IBM has something very similar. They have a processor called the Heron quantum processor. Not quite easy to make a very similar comparison to Willow. But essentially, it's a processor that is also running calculations with multiple qubits and reducing error rates.

Both of these companies are chasing the superconducting processor-based solutions, and then we've got Microsoft, which is doing something that's pretty crazy. Microsoft got fascinated, or its engineers got fascinated with a type of approach, which is very theoretical. It's based on encoding information in the physical properties of the particles themselves rather than measuring those particles. To do this, Microsoft had to prove the existence of what had been a theoretical particle called the Moderna Fermion, in order to make this system work. They seem to have done so. They've been putting out papers for a few years now, and it does look like they've proven the existence of this particle. But in doing so, their approach is more fault-tolerant than the other approaches I've described because you're really looking at the way the particle is shaped. There's a really common analogy here that some scientists have used to explain it to folks like me who don't get the math and physics of it, which is you think of a braided knot. As that knot starts to free from whatever external force, the knot itself remains. The coded information is very stable, and so it's taking an approach which is based on design. In some ways, you can think of it that way. These are three different approaches. Again, the first two are pretty similar. The third is out of left field, and I admire Microsoft for its ambition and the investments of however 100 millions they made. Just to take a really theoretical approach because it could have ended up where they got to the very last experiment and came back to top brass and said, Oh, guess what? That particle, we really can't prove its existence, and this approach doesn't work. Hundreds of millions of dollars later, we're going back to the drawing boards. I really admire Microsoft's ambition in that sense.

Mary Long: Yeah, to try to build out a full-stack quantum computer is no doubt a very expensive endeavor. We're going to talk a bit more about specialized builders in a bit, but apart from those three big dogs that you just walked us through, is anybody else currently working on building out a full-stack quantum computer right now?

Asit Sharma: Sure, there's a company called IonQ, which I still would characterize them as proof of concept, and maybe they're not a complete full-stack, but they have a computer which uses a trapped ion approach. Trapping ions, it's a very stable way to do quantum computing calculations, and theirs is it lends itself to a full stack approach. But to get to where it has reached, it really had to work on both the hardware and software systems. We've got that. We've also got a company called Quantinuum which is pretty interesting. Quantinuum is actually a joint venture between a company, it's very small company out of the UK, which merged up in this joint venture with the giant Honeywell computing. Honeywell's joint venture, now commonly referred to as Quantinuum. Is also full-stack approach in that it also has really a host platform, as IBM does. I didn't mention IBM has an operational system and a software language called Qiskit, so it enables customers to come use IBM's quantum computing approach through a Cloud interface, and Quantinuum has that as well.

Mary Long: I'm glad you mentioned Cloud interface because there's another way that big dogs are playing in this quantum space without having to tackle building out their own full-stack computer. Amazon is taking a totally different approach than the likes of Microsoft, Alphabet, and IBM. Rather than building out this full-stack computer, they're providing quantum computing access through its Cloud platform. It's like AWS, but for quantum. What's the advantage, Asit, of this approach over that which Google, Microsoft, and IBM are pursuing?

Asit Sharma: The advantage is interesting because it's an advantage that Amazon has exploited with Amazon Web Services through many iterations. Just look at their AI platform. Mary, they are agnostic into what technology they offer. When Deep Seek comes along, they can offer that via AWS and their AI Cloud. They offer many large language models, but you're going to rent space from Amazon because they can give that inference to you at a pretty good cost. Braket is the Amazon Web Services of quantum computing, and they are helping the whole ecosystem by allowing smaller companies like a company called Querra, like IonQ, which I've just mentioned, host their quantum computers on Amazon site. You can go to Amazon Braket, which by the way, is named after a notation system in quantum mechanics created by a physicist, Paul Dirac. It's a fun name because it almost sounds like Braket, but Braket is a punny name for this. It's using its might and its Cloud platform to further the industry along. But if one of these smaller companies really takes off? Well, they're already on Amazon's platform, and that will help Amazon scale up its quantum business. That's the advantage. What disadvantage, I guess you're going to ask me next, is, shouldn't you be out there trying to make your own quantum computer? What happens if Microsoft and IBM and Alphabet come with these major breakthroughs? I was a skeptic about this, Mary, when I first started examining Amazon's approach, and I was like, this is great.

