AI-generated summary
La computación cuántica representa una revolución en el procesamiento de información al utilizar qubits que pueden existir en múltiples estados simultáneamente, a diferencia de los bits clásicos. Esta capacidad expande enormemente el potencial computacional, permitiendo abordar problemas complejos en áreas como la logística global, el diseño de medicamentos y la ciberseguridad. Actualmente, esta tecnología está en una fase de aceleración impulsada por grandes empresas, startups y gobiernos, lo que genera una creciente demanda de talento especializado. Formarse en computación cuántica ofrece a estudiantes y profesionales la oportunidad de posicionarse en un campo emergente con alto impacto futuro.
El camino para especializarse incluye inicialmente comprender el cambio de paradigma que implica la computación cuántica, seguido de adquirir una sólida base técnica en matemáticas aplicadas, programación y algoritmos cuánticos. Posteriormente, es clave aprender a identificar casos de uso reales donde esta tecnología aporte valor, para finalmente especializarse en sectores específicos como ciberseguridad, inteligencia artificial, simulación molecular y finanzas. Estos ámbitos demandan perfiles interdisciplinarios capaces de integrar conocimientos técnicos y sectoriales para aplicar soluciones cuánticas efectivas.
No existe una única formación académica para ingresar a este campo, ya que la computación cuántica nace en la intersección de la ingeniería, la física, las matemáticas, la ciencia de datos y las ciencias empresariales. La clave está en el aprendizaje continuo y la adaptación a un entorno tecnológico en rápida evolución. Programas especializados y el contacto con expertos son fundamentales para desarrollar una carrera sólida y relevante en esta área que promete ser la próxima gran revolución tecnológica.
How to specialize in quantum computing, acquire key competencies and position yourself in one of the technologies with the greatest future impact.
Quantum computing proposes a new way of understanding computing. While classical computers work with bits that can only take two values, quantum systems use qubits capable of combining several states at the same time. This difference multiplies the possibilities of calculation and opens the door to solving challenges that today seem unattainable. For those who are defining their career path, getting into quantum computing means anticipating the future, acquiring scarce skills and being at the forefront of technological innovation.
Why now? The rise of Quantum Computing
For years, quantum computing advanced mainly in academic settings and research centers. Today it enters a decisive phase of acceleration. Large technology companies, deep tech startups and governments around the world are driving their development with investments, talent programs and pilot projects that connect research and real application.
This momentum responds to a change in scale in the challenges faced by companies and societies. Optimising global logistics chains, designing new medicines using molecular simulation or strengthening cybersecurity in the face of increasingly sophisticated threats requires new calculation capabilities. Quantum computing expands this framework and allows us to explore solutions that until now were out of reach.
This evolution has been analysed in depth by our Future Trends Forum, which in its report Quantum Computing and Artificial Intelligence points to the convergence between quantum computing and artificial intelligence as one of the technological vectors with the greatest transformative potential in the coming years. The report underlines a key point: the value of quantum is not only in the technology, but in the talent capable of understanding it, combining it and applying it to real problems.
For students and young professionals, this context opens a unique window of opportunity. The ecosystem is still under construction, specialized profiles are scarce and demand is growing steadily. Training now in quantum computing means growing together with technology, acquiring differential skills and positioning oneself from the beginning in one of the areas with the greatest projection of the digital future.
Roadmap: Steps to Specialize in Quantum Computing
Specializing in quantum computing opens up multiple paths. There is no single profile or depth of knowledge. The key is to move forward progressively, combining conceptual understanding, technical base, and orientation to real application. This roadmap proposes a realistic and accessible path, aligned with how the quantum ecosystem is evolving.
1. Understanding the paradigm shift
It all starts with understanding how the way information is processed changes. Before programming or designing algorithms, it is important to internalize what makes quantum computing unique.
As classical computing advances step by step, quantum systems take advantage of principles of physics such as superposition and entanglement to explore multiple possibilities simultaneously. This approach expands the computation space and is particularly effective for certain types of complex problems.
In this first stage it is not necessary to enter into advanced mathematical developments. The important thing is to acquire a solid intuition about:
- What is a qubit and how does it extend the capabilities of the traditional bit?
- Why some problems grow exponentially as they increase in size.
- In what types of algorithms and challenges quantum computing provides differential advantages.
Training programs that combine rigorous dissemination and applied vision, such as those promoted by Akademia Future Builders, are especially valuable at this point. They allow business, engineering or science profiles to understand the real impact of quantum physics and its connection with the professional world, without requiring previous training in advanced physics.
2. Build a solid (and practice-oriented) technical foundation
Once the conceptual framework is established, it is time to incorporate technical foundations. This step usually generates doubts, but the quantum ecosystem demands diverse and complementary profiles.
Most professionals working in quantum computing today focus on software, algorithms, simulation, or designing hybrid solutions. For these roles, key foundations include:
- Applied mathematics, especially linear algebra and probability.
- Programming, with Python as the reference language.
- Basic principles of quantum algorithms and their operating logic.
The goal in this phase is to understand how quantum concepts translate into computational models, not to master all the underlying physics. Structured training itineraries help to move forward with focus, avoiding unnecessary theoretical overload and connecting from the beginning with real cases.
3. Learn to identify real use cases
As knowledge is gained, a differential skill emerges: knowing where to apply quantum computing with meaning. Its value is not generic, but concentrates on very specific problems.
