Quantum computing in 2025: From sci-fi to real-world solutions

Keith Shaw: For many people, quantum computing has been this theoretical, far-off science fiction concept. But guess what? In 2025, the conversation has now shifted — and it’s not just a future thing — it’s happening now.

On this episode of Today in Tech, we're going to dive into the world of quantum computing and how it's evolving from complex theory into real-world business transformation. Hi everybody, welcome to Today in Tech, I'm Keith Shaw.

Joining me on the show today is Murray Thom, Vice President of Quantum Technology Evangelism at D-Wave Quantum. Welcome to the show, Murray.

Murray Thom: Hey Keith. Thanks for having me.

Keith: All right, so Murray, you've got a hard job here, because I'm pretty skeptical about the world of quantum computing. But if I was going to talk to anyone about this technology, it would be a quantum evangelist. So, yeah — you’ve got your work cut out for you.

Murray: You've come to the right place.

Keith Shaw: All right, so just as a quick icebreaker — do you remember the first time you realized that quantum computing wasn’t just theoretical? That it could actually start changing things? Because the theory’s been around for a while, right?

Murray Thom: Yeah, I do remember that moment. It was actually a phone call I had with my father.

He worked in the Canadian government in economic policy, and one of his friends said, “Hey, your son is in engineering physics — maybe he’d be interested in hearing about quantum computing.” The context is that when you're taking physics classes and learning about quantum mechanics, it feels a bit like that scene in The Matrix — when Morpheus is talking to Neo and says, “You feel a little like Alice tumbling down the rabbit hole.” That’s how strange quantum mechanics is when it’s first described, because it’s so different from our everyday lives.

But having learned how those systems behave, when someone said, “You can use those principles to help process information more quickly,” that made sense to me. When I started working to build quantum computers and commercialize them, I wasn’t sure we’d be able to do it.

But around 2015, we had a moment — our chief theorist was presenting about what was happening at the shortest timescales in our quantum systems. That’s when I thought: “Okay, this is going to happen.”

Keith: So has the definition of quantum computing changed over the last 20 years? Or is the concept still the same? Because it feels like people still don’t understand it. Maybe that's because most of us grew up learning how classical computers work — on or off, one or zero.

But with quantum, it's like, “It can be both?” Is that where the confusion starts?

Murray: I don’t think the definition has changed, but I do think we’re getting better at explaining it. Although, to be honest, a lot of folks in the industry still make it more complicated than it needs to be.

Keith: That might be true for a lot of technology. Murray: Exactly.

Quantum computing is, in its simplest form, energy-efficient computing for hard problems. It uses quantum effects to perform calculations that classical computers either can't do fast enough or can't do at all.

And while we’re still developing an intuition for how quantum effects behave, to a computer, it’s just another resource — one it can use to calculate solutions faster.

Keith: So when you talk to people in the tech world — not just in the quantum space — what are the biggest misconceptions you still hear? Is it like what I said earlier, where people think quantum is still just science fiction?

Murray: That’s definitely one of them. And honestly, we technologists are to blame. When we explain how quantum works, we get into all these counterintuitive concepts that sound bizarre and intimidating. But the biggest misconception? That we’re still waiting for quantum computers to exist. They already exist.

They’re being used right now by real businesses to solve real problems. This isn’t a single moment in time — it’s a spectrum of rapid development. There are applications today, and there are even more on the horizon.

Things like workforce scheduling, logistics routing, and production scheduling — those are all happening now. And in other areas — materials simulation, AI, and quantum chemistry — we’re making real progress toward those use cases too.

Keith: Looking back over the past 12 to 18 months, what’s changed? Has it been improvements in hardware, or are we seeing new software applications? I remember with robotics and automation, there was a “perfect storm” of lower costs and new tech. Is something similar happening in quantum?

Murray: Yeah, in the past year or so, we’ve seen major breakthroughs — especially around error correction. We now have high-quality demonstrations of error correction inside quantum systems, which is a huge deal. But even more impactful, people are actually using quantum computers productively.

For example, there's a company in Western Canada called Patterson Food Group. They own 13 grocery brands, and they’re using quantum apps to schedule their delivery drivers and in-store workforce.

So yes — when you order a five-pound bag of M&Ms, quantum computing might be involved in getting that to your door. Also, we’ve seen side-by-side comparisons where quantum systems solve problems faster than classical ones.

In March of this year, quantum machines simulated magnetic materials in 20 minutes — something that would take a supercomputer almost a million years to compute. Keith: Wow.

So is it fair to say that 2025 might be the tipping point for quantum computing? Or are we still on a gradual path where it’s just going to grow slowly, over time?

Murray: That’s a great question. What I’ve noticed is that when people have those “aha!” moments with a new technology, it’s usually when something incredibly powerful becomes incredibly easy to use. That’s what’s happening with quantum computing right now.

The systems are becoming dramatically more powerful, while the barriers to entry are dropping fast. So when I talk to executives and business leaders, and they realize there are no blockers — no barriers to getting started — that triggers a cascading effect.

More people adopt it, more use cases emerge, and suddenly, we’re in this moment where everyone looks around and says, “Wait… quantum is here.”

