Public digital transformation has been driven by artificial intelligence, automation, and big data. But a new horizon is emerging: quantum computing. In this new paradigm, quantum numbers in artificial intelligence represent not only a theoretical advance, but also a practical disruption that could redefine predictive analysis, model training, and the processing of large volumes of public information. The question is no longer whether it will arrive, but when and how it will be integrated.
In physics, quantum numbers describe the state of a subatomic particle (such as an electron), including its energy, position, and behavior. In quantum computing, these principles give rise to the qubit, a unit of information that can exist in multiple states simultaneously, unlike the classical bit. This allows complex operations to be performed with exponential efficiency. In IA, this property promises faster, more adaptive models capable of handling data with interdependencies that are impossible to resolve in traditional systems.
The incorporation of quantum numbers into artificial intelligence promises to profoundly transform public management by enabling faster, more efficient, and more predictive models for processing big data. This capability opens up new possibilities for optimizing key government processes, such as predicting demand for citizen services, early detection of fraud or irregularities, simulation of complex scenarios in public health, and improved decision-making based on interdependent data analysis. In addition, quantum AI can accelerate information processing in sectors such as mobility, energy, and education, enabling a more accurate and intelligent digital transformation that is aligned with the dynamic needs of society. Ultimately, this approach positions the government not only as a user of technology, but also as a protagonist in the new digital frontier.
The transition to quantum-inspired artificial intelligence requires a new institutional mindset. To prepare for this, public entities must strengthen multidisciplinary teams that integrate scientists, data engineers, and public sector analysts capable of interpreting both the challenges facing the state and the opportunities offered by emerging technology. Although quantum computing is not yet widely available, it is already possible to explore quantum machine learning algorithms through simulations, hybrid environments, and open platforms. It is recommended to invest in training and experimentation applied to specific issues— such as climate prediction, public health, logistics, or economic modeling prioritizing pilots that connect science with public decisions. In addition, it is essential to establish partnerships with universities, research centers, and companies in the quantum ecosystem to access infrastructure, talent, and specialized methodologies. Finally, any digital transformation strategy should consider a roadmap for the progressive adoption of quantum technologies, aligned with the government’s institutional data architecture, AI strategy, and interoperability objectives.
Artificial intelligence and big data are migrating from the classical to the quantum realm. The rules of the game are changing: we no longer talk about one or zero, but about multiple simultaneous possibilities, just like in the real world. Quantum numbers are no longer just formulas on blackboards: they are the key to understanding and modeling the complexity of the 21st century.
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