Quantum computing is often presented as an almost magical technology, capable of solving any problem in seconds. The reality is more interesting and also more complex: it is still at an experimental stage, but it could transform areas such as materials design, chemistry, and drug discovery.
On World Quantum Day, Google Quantum AI answered some of the most frequently searched questions about this technology and explained why building a useful quantum computer remains one of the great challenges of engineering.
Why do we need quantum computers?
The answer begins with an idea proposed by physicist Richard Feynman in 1981: if nature operates according to quantum rules, perhaps we need machines based on those same rules to truly simulate it.
Classical computers represent information using bits, which can have the value 0 or 1. Thanks to this architecture, we have built everything from phones to supercomputers. However, some problems involving molecules, materials, and physical systems become too costly to simulate as their complexity increases.
A quantum computer could approach certain problems in a different way. It would not replace traditional computers or be faster for every task. Its potential advantage would lie in solving specific types of calculations that are currently beyond the reach of even the most powerful classical machines.
Google highlights three areas with significant potential:
- Discovering more sustainable materials, by simulating their properties with greater precision.
- Pharmaceutical research, through the study of molecules and chemical reactions.
- Complex scientific problems that require quantum systems to be represented efficiently.
The promise is not to make everything faster, but to open the door to problems we currently cannot solve.
What is a qubit and why does it matter?
The basic unit of a quantum computer is the qubit, short for quantum bit. Unlike a classical bit, a qubit can exist in a combination of the 0 and 1 states.
This property is known as superposition. However, it does not simply mean that a qubit is a 0 and a 1 at the same time in a way we can read directly. When we measure it, we get a specific result. The advantage appears during the calculation, when the amplitudes and relationships between different states can be manipulated through quantum operations.
Google uses the Bloch sphere to visualize the state of a qubit. It is a geometric representation in which each point on the surface corresponds to a possible state of a two-level quantum system.
That is why the Bloch sphere appears in the Google Doodle for World Quantum Day. It is not just a visual element: it helps represent concepts that are difficult to observe directly, such as superposition and the changes in state produced by quantum operations.
How do they find answers through interference?
Quantum computing does not test every possible answer and then magically choose the right one. Quantum algorithms prepare states, apply operations, and take advantage of a phenomenon called interference.
Interference makes it possible to strengthen the probabilities associated with some results and reduce those of others. A well-designed algorithm uses this effect to increase the likelihood of measuring a useful answer.
Imagine waves meeting in the water. At certain points, they add together and form a larger wave; at others, they cancel each other out. Something similar happens in a quantum circuit with probability amplitudes, although the phenomenon is described using the mathematical rules of quantum mechanics.
The challenge lies in designing the right algorithm for each problem. Superposition alone does not guarantee a fast solution. You need a precise sequence of operations and a way to extract information without destroying the quantum state too soon.
The biggest obstacle: decoherence
Qubits are extremely sensitive. Any unwanted interaction with the environment, such as vibrations, temperature changes, or electromagnetic noise, can alter their state. This process is known as decoherence.
When decoherence occurs, quantum information degrades and the calculation can produce an incorrect result. It is like trying to preserve a very delicate signal in an environment full of interference: the more noise there is, the harder it becomes to keep the message intact.
That is why Google Quantum AI is working on quantum error correction. The goal is to distribute the information from a logical qubit across several physical qubits and detect, as well as correct, certain errors without destroying the information being processed.
This path requires moving from experimental demonstrations to stable, reliable systems capable of running long calculations. To achieve that, simply increasing the number of qubits is not enough. Their quality must also improve, error rates must be reduced, and the hardware must be coordinated with the algorithms and control software.
From scientific demonstration to a useful tool
Google argues that large-scale quantum computers with error correction could become tools for solving real-world problems. But that possibility still depends on major advances in physics, engineering, and computing.
The more realistic picture is not of a machine that replaces all current computers. It is a specialized system that works alongside classical infrastructure to tackle problems requiring a quantum description.
Quantum computing, then, is neither an instant promise nor a universal solution. It is a long-term effort to build machines capable of studying a part of nature that traditional computers can only approximate. The challenge is to preserve their quantum states long enough for an elegant idea to become a reliable tool.
Original source
https://blog.google/innovation-and-ai/models-and-research/quantum-computing/world-quantum-day-2026