Keith: You mentioned real-world applications earlier. Can we dive deeper into those? What industries are seeing traction with quantum right now? And when these companies choose to adopt quantum, is it because the problem is too complex for traditional computing?  Murray: Exactly.

What draws businesses in is the complexity they’re facing. They’re running into challenges that require a lot of resources, take too long to solve, and cost too much. It’s not just cryptography anymore.

That was the original “killer app” for quantum, but now we’re seeing a much more horizontal set of use cases — stuff like workforce scheduling, logistics, production planning. These are challenges every business faces.

For example, figuring out the most fair and efficient way to assign 500 deliveries to 30 drivers — that’s a hard optimization problem. Ford, for example, had issues scheduling their Ford Transit production line. It took them about 30 minutes to replan everything.

With a quantum hybrid solution, that dropped to 5 minutes. That kind of agility has huge business impact.

Keith: So it's not just about complexity — it’s also about speed? Murray: Right.

People often ask what kind of problems are a good fit for quantum computing. Here’s an example from energy grid optimization. Traditionally, you had one power plant and transmission lines going to homes and factories. As demand increased, you just ramped up power.

Classical computers handle that easily because the decisions are isolated. But today’s energy grids are very different. We’ve got solar panels on houses, electric vehicles that can charge and discharge, and decentralized energy sources all over the place. Now, every decision affects everything else.

Do I draw power from here or push it to the grid? That interdependency creates combinatorial complexity. Quantum computers are really good at that kind of optimization — where you’re making binary decisions in a tightly connected system.

Keith: Is there a real-time component to this too? Like if a delivery driver calls in sick or there’s a traffic jam, can quantum systems respond to that dynamically?

Murray: Yes, and that’s huge. Problems that used to take hours to solve can now be handled in minutes. That opens up all kinds of possibilities. Take Patterson Food Group again. Their in-store scheduling used to take 25 hours. Now it takes 2 minutes.

That means they can wait until the last possible moment to run the schedule — so it’s based on the most up-to-date information. If something changes? Just rerun it. Plus, businesses can now run “what if” scenarios. Do I need more part-timers? Could I shift more hours to weekends?

That kind of flexibility wasn’t feasible before.

Keith: We’ve talked a lot about logistics and scheduling, but what about industries like finance or pharmaceuticals? Are you seeing activity there? Murray: Absolutely.

In finance, for instance, a lot of folks are already optimizing portfolios using something called a covariance matrix — that’s actually the same structure as the core instruction set for our quantum computer. So when we tell them, “This is what we solve, with binary decisions,” it clicks immediately.

They also use it for risk modeling and fraud detection. It fits naturally with machine learning techniques too. On the life sciences side, you don’t just simulate molecules for drug discovery — you also have to optimize clinical trials.

For example, deciding how many patients you need from which regions to get a statistically strong result. That’s an optimization task. We’ve even seen companies designing protein-based treatments using hybrid approaches. They’re using quantum to accelerate parts of that process.

In fact, one project went all the way to live virus testing against COVID-19.

Keith: That’s amazing. Now I have to ask — how is AI influencing the quantum space? There’s been this big boom in generative AI over the last few years. Is there a convergence happening? Murray: Definitely.

These are highly compatible technologies. Most businesses are already using AI to increase productivity — whether it’s drafting documents or processing information. But even at the technical level, there’s overlap. When I say “restricted Boltzmann machine,” anyone in machine learning knows what that is.

And that’s the kind of problem our quantum computers are good at solving. There’s promising research showing that quantum methods can reduce training time for AI models. TRIUMF, the Canadian particle accelerator center, is using quantum for better data analysis in their particle detection work.

Japan Tobacco’s pharmaceutical division is building a quantum-enhanced language model framework to help discover new molecules. Also, remember that simulation we mentioned earlier — the one that would take a supercomputer a million years? That much computation would have consumed as much electricity as the world uses in a year.

Our quantum system did it using less than a dollar’s worth of electricity. That has huge implications for the sustainability of AI in data centers.

Keith: That brings up another question — where are these quantum computers actually located? I’m guessing most companies aren’t buying their own just yet? Murray: Right.

There are many types of quantum computers out there — some are very experimental, like lasers on optics tables. But the more mature systems look like regular compute units. They're self-contained, and they don’t need huge amounts of power or cooling like classical data centers.

We’ve got systems running in office buildings and research facilities. One is at USC’s Information Sciences Institute in Los Angeles. Another is going up in Huntsville, Alabama. The important part is, most companies don’t need to own the hardware — they access it via the cloud.

Our systems are cloud-accessible in 42 countries with sub-second response times and 99.9% reliability. It’s enterprise-grade and fully secure. So businesses can start building quantum-powered applications today, without needing to worry about the physical machine.

Keith: So, as we move forward, will quantum eventually replace classical computing? Or will they coexist, with each one tackling different types of problems?

Murray: Quantum won’t replace classical computing — they’re complementary. Classical systems are incredibly good at what they do. They break problems into additions and multiplications, and they’re extremely efficient when the task fits that model. But quantum computers use different instructions.

