Classical vs. Quantum Computing

Classical Computing


Let's review how classical computing works. This is what we use every day on our PCs, laptops and phones, which run on the binary system that uses bits, represented as 0 or 1.


Bits process information sequentially, and remember that:


  • 1 bit is the smallest unit, a 0 or a 1.
  • 8 bits make up 1 byte, roughly equivalent to a text character or number.
  • 1,024 bytes (approximately) make 1 kilobyte (KB).
  • 1,024 kilobytes make 1 megabyte (MB).
  • 1,024 megabytes make up 1 gigabyte (GB).
  • 1,024 gigabytes make 1 terabyte (TB).

(This is how we arrive at the famous storage capacities offered by different computer models, hard drives, flash drives or cloud storage providers).

For example, if we write the word "HOLA" in binary it would be 01001000 01001111 01001100 01000001, since:


  • "H" is 01001000
  • "O" is 01001111
  • "L" is 01001100
  • "A" is 01000001

When we combine many bits, we can represent numbers, letters, images and all kinds of data. Classical processors use electronic circuits to perform operations with these bits, like adding them, comparing them or moving them from one place to another. In essence, all classical computing is a huge series of operations with those zeros and ones.


With this basic principle of how our computers work in mind, we can move on to quantum computing for a better understanding.


Quantum Computing


Quantum computing, instead of working with bits, works with Qubits.


Qubits in quantum computing can be in what's called Superposition, which means they can represent both 0 and 1 simultaneously. This lets them process a huge number of possibilities at once, something classical bits can't do. A qubit can be 0, 1 or both at the same time, and that's the big difference that makes quantum computing so powerful for certain types of problems.


Until now, this was largely theory. It was known that quantum computing was powerful, but it hadn't yet been seen outperforming classical systems on real-world problems. According to IBM, that will change in 2026. This moment is known as Quantum Advantage: the first case in which a quantum computer solves a real problem more efficiently than any possible classical computer.


It's not that we'll soon have a quantum laptop at home. For now, that's not on the horizon. Today's quantum computers are large, delicate and require very specific conditions, like extremely low temperatures. So, for now, quantum computing is more of an advance for certain industrial sectors, scientific research and some highly specialized applications, not so much for us as end users.


This technology will have its first real impact in fields where the problem isn't a lack of data, but the impossibility of exploring every possible combination. In drug development, for example, the central challenge is accurately simulating the behavior of molecules and proteins, something classical computers can only approximate. A quantum computer lets you directly model those quantum systems, drastically reducing the time and cost needed to discover new medicines.


Something similar happens in materials science. Designing materials with specific properties — greater conductivity, less weight, better energy efficiency — requires understanding interactions at the atomic level that today are tested through trial and error. Quantum computing lets you simulate those interactions before manufacturing the material, speeding up the discovery of longer-lasting batteries, advanced superconductors or key materials for the energy transition.


In the financial world, the value isn't in calculating fast, it's in optimizing decisions across thousands of interdependent variables. Building investment portfolios, managing risk or simulating extreme scenarios are combinatorial problems that grow explosively. Quantum computing lets you explore huge solution spaces in parallel, offering us more robust models and better-grounded decisions.


Logistics and supply chains are another clear case. Optimizing routes, fleets, inventories and timing across global networks is a mathematically intractable problem for classical systems as scale grows. Quantum algorithms can find optimal or near-optimal solutions more efficiently, reducing costs, emissions and operational fragility.


Artificial intelligence won't be replaced by quantum computing, but it will be amplified by it. Instead of training entire models, quantum systems can accelerate specific tasks like parameter optimization or exploring complex probabilistic spaces. The result isn't a “quantum AI,” but a more efficient AI integrated into hybrid architectures.


That's why, when companies like IBM talk about 2026 as a turning point, they're not referring to a change we'll see as end users, but to the first time a quantum computer solves a real problem better than any available classical method. It's not the end of traditional computing, but the beginning of a new technological layer operating behind the scenes, right where today's limits fall short.