Quantum computing is not science fiction; it is a paradigm shift that exploits superposition and entanglement to solve problems that are currently intractable for classical machines. Major players and startups are already making strides in hardware and services (QaaS), and experts warn of two main vectors: real opportunities in optimization, chemistry, and AI, and concrete risks in security and cryptography that require forward planning. Preparing teams, assessing use cases, and migrating to post-quantum cryptography practices are practical steps that responsible organizations must start executing now.

What is quantum computing

Quantum computing is an innovative area of computer science and physics that uses the laws of quantum mechanics to process information in a different and much more powerful way than traditional computers. While classical computers, like your cell phone or laptop, store and manipulate data in bits that can only be 0 or 1, quantum computing uses units called qubits, which can represent multiple states at the same time.
Imagine a bit as a coin that can only be stationary showing heads (0) or tails (1). A qubit, on the other hand, would be like a coin spinning in the air, potentially being in several states at once until it is "observed"—that is, until it is measured. This allows a quantum computer to explore many possibilities simultaneously, giving it a kind of "massive parallelism" that does not exist in classical computers.

Two key concepts make this possible:

Superposition: is the ability of a qubit to be in a combination of 0 and 1 at the same time.

Entanglement: is when qubits become "connected" in such a way that the state of one instantly affects the state of the other, even if they are physically separated.

These principles run contrary to our experience with ordinary computers, but they are real phenomena of quantum physics, already observed in laboratory settings and used in computing experiments.   

The potential power of quantum computing comes from the fact that the number of possible states grows exponentially as more qubits are added, allowing certain tasks to be processed much faster than with a traditional computer.

However, huge challenges remain: qubits are extremely sensitive to the environment, require highly controlled conditions (such as temperatures near absolute zero), and the technology is still in an experimental and developmental phase.

In summary, quantum computing is a new way of "thinking" calculation, based on fundamental physics, which promises to revolutionize complex fields such as molecular simulation, system optimization, and even boost AI, but it is still at the beginning of its technological journey. 

Where is quantum computing already making a difference? 

Despite still being in development, quantum computing is already making an impact outside the laboratory. Today, major technology companies operate real quantum processors, accessible primarily via the cloud, allowing researchers and organizations to test algorithms in practical scenarios. Platforms like IBM Quantum, Google Quantum AI, and Azure Quantum offer this access without the need for proprietary infrastructure, a model known as Quantum Computing as a Service (QaaS).

In practice, the first uses are concentrated in highly complex problems where classical computing meets clear limits. Key areas include simulations of chemistry and new materials, optimization of logistics routes, advanced financial risk analysis, and experiments accelerating machine learning algorithms. These cases still function as proofs of concept, but they already demonstrate real gains in efficiency and calculation capacity.

The key point is that quantum computing does not replace current computers, but begins to act in a complementary way, solving specific niches. This initial stage is exactly where organizations tracking the technology can learn, test, and position themselves before it becomes widely competitive.

Which problems can it solve better than classical computing

Quantum computing is not a universal solution, but it excels in specific classes of problems that grow too fast for classical computers. Generally, these are challenges with many interdependent variables, where testing all possibilities would be unfeasible with current technology.

Complex optimization
Optimization problems appear in logistics, mobility, finance, and resource management, such as defining the best route for hundreds of deliveries or allocating limited resources efficiently. Classical computers use approximations; quantum systems can explore multiple combinations simultaneously, finding better or faster solutions in very complex scenarios.

Molecular and materials simulation
This is one of the most promising fields. Simulating molecules, chemical reactions, or new materials requires a huge amount of calculation because it naturally involves quantum phenomena. Quantum computers can model these systems more accurately, accelerating research into pharmaceuticals, batteries, fertilizers, and new industrial compounds.

Hybrid machine learning (classical + quantum)
Instead of replacing current AI, quantum computing tends to boost it. Hybrid models use classical computers for conventional tasks and quantum modules for specific steps, such as parameter optimization or analysis of high-dimensional data spaces. The expected result is more efficient training and better performance in complex problems, not a "magical quantum AI."

In summary, the quantum advantage arises when the problem involves scale, complexity, and multiple simultaneous possibilities. Although many of these uses are still in experimental phases, they clearly indicate where classical computing begins to find its limits, and where quantum computing starts to make strategic sense.

Immediate and medium-term risks

The biggest impact of quantum computing in the short and medium term is on digital security. Quantum algorithms will, in the future, be able to break cryptography methods widely used today, which puts communications, transactions, and sensitive data at risk. Because of this, there is already concern about data being stored now to be decrypted later.

Information privacy is also on alert. Organizations handling personal or strategic data will need to gradually migrate to post-quantum cryptography standards, avoiding an emergency transition when the technology matures.

Furthermore, integrating quantum solutions with traditional systems brings challenges of stability and technological dependence, especially in critical infrastructures. The primary risk, therefore, is not immediate collapse, but a lack of preparation in the face of a change that is already being planned globally.

How quantum computing can boost (or transform) AI

Quantum computing does not emerge to replace current artificial intelligence, but to expand its limits. Its greatest potential lies in accelerating parts of the process that currently consume a lot of time and energy, such as model optimization, fine-tuning of parameters, and searching extremely large solution spaces—common bottlenecks in advanced machine learning.

In practice, the most viable path is hybrid models, which combine classical and quantum computing. While traditional systems remain responsible for training and inference, quantum algorithms can be used in specific stages, such as improving learning efficiency, handling high-dimensional data, or solving optimization problems more quickly.

Although these uses are still in experimental phases, research indicates that this integration could result in more efficient training, lower computational consumption, and more precise models in complex scenarios. The transformation, therefore, is not immediate, but points toward a more powerful AI as quantum technology matures.

Practical steps to prepare your organization

Preparation begins with a risk assessment, identifying which data and systems need to remain secure in the long term. Next, it is worth investing in basic training for teams to understand concepts, limits, and real impacts of quantum computing. Partnerships with QaaS providers allow experimenting with the technology without major costs, while creating a post-quantum cryptography (PQC) roadmap helps ensure a secure and gradual transition.

The role of the third sector and small businesses: impact opportunities and how to participate in the transition

Quantum computing will not be exclusive to large corporations. For the third sector and small businesses, the main opportunity lies in access via the cloud (QaaS), which reduces costs and allows experimentation without proprietary infrastructure. This opens up space to use quantum or hybrid solutions in resource optimization, logistical planning, complex data analysis, and evidence-based social impact.

Participating in the transition begins with technological literacy, monitoring open standards (such as post-quantum cryptography), and partnerships with universities, innovation hubs, and cloud providers. Even without immediate adoption, understanding the landscape and testing use cases positions these organizations to seize future gains responsibly, efficiently, and with a focus on real impact.