In this context, heat pipes, passive devices that transfer heat through the latent heat associated with phase changes of an internal working fluid, have become a key component in the thermal architecture of satellites and space missions. Their ability to transfer heat efficiently, without moving parts and with high reliability, makes them an essential solution for maintaining systems within safe operating temperature ranges.

However, the performance of heat pipes in space does not depend solely on their structural design. The working fluid circulating inside them ultimately determines their thermal behavior. In the space sector, ammonia has become one of the most widely used working fluids in heat pipes, as it provides highly efficient thermal performance within the temperature range in which these systems typically operate.

What Are Heat Pipes?

Heat pipes are hermetically sealed, two-phase heat transfer devices designed to transport high heat fluxes with minimal temperature gradients. Their operation is based on the phase change of a working fluid contained inside and on the action of a capillary structure that provides the driving force required to circulate the fluid and sustain the evaporation–condensation cycle.

Operating Principle

From a physical standpoint, the operation of a heat pipe is based on three main processes:

1. Evaporation: the working fluid absorbs heat and vaporizes in the high-temperature region.
2. Vapor transport: the generated vapor flows naturally toward the colder region, carrying heat with minimal losses.
3. Condensation and capillary return: in the condenser, the vapor releases latent heat and condenses. The resulting liquid returns to the evaporator through the capillary structure. This mechanism enables operation in microgravity conditions.

Heat pipes are fully passive systems: they do not require pumps, compressors, or external power input. Fluid circulation is driven solely by internal pressure differences and capillary forces. This architecture eliminates mechanical wear mechanisms, resulting in long operational lifetimes and extremely high reliability.

Advantages Over Other Thermal Control Systems

Compared to solid conduction-based solutions or active cooling systems, heat pipes offer clear advantages:

• Ability to transport high heat fluxes with minimal temperature gradients
• Uniform heat distribution across satellite structures
• No vibrations or mechanical interference
• Stable operation in microgravity thanks to capillary-driven return
• Lower structural complexity and reduced mass compared to active systems

In space platforms, where constraints on mass, volume, and power consumption are critical, these characteristics make heat pipes a technically superior solution for passive thermal management. In the absence of convection, heat can only be managed through conduction and radiation, further reinforcing their role within spacecraft thermal control architectures.

For a long time, research has been forced to work within this gap: detecting damage only once it was visible and intervening when the disease was already advanced. The ability to identify biological changes before symptoms emerge, together with a more detailed understanding of the underlying cellular mechanisms, is driving a shift that is not only conceptual. It also opens the door to earlier, more precise, and potentially more impactful interventions.

Neurological disorders are currently one of the leading causes of disability and mortality worldwide. It is estimated that more than 55 million people are living with dementia globally, a figure that could double in the coming decades. At the same time, the healthcare and economic burden associated with conditions such as Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and hereditary ataxias continues to rise in increasingly aging societies.

A heterogeneous group with shared biological foundations

The term “neurodegenerative diseases” encompasses a wide range of conditions that differ significantly from one another, yet share a common biological pattern: progressive neuronal loss, accumulation of misfolded proteins, and gradual deterioration of the mechanisms that maintain cellular homeostasis. What varies from one disease to another is less the nature of the process than its location, its progression rate, and the functions it ultimately affects.

Among the most studied are several conditions that, despite being well known, still pose major unanswered questions:

• Alzheimer’s disease, the most common form of dementia, initially presents as memory loss and progressive cognitive decline. Biologically, it is associated with the accumulation of beta-amyloid and tau proteins, which disrupt neuronal communication and ultimately compromise cell survival.
• Parkinson’s disease, characterized by loss of motor control, tremors, and muscle rigidity. In this case, the problem originates from the degeneration of dopamine-producing neurons, along with the accumulation of alpha-synuclein in structures known as Lewy bodies.
• Amyotrophic lateral sclerosis (ALS), a particularly aggressive disease that affects motor neurons responsible for voluntary movement. As these neurons degenerate, patients progressively lose the ability to move, speak, and breathe, while cognitive functions often remain intact in many stages of the disease.
• Prion diseases, much rarer but extremely rapid in progression, in which a misfolded protein triggers a chain reaction that alters healthy proteins, leading to severe neurological deterioration in a short period of time.