They can have AWS for quantum computing, but maybe the bigger money is going to be for those that develop the actual quantum computer. But something interesting is happening along the way in that Amazon is hosting the actual cryogenic systems for some of these other companies. It's getting a first-hand view of what it takes to build these computers to operate them, and it's also developing some very interesting add-ons to its ecosystem. It's developed a quantum computing chip now in prototype phase called Ocelot, and that is aimed solely at reducing errors. It's trying to reduce quantum error corrections. When you can do this, then you automatically become a platform that companies developing their own systems want to work with. Because they have to sell that downstream to customers, and a more efficient system is a cheaper system. I think they're learning a lot about the entire quantum computer system, and it might not be too much of a lift for them in the future to jump back in.

Mary Long: We've talked about these big pocketed companies that are experimenting and trying to build a foundation in the quantum space. But it's not just MAG seven companies that are doing this. There's also a lot of little players who operate in different niches of the quantum industry, IonQ and Rigetti Computing. Those are both companies that develop quantum systems that can work with and link up to the Cloud. You just walked us through Amazon's Brackett system. Obviously, Amazon has the name Amazon behind it, and with that, a larger scope. But is what Ion Q and Righetti, what those companies are doing different than Brackett, or are they all playing in the same space?

Asit Sharma: Each has a different approach. There's a company called D-Wave Quantum, which we haven't mentioned yet, but was just in the news in the past few months, one for apparently having a breakthrough in quantum computing, where it reached quantum supremacy, but also for selling a quantum computer to a German research institution. This is a company that has an approach called annealing, and that's basically working mostly in logistics type applications. You're focused on math type problems. D-Wave quantum has its approach this annealing, it fits right into Baquet. Ion Q, as I mentioned before, trapping ions, it goes right up into Amazon Baquete.

For Rigetti computing, also, they have a more modular approach. Instead of trying to do this big full stack exercise or build only quantum computers, they want to build modules that can be used by customers. They are also on Baquet then there's another company that I have my eye on called Qa Computing. They're small, they're privately held. I think they're going to go public in a few years. Alphabet and Softbank and some other investors just invested $230 million in Q. But Qa has its own approach, and it has a neutral atom quantum computer, again, available on Amazon web services. If you think about it, all these different approaches on the particle level, can be used. If you can just build a pipe to your customer, then the customer can make their own quantum algorithms and try to solve their own problems via Amazon web services. To answer your question, they're each a different type of system, but they're all building that pathway so that they're available for people who want to do simulations and run algorithms or design their own to use on these computers on this one platform.

Mary Long: What about chipmakers and chip designers? How do they fit into this future quantum landscape or what foundations are they building today to one day fit into the quantum landscape?

Asit Sharma: The chipmakers right now are mostly looking at how their applications will be able to accelerate the development of quantum computing and I know you've got a question or two on that. Let's talk about the chip designers for a second. Just a quick second. When we think of companies like Cadence Design Systems and Synopsis, these are the businesses that help Nvidia or AMD design chips or Intel design chips. They have a part of their business which is focused on photonics. This is using light to transmit information, and as they become better at the physics simulations, they are becoming more important on the quantum landscape because this is the next step is looking at how you can transmit information from one quantum system to another. Quantum networking is what I'm referring to, and a lot of this is based on photonics. I think both of these two companies have a bright future in quantum computing, but so much as experimental now, you're looking at timelines that could be five or ten years. But I do think both have a way to play in this, especially Synopsis, which is acquiring a company called ANSIS. ANSIS specializes in these very types of physics simulations. The chip designers, the electronic design automation companies, I think, have a role to play. Right now, they're very busy laying out strategic roadmaps of how to become better integrated into this ecosystem as it grows.

Mary Long: Zooming out and thinking about all that you've taught us today from an investing perspective, it seems to me that there's a parallel between investing in the quantum industry and investing in the biotech pharmaceutical industry. There's a lot of interesting work that's happening among the smaller players in the quantum industry and in the biotech space, but it's highly technical. As a layperson, it might be hard to distinguish between what one small upstart is doing and how that differs from another small upstart also, a lot of those small upstarts aren't profitable, so it's hard to actually make a good judgment about their financial situation and what that might look like should this largely theoretical product get off the ground in X amount of time. Instead, in the healthcare industry, rather than investing in biotech, if that feels a little bit too crazy to touch, what you can do is go to the big dogs. You can go to pharmaceutical companies.