This is where specialization begins to be built. A relevant quantum profile is able to analyze a challenge and answer a key question: does this problem really benefit from a quantum approach or is it more efficient to address it with classical technologies?
This perspective is very present in the “Quantum Roadmap for Companies” of the Bankinter Innovation Foundation, which emphasizes practical application and the generation of real value. Quantum computing unfolds its potential when it is intelligently integrated into existing processes and responds to specific needs.
4. Specialize by sectors and applications
From this point, the paths diversify. Quantum computing acts as a cross-cutting technology that makes sense when combined with sectoral knowledge.
Finance, health, energy, logistics, cybersecurity and artificial intelligence are just some of the areas where promising applications are beginning to emerge. Real specialization is born by connecting quantum capabilities with a deep understanding of an industry and its specific challenges.
This crossroads between technology and context is where differential profiles are generated and where continuous training acquires a decisive role.
Sectors that will be revolutionized (career opportunities)
Quantum computing is beginning to show its potential where complexity exceeds the limits of traditional approaches. These are some of the sectors where their impact will be most visible and where new professional profiles are beginning to emerge.
Cybersecurity and cryptography: towards post-quantum security
One of the areas where quantum computing generates the greatest transformation is cryptography. Quantum algorithms such as Shor’s open up the possibility of solving mathematical problems that today support widely used encryption systems, such as RSA (after the initials of its creators: Rivest, Shamir and Adleman) and DH (Diffie-Hellman).
Far from being a theoretical scenario, this evolution is already mobilizing governments, international organizations and large corporations, which are working on the development of post-quantum cryptography: systems designed to protect information in a context where quantum computers will be part of the technological ecosystem.
This change drives new career opportunities, including:
- Specialists in post-quantum security.
- Cryptographic transition consultants for organizations.
- Experts in analysis and management of risks associated with quantum.
For profiles interested in cybersecurity, quantum computing is emerging as an area of specialization with strong future demand.
Artificial Intelligence and Machine Learning: Accelerating Learning
The relationship between artificial intelligence and quantum computing is getting closer and closer. On the one hand, quantum approaches promise to speed up certain processes of model optimization and training. On the other hand, artificial intelligence is already used to design, tune and improve quantum algorithms.
This convergence is among the most active lines of work in the current technological ecosystem and occupies a prominent place in the Bankinter Innovation Foundation’s “Quantum Roadmap for Companies“. The message is clear: quantum and AI are mutually reinforcing.
For those who are already training in data science or machine learning, quantum computing acts as an amplifying factor. It does not replace previous knowledge, but expands it and opens up new possibilities of application.
Molecular and pharmaceutical simulation: designing on the computer
The simulation of complex molecules represents one of the great computational challenges of today. As the number of particles and interactions increases, the computational effort grows exponentially. Quantum computing, being based on the same physical principles that govern these systems, offers a particularly suitable approach.
The potential impact is significant:
- Design of new drugs through advanced simulation.
- Development of materials with optimized properties.
- Reduction of time and costs in research and development processes.
This field stands out as one of the first where practical applications with tangible economic impact are expected, which makes it a particularly attractive career opportunity for STEM profiles interested in health, chemistry or materials.
Finance: Optimization and Detection of Complex Patterns
The financial sector is constantly working with extreme optimization problems: portfolio construction, risk management, scenario analysis or fraud detection. Many of these challenges fit well with hybrid quantum approaches, which combine classical and quantum computing.
Currently, financial institutions and fintechs are exploring pilot projects in areas such as:
- Advanced portfolio optimization.
- Real-time complex risk management.
- Fraud detection using quantum-classical hybrid models.
In this context, the most demanded profile is not that of the theoretical physicist, but that of the professional capable of translating business challenges into advanced technological solutions, connecting financial knowledge, data and new computational capabilities.
What degree to study for Quantum Computing?
Quantum computing was born at the intersection of several disciplines. For this reason, there is no single academic itinerary to get there. The quantum ecosystem values diverse profiles, capable of providing complementary perspectives and connecting technology, science and real application.
The most common paths start from:
- Engineering, such as computer science, telecommunications or industrial, where a solid foundation in systems, programming and complex problem solving is acquired.
- Physics and mathematics, key disciplines to understand the foundations that make quantum computing possible.
- Data science and artificial intelligence, areas increasingly connected to the development of algorithms and hybrid quantum-classical models.
- Economics, business or administration, when combined with specific technical training and a clear orientation to the application of technology in real contexts.
Beyond the initial degree, what makes the difference is the ability to continuously learn, adapt to a rapidly evolving technological environment, and translate the potential of quantum computing into concrete solutions. In this sense, quantum physics is not presented as a closed destination, but as a space of opportunity that can be reached from multiple starting points.
The importance of lifelong learning in a dynamic industry
Quantum computing is advancing at a dizzying pace. What has been learned today may become obsolete in a few years. Therefore, more than one-off training, it requires a continuous learning approach.
Programs such as those of Akademia Future Builders stand out precisely for this reason: they not only transmit knowledge, but also context, strategic vision and direct contact with experts who are building the future of this technology, such as Esperanza Cuenca, Head of Quantum Computing Developer Relations at NVIDIA, Carlos Kuchkovsky, co-founder and CEO of QCentroid and Víctor Canivell, co-founder of Qilimanjaro Quantum Tech.
Specializing in quantum computing is not a short-term gamble. It is an investment in employability, professional relevance and technological leadership capacity.
The time is now. Those who start building this knowledge today will be in a position to lead the next great technological revolution tomorrow.