Where classical machines struggle — especially with energy-intensive or combinatorially complex problems — that’s where quantum steps in. We’re already seeing it happen in grocery logistics, in telecom, in financial modeling. For example, NTT Docomo in Japan used quantum optimization to manage which cell towers handle which users.

That reduced their network traffic by 15%. They’re now deploying that across 250,000 base stations. So yeah — it’s already part of the IT fabric.

Keith: So if a company wants to get started, what do they need to do? Hire a bunch of quantum physicists?

Murray: That’s the best part — you don’t need to know quantum mechanics to benefit from quantum computing. The tools are open source, and the stack is layered. Physicists work at the machine level. Mathematicians can engage at the algorithmic layer. Developers work at the application layer.

For a business, it’s actually about reducing complexity. If someone’s trying to make your business more complicated with quantum, they’re doing it wrong. A lot of companies start by working with a vendor — like us — to solve a specific problem. For example, store scheduling.

We build the first solution. It’s open source, so they keep it. Then, they often build the second solution themselves. It becomes a very lightweight collaboration from that point on.

Keith: What kinds of talent and skills are in high demand right now? Is this still a PhD-only space, or can a developer or data scientist pick this up?

Murray: Honestly, if you know Python, have access to your business data, and remember some high school math — you can start building quantum-powered apps. People with computer science backgrounds are actually ideal at this stage. They’re trained to apply known methods to new problems.

Researchers are more focused on exploring unknowns, which can be slower. So if you’re building a team, you probably want developers first, not just PhDs.

Keith: Is there a global race in quantum computing, like we’ve seen with AI and space exploration? Murray: Yes, absolutely.

At the academic level, there’s a lot of collaboration. Universities and research centers are sharing knowledge to move the field forward. But at the national level, there’s fierce competition. China is investing about $15 billion in quantum. The U.S. is in the $4–5 billion range.

Countries are trying to define their role in the global quantum supply chain — just like they did with chips and semiconductors.

Keith: Okay, I know this might be a weird question — but could we ever see quantum computers in every home? Like how personal computers used to be unimaginable?

Murray: That’s not as far-fetched as it sounds. If you look at history, the smartphone in your pocket is more powerful than the world’s top supercomputer from the 1980s. So yes, there’s potential.

Right now, many quantum systems need cryogenics or operate in lab conditions, but we’re already working on smaller, more practical models. Some room-temperature quantum computers exist, though they’re less mature. Miniaturization is definitely an engineering challenge we’re actively exploring.

That said, there’s no real need for home quantum computers right now. Cloud access works great because the problems quantum solves are very data-light — you're uploading a compact problem, running a complex optimization, and getting a small result back. The cloud model makes that seamless.

Keith: Is there a dream application or breakthrough you really want to see happen with quantum computing? Murray: Definitely.

While I get excited about the long-term stuff — climate modeling, Mars missions, curing cancer — I’m also excited by near-term breakthroughs. In life sciences, there are optimization problems related to patient treatment planning or molecule discovery that quantum could accelerate soon.

If we can reduce the time it takes to identify the right treatment for someone, that’s potentially life-saving. That would make all the years I’ve spent in this field feel so worthwhile.

Keith: All right, I’m going to get a little nerdy. Did you ever see Avengers: Infinity War? Murray: Oh yeah.

Keith: So, there’s that moment where Doctor Strange uses the Time Stone to simulate 14 million possible futures to find the one way they beat Thanos. Isn’t that kind of what quantum computing is like?

Murray: You know what? That’s actually a great analogy. Sometimes quantum computing helps us find answers faster. But often, it's more about searching through incredible complexity — exploring massive solution spaces in ways classical computers simply can't.

Like Strange, the quantum computer is evaluating possibilities that would otherwise be impossible to compute in a practical time. It’s pulling out high-quality solutions from what feels like an impossible universe of options. So yes — it does feel a bit magical at times.

Keith: You don’t have to just call it “movie magic” anymore — it’s becoming real science. Oh — and another thought: during hurricane season, we see all those spaghetti-strand models showing different storm paths. Could quantum help improve that kind of forecasting? Murray: Absolutely.

Weather modeling involves simulating fluid dynamics and interpreting tons of messy data. That’s a long-term goal for quantum simulation. But even in the near term, quantum-enhanced machine learning could help detect patterns — say, identifying when a tornado is about to form — sooner and more accurately.

We’re already making progress on fluid simulations with gate-model systems, and we hope to reach commercial viability for that within the next 7 to 15 years.

Keith: All right, Murray. I’m definitely having you back when we do the “Are we living in the Matrix?” episode.

Murray: Count me in!

Keith: Murray Thom, thanks again for joining us — fascinating stuff. I’m definitely more optimistic about quantum computing than I was at the start of the show. So, you did your job. Murray: Excellent.

Thanks, Keith. Appreciate being here.

Keith: And that’s going to do it for this week’s episode of Today in Tech. Be sure to like the video, subscribe to the channel, and leave your thoughts in the comments. Join us every week for more great conversations. I’m Keith Shaw — thanks for watching.