Quantum Intelligence (part II): Problems (complex) and solutions (complex)

Quantum computing is starting to be applied in areas such as machine learning and optimization. Find out about the real possibilities of this technology to address complex problems and learn about current trends, from Quantum-as-a-Service to our position in the quantum ecosystem. I invite you to read a practical and realistic vision of this field. In this second part we will talk about where it is useful (and where it is not) and the tools available.

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Quantum computing is exploring its potential to address complex challenges in three key areas: optimization, simulation of complex systems, and process improvement in artificial intelligence and machine learning. It is already being applied in practical cases where its ability to process information differently from classical methods offers promising advantages, although it is still an emerging technology.

Optimization

Optimization problems, which seek the best solution among a large number of possibilities, are an area where quantum computing stands out. Quantum algorithms already exist to tackle such problems. Examples such as logistics route planning or supply chain management require evaluating millions of combinations to find the optimal solution.

Quantum computing addresses these challenges through algorithms such as the Quantum Approximate Optimization Algorithm (QAOA), which explores solutions in parallel by exploiting quantum superposition. In practice, the results of these algorithms have to be combined with classical systems in systems that we can call hybrids.

Simulation

This is one of the most natural applications for quantum computing, as classical computers face limitations in modeling systems that follow the laws of quantum mechanics. Simulating complex molecules or advanced materials, for example, requires computational resources that grow exponentially with the size of the system.

Quantum processors, on the other hand, can inherently represent these states, which accelerates the study of atomic interactions. Researchers and companies are using this capability to design more effective drugs or innovative materials.

Improving AI and machine learning

In the specific field of machine learning, quantum computing is being investigated to accelerate tasks such as parameter optimization in predictive models. Quantum algorithms could identify patterns in large data sets by processing information in high-dimensional spaces.

However, current hardware limitations force the use of hybrid approaches, where the quantum part is reserved for specific subtasks, such as the selection of relevant models within an ensemble algorithm (an approach that is already available on some platforms).

Ecosystems and technologies

The quantum computing industry is made up of large technology companies as well as specialized startups.

Large tech companies are creating ecosystems that combine quantum processors with classical servers and Quantum as a Service (QaaS) offerings, providing access to quantum computers through the cloud.

Hyperscalers are also active in this market, developing their own hardware (Willow, Majorana, Ocelot, etc.) along with libraries and programming languages (Cirq, Q#).

Among emerging hardware manufacturers, notable companies include D-Wave Systems, Atom Computing, Xanadu, IonQ, and Quantinuum, among many others.

Developer tools

Quantum development relies on frameworks that abstract hardware complexity. Qiskit has become the de facto standard, followed by Cirq and Pennylane. While these libraries can be used in popular programming languages like Python, there are also quantum-specific programming languages, such as Q#.

Access to quantum technology

There are currently two main approaches to working with quantum computers:

First, you can deploy a quantum computer. This is the more expensive option but also the one that allows intensive use with controlled budget. Typically, a hybrid quantum-classical system will be installed where quantum processors will be used for specific subtasks where they can offer an advantage.

Secondly, and as we have advanced, there is Quantum-as-a-Service (QaaS), which is a cloud service model that provides remote access to quantum computing resources. This approach allows experimentation with quantum technologies without significant upfront investments.

QaaS offers key advantages, such as access to up-to-date state-of-the-art technology or scalability according to project needs.

Quantum vs Quantum inspired

There are also companies in the market that sell quantum inspired computing rather than quantum computing. It is important to understand the differences.

Ilustración 4: Quantum vs Quantum Inspired

Quantum-inspired computing is an approach that applies mathematical principles and methods from quantum physics to develop improved classical algorithms, without the need for actual quantum hardware. It should be noted that it cannot match the potential of quantum computers for complex problems and its performance is limited by the classical hardware that supports it. Although they do not take advantage of quantum phenomena, these methods seek to replicate some theoretical advantages of quantum algorithms to solve specific problems.

In this line, HPC (High Performance Computing) systems can act as a temporary bridge towards the adoption of quantum computing, especially in tasks that require massive processing, but do not rely on quantum advantages.

On the other hand, and as you should know by now, quantum computing is a radically new paradigm that leverages the principles of quantum mechanics to process information.

Telefónica and quantum computing

Telefónica is consolidating its position in the European quantum ecosystem through a strategy based on public-private collaboration and deployment of specialized infrastructures. Several key projects stand out in 2025:

Alliance with Diputación de Vizcaya

Here at Telefónica España became a technology partner of the Diputación de Vizcaya in February 2025 to drive its quantum industrial strategy. This collaboration includes the installation of Fujitsu's first Digital Annealer outside Japan, a hybrid system that combines quantum-inspired techniques with classical supercomputing. Hosted at our Telefónica headquarters in Vizcaya, this equipment will solve problems in multiple industries.

Center of Excellence in Quantum Technologies

Unveiled at MWC 2025, this center coordinates all of our Telefónica's quantum initiatives, focusing on three pillars:

• Quantum communications and cyber security: Development of secure quantum networks and migration to post-quantum algorithms to protect critical infrastructures.
• Computation and simulation: Integration of quantum processors with classical supercomputing for practical cases in optimization and machine learning.
• Quantum sensors: Research in precision metrology for telecommunications and network monitoring.

Telefónica Tech and IBM collaboration

At Telefónica Tech we also collaborate with IBM on quantum-safe solutions, integrating technologies to protect critical data against future quantum computing threats. This alliance includes the deployment of cryptographic infrastructures in Madrid and the creation of a joint laboratory.

In addition to these public collaborations, Telefónica Tech continues to develop its internal capabilities to be able to offer its customers quantum computing and tackle projects to solve problems by combining AI and quantum.

As you can see, while we continue to see advances in hardware and real applications, one thing is certain: quantum computing is not only the future, it is already here. So, get ready for an exciting journey. Don't miss out!

Photo (cc) of a model of IBM Quantum System One, at Shin-Kawaski for the University of Tokyo. IBM Research.