A lot of those pharmaceutical companies have very clear cut financials. They're steady companies, and oftentimes they have partnerships with the upstarts that are doing some of the more innovative work. Again, seems to be the same situation that you've laid out here. There's big names that are solid, steady companies that have partnerships and relationships with these smaller ones. The benefit in my mind, of applying this biotech approach to the quantum industry and investing in it, that feels obvious. But what are the downsides of going that way and only keeping an eye on the big dogs rather than taking on a bit more risk and looking at the more innovative but smaller players?

Asit Sharma: The downside is that some of these smaller players may scale more quickly than would think at a first glance. I'll give you the example of IonQ I'm not saying, go out and purchase this company. I happen to own shares, but they're very decided on this idea of quantum networking. They actually acquired a quantum networking company. They want to be able to scale not just by building better fault-tolerant computers, but then to connect those even over wide distances. This is one of the things that's going to be very useful as we think about secure systems because quantum computing is going to open up a Pandora's box in all of our encryption standards are based on problems that are too hard for classical computers to solve, like the combinatorial problems of when you've got an encryption key that is very long. We never thought that anybody would be able to crack this, and that's why our whole cybersecurity industry has been built on concepts of mathematical encryption. Well, guess what? With a quantum computer, you ought to be able to crack that stuff pretty easily. Many of the companies we've mentioned, as they're discovering how to build their quantum computers. They're also already thinking about how they can thwart the day when it becomes really easy to crack the code, and you and I can never exchange any more snarky messages on signal. Not that we do, Mary, because you and I exchange snarky messages on Slack. We have no need to encrypt them, but you get my drift here.

A company that's very small like Ion Q which is thinking of building these large quantum networks over distances in addition to its own computer, has potentially a solution for a secure network that a bank could use. There is some possibility that a few of these companies, just like the little biotechs that you alluded to, could blow up. Now, could there be binary outcomes where you put your money in and it's either boom or bust? Sure. But I think at least to be able to follow this space and understand what's going on in it and where the potential lies, it's not bad to at least follow a few of the names we've mentioned, even if you're not going to invest in them, the risk is that we have what turns out to be an Nvidia because you know, 27 years ago, Jensen Wong had an idea. Now, here he is saying 15-30 years in the future, this thing could be big. It's fun if you could identify a company and you're able to hold it for 20 years. That could be right now a small puppy growing into a big dog alongside the other companies.

But I like your approach, and I think at the end of the day, it is important if you're interested in the industry to make sure you have some shares in one of the big dogs, as you call them, or even indirectly, as you said, if you like biopharmaceutical companies, I mean, Amgen, AstraZeneca are two companies I can think of that are actively trying to understand how quantum can assist their discovery, their drug discovery. It might not hurt to buy some shares in a few forward-looking companies that are embracing quantum across a few big-picture industries. Fidelity is another company that is also using quantum computing to think about the stuff I was talking about in finance before, like portfolio optimisation. Like a blend of this, companies that are embracing it, that will be customers of the big dogs, maybe one or two of the big dogs, and maybe a couple of the speculative ones whose ideas appeal to you, whose approach appeals to you. That's not a bad strategy as you're gaming this out, because it is at least five years away, and I'm one of the people I think who thinks it'll come a lot sooner than that 15-30 year initial time frame.

Mary Long: Well, of course, you think it'll come sooner, Asit, because you are, after all, visiting us from the quantum realm itself. Appreciate you jumping through so many hoops and multiple dimensions to be with us and to educate the listeners of Motley Fool Money on all that is quantum. Thanks so much, Asit.

Asit Sharma: I appreciate it, Mary. This was a lot of fun.

Ricky Mulvey: Members of Motley Fool one can check out Asit's full report on quantum computing. We'll put a link in the show notes. As always, people on the program may have interest in the stocks they talk about in the Motley Fool may have formal recommendations for or against. Not buyers sell stocks based solely on what you hear. All personal finance content follows Motley Fool editorial standards and are not approved by advertisers. Advertisements are sponsored content and provided for informational purposes only see our full advertising disclosure. Please check out our show notes. The Motley Fool Olympics products that would personally recommend to friends like you. I'm Ricky Mulvey. Thanks for listening. We'll be back on Monday.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool’s board of directors. Asit Sharma has positions in Advanced Micro Devices, Amazon, D-Wave Quantum, IonQ, Microsoft, Nvidia, and Synopsys. Mary Long has no position in any of the stocks mentioned. Ricky Mulvey has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Amgen, Cadence Design Systems, International Business Machines, Microsoft, Nvidia, and Synopsys. The Motley Fool recommends AstraZeneca Plc and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

The Promise of Quantum Computing was originally published by The Motley Fool