Evolution of Quantum Computing

In 1981, Richard Feynman questioned why researchers were trying to simulate Quantum Physics on classical computers if nature itself is quantum. From this insight was born the idea of building computers that use the rules of Quantum Mechanics directly to solve problems that, for traditional machines, would be impossible or economically unfeasible. 

For decades, Quantum Computing was a topic for scientific conferences, not board meetings. It remained restricted to papers, laboratories, and a small group of specialists scattered around the world. It was seen as something “for many decades from now.”

Fast-forward to today. In one of his keynotes, Jensen Huang, NVIDIA’s CEO, gives a straightforward summary: hardly anyone talked about Quantum Computing outside Academia; results were essentially experimental. Now, the vocabulary has changed. Words like product, platform, partnership, and investment have entered the scene. Major players (Google, IBM, Microsoft, and NVIDIA itself) have turned a research field into a commercial race. When companies of this size align brand, budget, and roadmap around the same technology, the message to the market is clear: it is no longer science fiction; it is a transition already underway. 

From theory to practice: the evolution of Quantum Computing 

This history can be summarized in a few milestones. In the 1980s, Feynman planted the seed by proposing that quantum systems be used to simulate quantum phenomena. In the following decades, the 1990s and 2000s, algorithms emerged that showcased the potential of this model, such as Shor’s algorithm, capable of factoring large numbers much more efficiently than any known classical method, and Grover’s algorithm, which speeds up searches in databases. It was still all theoretical, but there was already a huge promise embedded in it.

From 2010 onwards, we began to see the first accessible devices. Cloud platforms allowed researchers and companies to experiment on real quantum hardware. IBM, for instance, made remote access to its first quantum computers available, opening up the possibility of testing algorithms and applications without having to set up an entire lab. 

Between 2020 and 2025, the curve steepened. We entered the phase in which Quantum Computing ceases to be just a “paper topic” and starts appearing in product presentations, investor pitches, and big techs’ long-term strategies. This is the moment when qubits (quantum bits, that is, the basic unit of information in quantum computing, and quantum circuits start sharing space with KPIs, ROI, and technology roadmaps. 

The role of the giants: Google, IBM, Microsoft, and NVIDIA 

Google grabbed the spotlight when it announced that its Sycamore quantum processor, with 53 qubits, had performed a specific task in about 200 seconds, something which, according to the company itself, would take thousands of years on a classical supercomputer. The term “quantum supremacy” generated debate, but one conclusion became clear: there are already very specific problems in which a quantum computer outperforms any known classical machine. For the corporate world, this simultaneously lights up two signals: opportunity (new ways to simulate, optimize, and analyze data) and risk (falling behind in modeling and innovation capabilities). 

IBM took a complementary path, treating Quantum Computing as a product from early on. By announcing processors with more than 1,000 qubits, such as Condor, the company goes beyond the absolute number of qubits and presents a public roadmap, speaking openly about quantum-centric supercomputers that combine quantum hardware, software, cloud services, and well-defined use cases. Finance, chemistry, logistics, and energy are some of the sectors already mapped in its materials and demos. The message is: there is a plan, not just a lab experiment. 

Microsoft, in turn, is betting on topological qubits, a type of qubit designed to be more resistant to noise and, therefore, better suited for scalability. The announcement of chips with “topological cores” and the vision of reaching millions of qubits on a single chip reveal a long-term ambition. There is skepticism in the scientific community, and plenty of discussion about the extent to which marketing gets ahead of technical reality. Even so, the investment is concrete: expansion of labs, partnerships with research centers, and clear integration with the Azure ecosystem, preparing the environment for quantum workloads as soon as the technology is ready for production. 

NVIDIA has chosen a different strategic position. Instead of competing directly to have the “largest quantum computer,” the company wants to be the glue between Quantum Computing, AI, and High-Performance Computing. With the CUDA-Q platform, it offers an environment where developers can orchestrate CPUs, GPUs, and QPUs in the same workflow. In practice, it becomes possible to write applications where part of the workload runs on GPUs (simulation, AI, preprocessing) and another specific part is sent to a quantum processor. Adding in specialized interconnects and partnerships with quantum companies, NVIDIA positions itself as the standard infrastructure for a hybrid future in which different types of processors work together. 

