What Will Quantum Computers Be Good For?

And what won't they do?

Image AI-generated by author using Midjourney, January 2026

Like many of you, I connect and work with a lot of people in the quantum business, so take for granted some of the features and benefits of quantum technologies. But for those who don’t work in the field, mentioning the word “quantum” is often baffling. One of the most common questions laymen pose is “how will a Quantum Computer be different from a classical computer?” Often, the assumption is that Quantum Computers are going to just be super-powered classical computers, but that is not actually the case.

Stepping back one level, “quantum” simply refers to what happens at the tiniest of sizes. At the scale of atoms, photons and electrons, the physics is different from what we experience at our human-sized scale. At our human scale we are intuitively familiar with “Newtonian” physics and we experience force, momentum and acceleration daily. Even if we don’t know the underlying formulas, we “feel” these forces and learn how to interact with them. However, the physics are different at the quantum scale and things like “superposition” and “entanglement”, come into play. While these quantum mechanical properties are difficult to understand or visualize, we are learning how to harness them for important new technologies including Quantum Computing, and because their properties are so different, the capabilities they enable are also quite different.

A New Computing Paradigm

In prior posts I’ve highlighted that classical computers only have two input states, 1 or 0, and only three “rules” or gates of operations, NOT, AND, and OR. That’s it - a binary set of inputs and only three logic gates. Despite these sparse tools, clever programmers and ever faster processors enable your basic PC to be astonishingly powerful.

A quantum computer, leveraging quantum mechanics, has a different set of inputs, rules, and constraints. Instead of binary bits, quantum computers use “qubits”, which can be placed in a superposition meaning they can be ‘1’ or ‘0’ or some combination of both (a weighted average of the two states). In addition, qubits are 3-dimensional objects so the qubit can be oriented in any direction among those three dimensions. These 3-dimensional inputs can be manipulated by more than just the 3 rules that limit classical computing. There are at least 6 primary rules or gates that can be applied (X, Y and Z Gates, which rotate the qubit along those dimensional axes, and the Hadamard, Phase and T- Gates which essentially rotate the qubit in different ways). Plus, since qubits can be entangled, there are various multi-qubit gates as well (the “CNOT” also known as the “Controlled Not” gate being a primary example). Setting aside the details of these differences, you can appreciate that Quantum Computers will operate very differently from classical computers [For more on these concepts see “Ways to Appreciate Quantum Computing Power.”]

With this context, let’s examine how this vastly different computing paradigm will manifest for real-world applications.

“Combinatorial” Problems

Early followers of the Quantum Leap may be familiar with an example I’ve cited before to explain what a combinatorial problem is, namely the dinner seating chart. Let’s assume you are planning a dinner for 6 guests, where specific seat placement is important (mom near the kitchen, cousin Freddy shouldn’t be next to aunt Frieda, etc.). Can you guess how many different seating arrangements there are for just 6 guests? Believe it or not, there are 720 different seating arrangements! Because each guest has some correlation which each other guest, the inter-relationships are quite numerous and scale rapidy. Add just one more guest, and your 7 person table now has over 5,000 possibilities. And at just 16 guests there are more than 20 trillion combinations! So “Combinatorial” problems are those where each new entrant has a relationship with every existing entrant and so represents factorial growth (which is even more than exponential growth). Classical computers generally attack these problems by rote force, trying each combination one-by-one to try and find the optimal answer. As the inputs scale into the 100’s or 1000’s the computing processing power required reaches a limit and can’t solve the problem in a reasonable amount of time.

Ok, I know you’re thinking to yourself, do we really need Quantum Computers to figure out our seating charts? Not exactly. The seating chart is just to illustrate the class of problem that cannot be efficiently solved by today’s classical computer. Combinatorial challenges such as the seating chart example, represent the same class of problem as designing a new drug, where instead of party guests, the variables are atoms and electrons. Here are a few additional real-world combinatorial challenges where today’s classical computers have hit a limit of utility, but where Quantum Computers can help:

• Drug Discovery: Pharmaceutical researchers need to find molecules that work—molecules that bind to the right target, pass through cell membranes, stay stable in the body, and don’t cause toxic side effects. The theoretical chemical space they’re searching through contains roughly 10^30 possible molecules. That’s more than the number of stars in the observable universe. Classical computers struggle here because testing each candidate individually is computationally exhausting. Researchers currently use screening libraries and AI to narrow the field, but it’s still a slow process. Quantum computers excel at this specific type of search. They can explore the vast chemical space simultaneously, evaluating molecules against multiple constraints (solubility, toxicity, binding affinity) in parallel. Recent demonstrations show that quantum systems can now generate promising drug candidates in hours rather than months—potentially compressing years of early-stage discovery work.

• Materials Science: Similar to drug discovery, materials scientists face a combinatorial explosion when searching for new materials with specific properties: semiconductors that conduct electricity more efficiently, batteries that hold more charge, metals that withstand higher temperatures. etc. Quantum computers can simulate molecular interactions with precision that classical systems struggle to match, allowing researchers to predict material behavior before spending time and money synthesizing them in the lab.

• The Traveling Salesman: This classic optimization problem asks: Given a list of cities and the distances between them, what’s the shortest route that visits each city exactly once and returns home? With five cities, there are 120 possible routes. With 15 cities, there are over a trillion possible routes. With 50 cities, the number of possibilities exceeds the atoms in the universe. In the real world, this problem appears everywhere: delivery route optimization, supply chain management, and logistics planning. Slight improvements in efficiency translate directly to enormous cost savings for companies moving goods or services.

• Portfolio Optimization: Investment managers face a similar problem when building portfolios. With hundreds or thousands of potential assets, the number of possible combinations and correlations between assets is astronomical. The goal is to find the portfolio that maximizes returns while minimizing risk. This is another textbook combinatorial optimization problem. Quantum computers are being tested now on portfolio optimization, showing promise in decomposing large problems into manageable pieces and finding superior portfolios with lower volatility than classical approaches.

Here are some Examples of Combinatorial Problems:

Source: Boston Consulting Group

Why Will Quantum Computers Be Better At These Problems and What Won’t They Be Better At?

Quantum Computers approach these types of problems differently. Qubits can exist in superposition and they can be entangled so they can simultaneously represent multiple states and explore possible solutions in parallel rather than sequentially. For specific problem structures like combinatorial models, this enables Quantum Computers to have an exponential advantage over classical computers. And while today’s Quantum Computers are fragile and have not yet scaled to many qubits, even modest improvements for combinatorial problems will have commercial value. Even a 10% improvement in drug discovery efficiency, supply chain logistics or portfolio optimization can translate into billions of dollars across the global economy and I expect we’ll begin seeing some commercial utility in this regard from Quantum Computers in the near future.

However, Quantum Computers won’t be general-purpose replacements for your laptop. They won’t be good at web browsing, email or spreadsheets. They won’t render graphics faster or enable better media streaming. So don’t give up your existing computer, but be on the lookout for Quantum Computers helping augment existing computing workflows and for commercial utility regarding the many combinatorial problems we currently face.

Disclosure: The author is a venture investor with investment interests in quantum and may have an interest in companies discussed in this post. The views expressed herein are solely the views of the author and are not necessarily the views of Corporate Fuel Partners or any of its affiliates or any companies it has investment interests in. Views are not intended to provide and should not be relied upon for investment advice.

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