Quantum computing is no longer confined to university laboratories. Companies such as IBM, Google, Microsoft, Quantinuum o IonQ They are already working on processors, development tools, and cloud services to experiment with quantum algorithms. Even so, it is wise to temper expectations: most business applications are still in the research, proof-of-concept, or resource estimation phase.
The main idea is simple to express, though difficult to build: to use properties of quantum mechanics to solve certain problems in a way that is different from a classical computer.
This does not mean that a quantum computer will replace current servers, laptops or cloud systems. Its natural field lies in very specific problems, such as molecule simulation, optimisation, cryptography, materials or certain mathematical models.
For those training in technology, quantum computing opens up an interesting career path as it combines physics, mathematics, programming, cloud, cybersecurity, and artificial intelligence. It is not yet a mass market, but it is creating a specialised demand for professionals capable of understanding both the technical fundamentals and its real-world limitations.
What is quantum computing
A classic computer works with bits, which take values of 0 or 1. A quantum computer works with qubits, units of information that represent quantum states described by amplitudes. Before being measured, a qubit can be in a superposition of states; upon measurement, a classical result is obtained.
This difference allows algorithms to be designed that take advantage of phenomena such as superposition, entanglement, and interference. Superposition allows a qubit to be described by a combination of states. Entanglement connects the behaviour of several qubits in a way that has no direct equivalent in simple classical correlations. Interference is used to increase the probability of obtaining useful results and reduce that of results of no interest.
This does not turn the quantum computer into a “faster for everything” machine. In many cases, a classical system will remain cheaper, more stable, and more practical. The quantum advantage appears when there is a suitable algorithm, a well-posed problem, and hardware of sufficient quality to execute it.
It is so difficult to build quantum computers because they are incredibly sensitive to their environment and susceptible to errors.
The main problem lies in errors. Qubits are very sensitive to noise, temperature, vibrations, and small environmental interferences. This fragility leads to information loss and incorrect results.
That's why current research isn't just focused on making more qubits. Efforts are also being made on logical qubits, error correction, real-time decoders, and architectures capable of scaling without noise destroying the computation.
Google published results in Nature regarding its Willow processor generation, including surface code memories that operated below the error correction threshold. The work involved a distance-5 code with real-time decoding and a distance-7 code on a 105-qubit superconducting processor. This is a relevant technical advance for building more reliable systems, but it does not yet equate to having a general-purpose fault-tolerant quantum computer.
IBM has also placed a focus on fault tolerance. Its roadmap sets Starling for 2029, with the aim of achieving 200 logical qubits and executing 100 million quantum gates. It should be viewed as a technological roadmap, not as a business capability that is already available today.
What applications can it have
The most intuitive application is in the simulation of quantum systems. Molecules, materials and chemical reactions obey quantum laws. Simulating them with classical computers can become very complex as the system size increases. A well-designed quantum computer could help study new materials, catalysts, batteries or chemical processes.
There is also interest in optimisation. Many companies have problems where the best combination needs to be chosen from millions of possibilities: logistics routes, resource allocation, energy planning, financial portfolios, or industrial design. Quantum computing could deliver improvements in specific cases, although today many approaches are still competing with highly optimised classical algorithms.
Cryptography is another affected field. Shor's algorithm, if run on a sufficiently large and fault-tolerant quantum computer, could compromise public-key cryptography schemes based on factoring or discrete logarithms, such as RSA, Diffie-Hellman and elliptic curve cryptography. That scenario still requires machines far more advanced than those available today, but preparations have already begun.
In August 2024, NIST published the first three post-quantum cryptography standards: FIPS 203, based on ML-KEM, for key-encapsulation mechanisms; FIPS 204, based on ML-DSA, for digital signatures; and FIPS 205, based on SLH-DSA, also for digital signatures.
There are also projects in quantum machine learning, search, finance, security, and data science. In all these cases, promising research needs to be separated from actual adoption. A proof of concept can demonstrate that an approach works on a small scale, but that doesn't mean it's ready for production or that it outperforms classical methods in cost, scale, or reliability.