What is already possible to do with Quantum Computing today 

If you are a technology or business decision-maker, it is natural to ask what can be done in concrete terms right now. It is important to be honest: most of today’s equipment is in the NISQ phase (Noisy Intermediate-Scale Quantum), which means they have tens or hundreds of qubits that are still quite susceptible to noise. Even so, several application fronts are already being tested. 

In the field of optimization, problems such as fleet routing, production planning, and resource allocation are being studied with quantum algorithms or algorithms that use quantum concepts, often combined with Artificial Intelligence techniques. In chemistry and materials science, simulations of complex molecules and advanced structures are natural candidates for quantum advantage, with impact on pharmaceuticals, energy, fertilizers, batteries, and more. In the financial sector, there is research on risk modeling, derivatives pricing, and portfolio optimization using hybrid approaches. More recently, experiments have begun to appear in Quantum Machine Learning (QML), which use quantum circuits as building blocks in AI models. 

In practice, most of this is still at the R&D stage: pilot projects, proofs of concept, and collaborations between technology companies, universities, and players in strategic sectors. The crucial point is that organizations that start experimenting now gain a stock of knowledge that is hard to copy later. 

Security and cryptography in the post-quantum world 

Another point that concerns executives and technology teams is security. Quantum algorithms such as Shor’s have the potential to break cryptographic schemes widely used today, such as RSA and ECC, provided that large and reliable quantum computers exist. This means that the discussion on post-quantum security is no longer theoretical; it has become strategic. 

Standardization bodies such as NIST (National Institute of Standards and Technology) have already selected post-quantum cryptography algorithms, such as CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium, FALCON, and SPHINCS+ for digital signatures. At the same time, concern is growing over the “harvest now, decrypt later” scenario, in which data encrypted today is stored to be decrypted in the future, when Quantum Computing reaches maturity. 

If your organization deals with sensitive information of long duration (health data, government records, intellectual property, long-term contracts, trade secrets), the migration to post-quantum algorithms is not a topic to leave for “later.” It needs to enter the security roadmap now, alongside other digital transformation initiatives. 

What this means for your company today 

The good news is that you do not need to buy a quantum computer tomorrow morning. The bad news is that, if nothing is done, it is possible to fall behind precisely when this technology begins to deliver practical advantages. The key is to act on three fronts: risk, opportunity, and preparation. 

First, it is essential to understand your business’s risk and opportunity horizon. For how long do your data need to remain confidential? If the answer is measured in decades, post-quantum cryptography immediately enters the conversation. Does your business model depend on complex optimization, sophisticated simulations, or pattern detection? In that case, Quantum Computing is not just a threat; it becomes a concrete opportunity for competitive advantage. 

Next, it is worth starting small, but starting. Running code on a physical quantum computer is not necessary; high-performance simulators, often GPU-based, already allow you to test algorithms, understand limitations, adapt teams, and map relevant use cases. A well-defined pilot project, even if modest, usually generates more real learning than years of distant observation. 

Finally, it is essential to strengthen your technological foundation. Quantum Computing will not replace legacy systems all at once; it will connect to them. This requires well-governed data, modern API-oriented architectures, strategic use of cloud, and, above all, maturity in Artificial Intelligence. The most promising applications of the future tend to be hybrid, combining AI, supercomputing, and quantum accelerators in the same workflow. 

How Visionnaire can support your journey in this new landscape 

At Visionnaire, we have been following for decades the evolution of technologies that initially seemed distant from business reality, but today are part of companies’ daily routines, such as AI itself. With Quantum Computing, it will be no different. More important than “programming qubits” right now is building the foundations so that your organization is ready when this technology enters the stage of large-scale “useful use.” 

We can help your company assess maturity in data, AI, and systems architecture, identifying whether you are ready to integrate emerging technologies. We also support the construction of a realistic technology roadmap that considers topics such as post-quantum cryptography, generative AI, GPU acceleration, and, further ahead, integrations with quantum platforms. And, of course, we operate as a Software and AI Factory to develop tailor-made solutions, already optimized for high performance, simulation, and advanced algorithms. 

You do not need to become a Quantum Computing expert to make good decisions today. What you do need is to decide whether you want to be a spectator or a protagonist in the next major wave of technological transformation. If your intention is to lead, the next step is simple: talk to Visionnaire and start designing, right now, the strategy that will position your company in the post-quantum world.