For years, research focused on these aggregated proteins as the main cause of neuronal damage. However, current knowledge points to a more complex scenario. Today, it is understood that multiple biological processes converge in these diseases, including mitochondrial dysfunction, alterations in energy metabolism, failures in cellular degradation systems, neuroinflammation, and genetic predisposition. This more integrated view, supported by recent studies, has shifted the focus from a single trigger to interconnected pathological networks, where multiple factors interact and reinforce one another.

From symptom to molecular mechanism

En In the study of neurodegenerative diseases, the clinical analysis of symptoms has long been the foundation for understanding and diagnosing these conditions. In recent years, this approach has been complemented by greater attention to the biological processes that precede those symptoms, expanding the ability to characterize disease in earlier stages.

In Alzheimer’s disease, for example, the criteria established by the Alzheimer’s Association now incorporate measurable biological indicators, such as beta-amyloid and tau accumulation, detectable through PET imaging or cerebrospinal fluid analysis even before symptoms appear. This shift enables identification of the disease at much earlier stages.

This approach has directly influenced therapeutic development. Antibodies targeting beta-amyloid protein, although their clinical results remain under debate, reflect an effort to intervene in the underlying mechanism rather than merely treating symptoms.

At the same time, genetic sequencing has made it possible to identify molecular subtypes in diseases such as ALS, as well as to classify the disease as sporadic (90–95% of cases) or familial (5–10%). Mutations in genes such as SOD1 have opened the door to targeted therapies, such as antisense oligonucleotides, designed to reduce the production of altered proteins. These strategies represent some of the first advances toward precision medicine in neurodegeneration.

ALS: a paradigm of scientific urgency

La Amyotrophic lateral sclerosis occupies a particular place within this landscape. It is a rare but devastating disease, with an average survival of three to five years after diagnosis, which typically occurs when symptoms have already been present for a long time. It selectively affects cortical and spinal motor neurons, leading to progressive muscle weakness and respiratory failure.

For years, therapeutic options were limited and had modest impact. However, the identification of genetic subtypes and a deeper understanding of the underlying cellular processes are reshaping research in this field. International initiatives such as the Project MinE consortium have generated large-scale genomic datasets to accelerate the discovery of therapeutic targets.

ALS illustrates how our understanding of these diseases is evolving. It is increasingly viewed not as a single uniform entity, but as a set of molecular alterations converging into a common clinical phenotype. This deeper biological characterization enables more targeted therapeutic strategies, although it also introduces new challenges in clinical trial design, which must adapt to greater diversity in patient profiles.

Beyond neurons: the role of the cellular environment

Another important shift is the recognition of the role of non-neuronal cells. The activation of microglia and astrocytes, alterations in the blood–brain barrier, and the involvement of the peripheral immune system are now understood as part of the pathological landscape. Neurodegeneration is increasingly seen as a multicellular process.

This broader perspective has driven the search for therapies that modulate inflammation, metabolism, or intercellular communication. The challenge lies in intervening without disrupting essential physiological functions, which requires a nuanced understanding of the biological balances involved.

Molefy Pharma and the search for new targets

In this context, biotech spin-offs are playing an increasingly important role in translating scientific knowledge into concrete therapeutic solutions. At ARQUIMEA, this commitment is embodied in Molefy Pharma, a company founded in 2024 with a clear objective: to develop therapies capable of changing the course of neurodegenerative diseases, with a primary focus on amyotrophic lateral sclerosis (ALS).

Unlike more traditional approaches, Molefy Pharma focuses its research on identifying new molecular targets that allow intervention in the cellular processes driving the disease. Its main line of work centers on the development of AP-2, a small molecule designed to restore cellular balance by regulating a key kinase involved in the homeostasis of the TDP-43 protein, whose abnormal accumulation is present in the vast majority of ALS cases.

