In 1981, American physicist and Nobel Prize winner Richard Feynman gave a lecture at the Massachusetts Institute of Technology (MIT) near Boston, outlining his revolutionary ideas. Feynman suggested that the strange physics of quantum mechanics could be used to perform calculations.
The field of quantum computing was born. Over the 40 years since then, it has become an intensive research field in computer science. Despite years of frantic development, physicists have yet to build a practical quantum computer suitable for everyday use and normal conditions (e.g., many quantum computers have very low temperature). Questions and uncertainties remain about the best way to reach this milestone.
What exactly is quantum computing? And how close is it to widespread use? First, let’s start with classical computing, which is why I wrote this article Let’s take a look at the types of computing we rely on today, such as the laptops we use.
Classical computers process information using combinations of “bits,” the smallest units of data. The value of these bits is 0 or 1. Everything you do on your computer, from composing email to surfing the web, is made possible by manipulating combinations of these bits in strings of 0s and 1s.
Quantum computers, on the other hand, use quantum bits, or quantum bits. Unlike classical bits, qubits do not simply represent 0 or 1. Thanks to a property called quantum superposition, qubits can be in multiple states at the same time. This means that a qubit can be 0, 1, or both at the same time. This allows quantum computers to process large amounts of data and information simultaneously.
Imagine being able to consider all possible solutions to a problem at once instead of all at once. This allows you to navigate the maze by trying all possible paths simultaneously to find the correct one. Quantum computers are therefore incredibly fast at finding optimal solutions, such as identifying the shortest path or fastest method.
Juric Peter / Shutterstock
Consider the highly complex problem of rescheduling a flight after delays or unforeseen circumstances. Although this happens regularly in the real world, the applied solution may not be the best or optimal one. To derive the optimal response, a typical computer must consider every possible combination of flight movements, reroutes, delays, cancellations, or groupings one by one.
Every day, more than 45,000 flights are operated by more than 500 airlines and connect to more than 4,000 airports. This problem would take years to solve with traditional computers.
Quantum computers, on the other hand, can try all of these possibilities at once and organically arrive at the optimal configuration. Qubits also have a physical property known as entanglement. When qubits are entangled, the state of one qubit can depend on the state of another, no matter how far apart they are.
Again, there is no equivalent in classical computing. Quantum entanglement allows quantum computers to solve certain problems exponentially faster than classical computers.
Read more: Answers about brain implants, agent AI, and dark matter: What we can expect from science in 2025 – Podcast
A frequently asked question is whether quantum computers will completely replace classical computers. The short answer is “no.” At least not in the near future. Quantum computers are extremely powerful for solving specific problems, such as simulating interactions between different molecules, finding the best solution among many alternatives, and handling encryption and decryption. However, it is not suitable for all types of tasks.
Classical computers process one calculation at a time in a linear sequence, using algorithms (sets of mathematical rules for performing specific computational tasks) designed for use with classical bits of 0 or 1. ). This makes computers highly predictable and robust, making them less prone to errors than quantum machines. Traditional computers will continue to play a major role in everyday computing needs such as word processing and Internet browsing.
There are at least two reasons for this. The first is practical. Building a quantum computer that can perform reliable calculations is extremely difficult. The quantum world is incredibly unstable and error-prone because qubits are easily disturbed by things in their environment, such as interference from electromagnetic radiation.
The second reason is the inherent uncertainty when working with qubits. Because qubits are in a superposition (not 0 or 1), they are not as predictable as the bits used in classical computing. Therefore, physicists explain qubits and their computations in terms of probability. This means that running the same problem multiple times on the same quantum computer using the same quantum algorithm may return a different solution each time.
To deal with this uncertainty, quantum algorithms are typically executed multiple times. The results are then statistically analyzed to determine the most likely solution. This approach allows researchers to extract meaningful information from quantum computations that are stochastic in nature.
From a commercial perspective, the development of quantum computing is still in its early stages, but the landscape is very diverse, with many new companies emerging every year. It is interesting to see that in addition to large established companies such as IBM and Google, new companies such as IQM, Pasqal, and startups such as Alice and Bob are participating. They are all working to make quantum computers more reliable, scalable, and accessible.
Anna Siraj/EPA Images
Until now, manufacturers have focused on a quantum computer’s number of qubits as a measure of its performance. Manufacturers are increasingly prioritizing ways to correct the errors that quantum computers are prone to. This change is critical to the development of large-scale, fault-tolerant quantum computers, as these techniques are essential to improving ease of use.
Google’s latest quantum chip, Willow, has recently shown impressive progress in this field. The more qubits Google used in Willow, the fewer errors it made. This achievement represents a significant step toward building commercially relevant quantum computers that could revolutionize fields such as medicine, energy, and AI.
More than 40 years later, quantum computing is still in its infancy, but significant advances are expected over the next decade. The probabilistic nature of these machines represents a fundamental difference between quantum and classical computing. This is what makes them brittle and difficult to develop and extend.
At the same time, it becomes a very powerful tool for solving optimization problems, allowing you to explore multiple solutions simultaneously and do so faster and more efficiently than traditional computers.