What is the relationship with cloud computing
Most people starting in quantum computing don't have physical access to a quantum processor. They access it via cloud platforms, simulators, and managed environments. This has brought the technology closer to people coming from software, data science, or cloud architecture backgrounds.
Azure Quantum, IBM Quantum, and other environments allow circuits to be executed, algorithms tested, and resources estimated. Microsoft, for example, maintains a resource estimator designed to calculate physical and logical qubits, execution times, and other requirements for running quantum programmes on a fault-tolerant computer. It is an estimation tool, not a test of actual execution on fault-tolerant hardware.
This area links quantum computing with skills already present in many companies: programming, automation, APIs, cloud computing, security, monitoring and resource management. A technical professional does not need to start by building quantum hardware to work in this field. They can contribute through software development, integration, applied research or the analysis of use cases.
What role does post-quantum cryptography play?
Post-quantum cryptography is not about using quantum computers for encryption. It is about designing and implementing classical algorithms that are resistant to attacks from future quantum computers. Nor should it be confused with quantum key distribution, or QKD, which is a different technological line.
This difference is important for businesses, administrations, and cybersecurity teams. The change doesn't happen overnight. First, systems, certificates, protocols, dependencies, libraries, and data that must be protected for many years need to be inventoried. Then, a gradual migration to approved algorithms and hybrid schemes is planned where appropriate.
The publication of standards by NIST marks a practical reference for vendors and organisations. FIPS 203 is oriented towards key encapsulation mechanisms, while FIPS 204 and FIPS 205 focus on digital signatures.
For cybersecurity professionals, this field already offers tangible work: cryptographic auditing, certificate management, library updates, protocol compatibility and risk assessment. Here, quantum computing is already having an impact on the present, even though fault-tolerant quantum computers are still under development.
To work in quantum computing, you need skills in the following areas: * **Quantum Physics:** A strong understanding of quantum mechanics, including concepts like superposition, entanglement, and quantum gates. * **Computer Science:** Core computer science principles, algorithms, data structures, and programming. * **Mathematics:** Advanced mathematics, particularly linear algebra, calculus, and probability theory. * **Programming:** Proficiency in relevant programming languages such as Python, as well as experience with quantum programming frameworks (e.g., Qiskit, Cirq, PennyLane). * **Algorithms:** Knowledge of quantum algorithms (e.g., Shor's, Grover's) and the ability to design new ones. * **Software Engineering:** Skills in developing, testing, and deploying software, especially for complex systems. * **Hardware:** For those interested in the physical implementation, knowledge of quantum hardware, cryogenics, and control systems is beneficial. * **Problem-Solving:** Strong analytical and problem-solving skills are crucial for tackling the complex challenges in this field. * **Communication:** The ability to explain complex quantum concepts to both technical and non-technical audiences. * **Teamwork:** Quantum computing is a collaborative field, so working effectively in a team is essential.
The starting point depends on the role. A research-focused role will require a strong foundation in linear algebra, probability, quantum mechanics and computer science. A software-focused role might start with quantum circuits, Python, Qiskit, Cirq, PennyLane or similar tools.
There is also scope for roles in cloud computing and DevOps. Quantum environments are often consumed as cloud services, complete with execution queues, simulators, APIs, version control and reproducible experiments. Knowing how to document tests, measure results and compare runs is extremely valuable.
In businesses, the most useful profile usually combines a technical foundation with applied judgment. It's necessary to understand when it makes sense to explore quantum computing and when a classical algorithm suffices. This capability avoids expensive, poorly measurable, or unrealistically expectation-based projects.
Professional roles related
1. Quantum software developer
Designs quantum circuits and algorithms using specific frameworks. Typically works with simulators, cloud-accessible processors, and quantum programming libraries. Needs to be able to translate a mathematical problem into operations that a quantum machine can execute.
2. Quantum algorithm researcher
Investigate new algorithms or adapt existing algorithms to specific cases. This profile requires a greater mathematical load. Work closely with universities, research centres, or advanced R&D teams.