This approach does not aim merely to slow disease progression, but to go a step further: to intervene in the mechanisms that originate the disease with the goal of reversing cellular dysfunction. Data obtained from patient-derived cellular models and preclinical animal studies show a promising profile in terms of both efficacy and safety, positioning these strategies as highly relevant within current therapeutic development.

The work of Molefy Pharma reflects a broader trend in the field: moving toward therapies that act on specific biological processes, integrating molecular knowledge with pharmaceutical development. In diseases such as ALS, where clinical urgency is particularly high, this approach not only expands the range of available options but also introduces a different expectation, the possibility of intervening more directly at the root of the disease.

In this sense, research in neurodegeneration is no longer limited to describing deterioration, but to understanding it with sufficient precision to attempt to modify it. It is at this intersection between fundamental science and applied development where initiatives such as Molefy Pharma find their purpose, providing new tools to a field that, until recently, offered very few real therapeutic alternatives.

Even so, collaborative robotics already has a discreet but tangible presence in real-world environments. According to the International Federation of Robotics, more than 55,000 collaborative robots were installed worldwide in 2023, mainly for assembly, logistics, and handling tasks. In practical applications, these systems can reduce repetitive physical strain in certain jobs by around 30%, according to industrial ergonomics studies, without replacing human supervision or decision-making.

This everyday use, although often invisible to the general public, marks a key difference from traditional automation. In certain scenarios, robots may share workspace with people without the conventional safety cages, provided that a proper risk assessment has been carried out and the corresponding safety functions are implemented. Collaboration does not imply the absence of protection, but rather a different approach based on force control, speed limitation, and environmental monitoring. Before asking how far this coexistence may go, it is worth pausing on a fundamental question: what truly defines a collaborative robot and what distinguishes it from other robotic systems.

What a Collaborative Robot Is (and Is Not)

A collaborative robot is defined less by the task it performs than by how it is configured to interact with people in a specific application. The term “cobot” is widely used, but collaboration is not an intrinsic property of the robot itself; rather, it results from a validated safety concept that enables human–robot interaction under defined conditions.

Unlike traditional industrial systems designed to operate physically separated from workers, collaborative applications may allow shared workspaces when a proper risk assessment has been conducted and appropriate safety functions are implemented, whether internal or external to the system. These include force and torque monitoring, speed limitation, environmental monitoring, and contact detection. Standards (such as ISO 10218 and ISO/TS 15066) provide the framework for these collaborative applications, offering context-dependent guidance rather than fixed universal limits.

It is also important to clarify that a cobot is not “intelligent” in a human sense, nor an autonomous system that improvises tasks. Its value lies in performing repetitive, precise, or physically demanding actions while the human operator retains control of the process, makes decisions, and resolves unforeseen situations.

From Factory to Workshop: Collaboration in Real Industry

Industry remains the main field for cobot deployment, but their use has changed in scale and context. It is no longer limited to large automotive plants, but extends to industrial SMEs, specialized workshops, and flexible production lines where volumes do not justify heavy automation.

In these environments, cobots typically handle tasks such as:

• Repetitive screwing and assembly
• Transferring parts between workstations
• Applying adhesives or sealants
• Camera-assisted visual inspection

The reason for their adoption is not solely productivity. In many cases, it responds to ergonomic challenges, shortages of skilled labor, or the need to adapt quickly to product changes. A cobot can be reprogrammed in hours or days, compared to weeks for traditional industrial systems.

Some manufacturers have documented cases where the robot does not replace the operator, but instead reduces physical fatigue and errors, allowing the person to focus on higher value-added tasks.

Healthcare and Laboratories

Outside industry, one area where collaborative robots have progressively found their place is the healthcare and biomedical sector, though far from fully automated, futuristic operating rooms.

