Quantum computing emerged because classical computers, despite their extraordinary success, encounter fundamental limits when asked to model certain kinds of reality.
For decades, progress in computing followed a predictable trajectory. Faster processors, smaller transistors, and greater parallelism made increasingly complex problems tractable. This progress shaped everything from scientific research to global finance. Yet, some problems scale in a way that makes them effectively impossible to solve, regardless of how much hardware is added.
The issue with standard computing
Classical computers process information using bits that exist in one of two states, zero or one. Even when billions of these bits operate in parallel, they remain bound by deterministic rules. For many tasks, this is sufficient. Sorting data, rendering graphics, routing network traffic, and training machine learning models all fit comfortably within this paradigm.
The difficulty arises with problems whose complexity grows exponentially. In these cases, each additional variable multiplies the number of possible states that must be examined. Cryptographic key searches, molecular simulations, and certain optimization problems quickly exceed any realistic computational budget. Adding more processors does not fundamentally change the scaling behaviour. It only postpones the failure point.
This is not a software limitation or a lack of engineering ingenuity. It is a mathematical reality tied to how classical information is represented and manipulated.
The Moore's Law detour
- Moore's Law is the observation that the number of transistors in a dense integrated circuit (IC) doubles approximately every two years. Originally formulated by Gordon Moore, co-founder of Fairchild Semiconductor and Intel, in 1965, this rate became widely adopted as a guiding principle for the semiconductor industry.
For a long time, Moore’s Law obscured these limits. As transistors shrank and clock speeds increased, once infeasible tasks became routine. Entire industries grew around the assumption that future hardware would simply catch up with today’s computational demands.
That assumption no longer holds. Physical constraints now dominate processor design. Heat dissipation, energy consumption, and quantum effects at nanometre scales prevent indefinite miniaturization. Modern gains come from architectural tricks and specialized accelerators.
The slowdown of Moore’s Law removed the illusion that classical computing alone could scale forever.
What is quantum computing?
- Quantum computing is a multidisciplinary field that leverages the principles of quantum mechanics to solve complex problems faster than classical computers. Unlike classical computers, which use bits that are either 0 or 1, quantum computers use quantum bits, or qubits, which can exist in a superposition of states, meaning they can represent both 0 and 1 simultaneously.
The idea behind quantum computing
The original motivation for quantum computing came from physics, not computer science. In the early 1980s, researchers, including Richard Feynman, observed that simulating quantum systems on classical computers was inherently inefficient. The memory and time required increased exponentially with the system’s size.
The problem was not that physics was too complex, but that classical computation was using the wrong language to describe it.
This insight led to a radical idea. Instead of forcing classical machines to approximate quantum behaviour, build machines that operate according to the same rules as the systems being studied. A quantum computer would not simulate quantum mechanics. It would embody it.
Hard problems vs useful problems
Not every computationally hard problem justifies a new computing paradigm. Many hard problems are hard in ways that are irrelevant to practical applications. Quantum computing gained traction because it promised advantages for specific classes of problems with real-world impact.
These include factorization, which underpins much of modern cryptography, unstructured search, certain optimization tasks, and the simulation of chemical and material systems. Importantly, quantum computing does not make all problems easier. It does not replace classical computing, and it does not offer universal speedups.
The appeal lies in targeted advantage. Even a narrow improvement can have outsized consequences when applied to security, drug discovery, or large-scale optimization.
The hype on quantum computing
Quantum computing quickly became a magnet for exaggerated claims. Popular narratives describe it as infinitely parallel, capable of trying all possibilities at once, or as a machine that instantly solves problems beyond human comprehension.
These metaphors are misleading. Quantum computers are constrained, fragile, and extremely difficult to scale. Their power comes from careful manipulation of probability and interference, not from brute-force exploration of all possible answers.
Understanding why quantum computing exists requires resisting both dismissal and mystification. It is neither a magic device nor a research curiosity. It is a response to specific, well-defined limitations in classical computation.
A tool born of limits
Quantum computing exists because some problems sit at the intersection of physics, mathematics, and computation in a way that classical machines cannot efficiently navigate. It represents an attempt to align our computational tools more closely with the structure of the physical world.
Whether this attempt will fully succeed remains an open question. What is clear is that quantum computing was not invented to be faster in general. It was invented because, for certain problems, speed is not the real issue. Representation is.