3. Quantum Hardware Engineer
Participates in the design, control and improvement of quantum processors. Can work with superconductors, trapped ions, photonics, neutral atoms or other technologies. Experimental physics, electronics, cryogenics and system control carry significant weight here.
4. Quantum Cloud / Platform Engineer
Integrate quantum services within cloud environments and experimentation platforms. It can prepare pipelines, manage access, automate tests, connect simulators with analysis tools, and document reproducible experiments. This profile is closely related to cloud computing, DevOps, and platform architecture.
5. Post-quantum cryptography specialist
Evaluate current cryptographic systems and plan the transition towards quantum-resistant algorithms. This role can grow in banking, insurance, public administration, telecommunications, and companies with long-term sensitive information.
6. Quantum Data Scientist
Explore the use of quantum or hybrid algorithms in data, optimisation and machine learning problems. You will need a solid foundation in statistics, programming, and experimental evaluation. Your work will heavily rely on comparing results against classical methods.
Common mistakes when talking about quantum computing
The first is to think that more qubits automatically means more useful capacity. The quality of the qubits, the connectivity, the gates, the error rate and correction matter as much as the quantity. A device with many noisy qubits can be less useful than a smaller, better-controlled one.
The second is to assume that quantum computing will break all cryptography immediately. The risk exists for certain algorithms, especially public-key ones, when sufficiently large fault-tolerant machines become available. The sensible response is to prepare post-quantum migrations, not act as if the problem has already been solved by attackers.
The third is to look for business applications without a clear metric. A quantum project must define what problem it is trying to improve, what classical algorithm it is being compared against, what resources it needs, and what result would be sufficient to continue.
The fourth is to forget the role of classical software. Quantum systems work with classical control, preprocessing, postprocessing, cloud, compilers, estimators, and analysis tools. The quantum part is just one piece of the complete system.
How to get started if you're interested in this field
The first step is to reinforce basic mathematics: vectors, matrices, complex numbers, probability, and optimisation. Following that, it's advisable to learn the fundamental concepts of qubits, gates, circuits, measurement, and entanglement.
From there, you can use simulators to run small circuits. You don't need to access real hardware from day one. In fact, simulators help you understand better what's happening before you encounter the noise of a physical machine.
It is also worth studying post-quantum cryptography from a practical perspective. It is one of the fields where companies can start working now, with inventories, compatibility testing, and migration planning.
For those coming from cloud, data, or artificial intelligence, the most sensible route is to connect current knowledge with quantum tools. A cloud profile can learn to access quantum services. A data profile can test hybrid models. A cybersecurity profile can focus on post-quantum standards.
Preguntas frecuentes sobre computación cuántica
What's the difference between a bit and a qubit?
The classical bit represents a 0 or 1. The qubit represents a quantum state described by amplitudes, which can be in superposition until measured. This property allows for the construction of algorithms distinct from classical ones.
Will quantum computing replace classical computing?
Not in general terms. It's most likely to work as a specialised technology for certain problems. Classic systems will still be necessary for the majority of business tasks.
Which sectors can benefit from it sooner?
Chemicals, materials, pharmaceuticals, energy, finance, logistics and cybersecurity are among the sectors conducting the most research into it. Adoption will depend on specific use cases and more reliable hardware.
Do you need to know physics to get started?
It depends on the role. For research or hardware, yes. For software, cloud, or post-quantum cryptography, you can start with mathematics, programming, and quantum computing fundamentals.
Is there any point in training now?
Yes, provided it's done with realistic expectations. The technology is still maturing, but there is already specialist demand for profiles who understand algorithms, cloud, security, and technical evaluation.
The next step
Learning quantum computing requires more than just knowing catchy concepts. It means understanding algorithms, hardware limitations, error correction, the cloud, and post-quantum cryptography. For tech professionals, it can be a useful specialisation if combined with a solid foundation in programming, data, cybersecurity o Cloud architecture.