In hospitals and laboratories, cobots are mainly used for:

• Handling biological samples
• Automated preparation of reagents
• Supporting physical rehabilitation processes
• Assisting with internal logistics tasks

A clear example can be found in clinical analysis laboratories, where robotic automation is used for tasks such as sample pipetting, tube sorting, or internal transport between workstations. In these environments, repeatability is critical: small errors in volume or handling can alter diagnostic results. The introduction of robotic systems has been shown to improve consistency and reduce operational variability, while also limiting staff exposure to biological agents.

In rehabilitation, some cobots function as controlled assistance devices, guiding repetitive movements in patients with neurological damage. Here, the key is not autonomy, but the ability to adapt to the patient’s strength and pace, something impossible with rigid systems.

Services, Logistics, and Shared Spaces

Logistics is another area where human–robot collaboration is becoming commonplace, even if it often goes unnoticed. In warehouses and distribution centers, cobots handle:

• Order picking
• Package sorting
• Assisting operators during long routes

Unlike autonomous mobile robots (AMRs), cobots typically operate at the same workstation as the human, sharing a table, shelf, or conveyor belt.

In services, their presence is more limited but growing. Some airports, libraries, and research centers use collaborative robotic arms for basic assistance tasks, educational demonstrations, or light maintenance. They do not replace human interaction, but expand the operational capacity of existing teams.

Technical and social limits that should not be ignored

Despite their expansion, collaborative robots are far from a universal solution. Their limitations are clear:

• Lower speed and payload compared to traditional industrial robots
• Dependence on relatively structured environments
• Careful integration required to ensure safety
• Costs that remain high for certain sectors

These are compounded by less technical but equally important challenges: social acceptance, workforce training, and the design of truly collaborative processes. Numerous studies in ergonomics and sociotechnical systems, published in journals such as Robotics and Computer-Integrated Manufacturing and IEEE Robotics & Automation Magazine, show that collaboration only works when the system adapts to the worker, not the other way around.

To What Extent Are They Already Part of Our Everyday Lives?

Collaborative robots have not entered daily life in a spectacular way, and that may be a good sign. Their integration has been gradual, pragmatic, and quiet, focused on solving specific problems rather than radically transforming environments.

They are not yet widely present in our homes, nor do they make decisions for us. But they are present in the products we consume, the healthcare processes that serve us, and the services we use, even if we do not always see them.

The underlying question is not whether we will coexist with cobots, but under what conditions: with which standards, what levels of transparency, what training, and what social objectives. Collaborative robotics presents an interesting opportunity precisely because it does not seek to replace people, but to redefine how we share work with machines.

Whether that collaboration will be truly useful, safe, and accepted will depend less on the technology itself than on the human decisions guiding its design and deployment. Ultimately, the critical factor is not the robot, but us.

Robotics at ARQUIMEA Research Center

At ARQUIMEA Research Center, we approach robotics with a clear premise: collaboration between humans and machines is not only a matter of physical safety, but of genuine understanding and integration in shared environments. Our research focuses on advanced perception, human–robot interaction, and the development of autonomous systems capable of operating in complex and dynamic contexts.

One example of this commitment is PULSAR HRI, an ARQUIMEA spin-off specialized in enabling safe and dynamic physical interaction between humans, robots, and environments. At PULSAR HRI, technologies are developed that allow robots to interpret their surroundings at high speed and move with human-like agility, dynamically responding to environmental changes and human interactions. The goal is not merely to automate tasks, but to enhance the quality of interaction and make collaboration more natural, efficient, and safe.

We believe that the future of collaborative robotics depends not only on faster sensors or algorithms, but on systems capable of integrating seamlessly into everyday life. That is the guiding principle behind our work: advancing toward robotics that does not replace, but complements and amplifies human capabilities.

Looking ahead to 2026, both fields share structural challenges such as growing technical complexity, distributed operations, and reliance on interconnected systems. In this context, mission-oriented engineering and industrial capability are emerging as key factors in ensuring performance across the entire life cycle.

Space Trends Toward 2026

The space sector is evolving toward a more operational model, where differentiation no longer lies solely in access to orbit, but in the ability to design, deploy, and operate space systems in a sustained manner.

Low Earth Orbit Constellations and Connectivity

The deployment of large constellations is transforming access to connectivity and data services on a global scale. The main challenge shifts to coordinated operations, where fleet management, service continuity, and system resilience are decisive. Through CanarySat, we are developing a constellation of satellites in LEO orbit to deliver resilient and sovereign communications.

Smaller Satellites, Greater Capability

Advances in miniaturization, onboard electronics, and system integration are enabling compact platforms to take on increasingly demanding missions. This supports more flexible and scalable architectures, shortens development timelines, and facilitates the deployment of constellations in low Earth orbit.

Technological Sovereignty as a Strategic Requirement

As space becomes established as critical infrastructure, technological sovereignty is taking on increasing importance. Having in-house capabilities in key technologies is essential to ensure security, control over critical systems, and operational continuity.

The Lunar Race: Return and Sustained Operations

Lunar activity is entering a new phase defined both by the return to the Moon and by the need to operate sustainably in an extreme environment. Limited communications, high levels of autonomy, and long-term reliability make the lunar environment a key setting for exploration and for the validation of critical technologies.

Autonomy and Resilience as a Common Denominator

All of these trends converge on autonomy as an essential operational capability. Space systems must be able to manage anomalies and adapt to changing conditions without constant ground intervention, reinforcing resilience as a key design criterion.

Defense Trends Toward 2026

In defense, the evolution follows a similar path. Priority is shifting toward real operational capability, where system integration, deployment, and sustainment are decisive.

Industrial Capability and Rapid Deployment

Production, scalability, and industrial resilience are becoming established as strategic factors. Industry is increasingly an active part of the defense system, ensuring availability and sustained response capability.

Autonomy and Artificial Intelligence in Operational Use

Artificial intelligence is moving toward integration into concrete operational functions, such as decision support, sensor management, and increased platform autonomy. Its value lies in reliable and secure integration into critical systems.

Uncrewed Systems and Coordinated Operations

Uncrewed platforms are evolving toward coordinated and swarm-based operational models. This approach increases flexibility and reduces risk, but requires high levels of autonomy, coordination, and integration with other capabilities.

Multidomain Approach and Interoperability

Modern operations are conducted simultaneously across multiple domains. Interoperability and secure information sharing are becoming essential requirements for operational effectiveness.

Cybersecurity as an Operational Pillar

Cybersecurity is no longer merely a cross-cutting concern, but has become an operational pillar. Resilience against cyber threats is integrated from the earliest design phases, on par with the physical protection of systems.

A Cross-Cutting View of Operational Capability

The trends shaping the space and defense sectors toward 2026 reflect a shared priority: having systems capable of operating with reliability, autonomy, and continuity in complex and demanding environments. The challenge is no longer only to develop advanced technology, but to integrate it and sustain it operationally over time.

From an industrial and engineering perspective, the space and defense trends toward 2026 reinforce the need for a mission-oriented vision, in which autonomy, interoperability, and resilience are built in from the design phase. In this context, ARQUIMEA addresses these challenges through cross-cutting experience in space and defense, with a focus on taking technologies from design and industrialization through to their operational integration under demanding requirements.

This year’s scientific and technological calendar will serve as a clear indicator of current priorities in science and technology. From artificial intelligence to energy and climate, these events will offer a concrete view of where research is heading and which topics are likely to shape its evolution in the medium term.

Artificial Intelligence

Mobile World Congress

📅 March 📍 Barcelona, Spain

March will once again place Barcelona at the center of the global technology map with a new edition of the Mobile World Congress. Although its origins lie in telecommunications, MWC has established itself as one of the leading international forums for discussing artificial intelligence, distributed computing, cybersecurity, edge computing, and applied quantum technologies.

Each year, it becomes increasingly clear that what is presented at MWC is not just devices, but the digital infrastructures that sustain entire sectors of the economy.

European Conference on Computer Vision (ECCV 2026)

📅 September 📍 Malmö, Sweden

From September 8 to 13, ECCV 2026 will take place, one of the most influential conferences worldwide in computer vision and artificial intelligence.

It is a key forum for advances in visual perception, generative models, and autonomous systems. Many of the technologies that underpin autonomous driving or medical image analysis today are first introduced at this conference.

Robotics and Automation

IEEE International Conference on Robotics and Automation (ICRA 2026)

📅 June 📍 Vienna, Austria

ICRA 2026 will be held from June 1 to 5 and is the world’s leading conference on robotics and automation. It is the reference forum for advances in autonomous systems, mobile robotics, manipulation, perception, and learning applied to control.

As in previous editions, PULSAR HRI, a spin-off born out of ARQUIMEA, will attend the conference and present its solutions in human–robot interaction, illustrating how robotics research is beginning to materialize into concrete, operational solutions.

European Robotics Forum (ERF 2026)

📅 April 📍 Stavanger, Norway

The 2026 edition of the European Robotics Forum will bring together researchers, industry, and policymakers from across Europe to discuss advances and challenges in applied robotics. ERF is one of the continent’s most relevant gatherings in this field, with a strong practical focus on collaborative robotics, autonomous systems, human–robot interaction, and industrial and social applications.

Biotechnology

BIO-Europe

📅 March 📍 Lisbon, Portugal

From March 23 to 25, BIO-Europe Spring 2026 will take place, one of the most important events in the European biotechnology sector. The meeting brings together companies, research centers, and investors with a clear focus on technology transfer, biomedicine, and life sciences.

The event reflects the growing convergence between biotechnology, data analysis, and artificial intelligence applied to the development of new treatments and industrial processes.

A second edition will be held from November 9 to 11, 2026.

European Congress on Biotechnology 2026

📅 June – July 📍 Antwerp, Belgium

From June 28 to July 1, 2026, ECB2026 will be held in Antwerp, one of Europe’s leading scientific conferences in biotechnology. Organized by the European Federation of Biotechnology, the event will bring together the research community and industry to address advances in applied biotechnology, biomedicine, bioengineering, and bioprocesses.

Its cross-cutting nature makes it a key event for understanding how European biotechnology is moving from basic research toward applications with real impact on health, industry, and the environment.

Quantum Technologies

5th Annual Commercialising Quantum Global 2026

📅 June 📍 London, United Kingdom

In June 2026, the 5th Annual Commercialising Quantum Global will take place, one of the most relevant European events dedicated to applied quantum technologies. The conference brings together researchers, industry, startups, and public-sector stakeholders to assess the real state of quantum computing, secure communications, and quantum sensing.

Unlike purely academic conferences, this event focuses on the transition from the laboratory to concrete applications. Hardware scalability, integration with classical systems, real-world use cases, and impact on sectors such as cybersecurity, defense, and finance are central to the program. In many ways, it is a strong indicator of how quantum technologies are beginning to move beyond the experimental stage.

The European Quantum Technologies Conference (EQTC 2026)

📅 November 📍 Dublin, Ireland

EQTC 2026 will be one of Europe’s most significant scientific events in quantum technologies, with a cross-disciplinary approach covering quantum computing, communications, simulation, and sensing. The conference brings together the European research community alongside industry representatives and public R&D programs.

Beyond theoretical results, the program typically focuses on demonstrators, shared infrastructures, and common technical challenges such as scalability, system reliability, and integration with classical technologies. In the European context, it is a key event for understanding how quantum technologies are transitioning from basic research to real-world applications.

Earth and Climate Sciences

EGU General Assembly

📅 May 📍 Vienna, Austria

In May, Vienna will host the General Assembly of the European Geosciences Union, one of the most important scientific conferences in Europe. Thousands of researchers will gather to share advances in fields ranging from seismology and volcanology to climate science, hydrology, and Earth observation from space.

Each year, the EGU acts as a true scientific barometer. Many of the emerging consensuses—and uncertainties—about the state of the planet and its complex systems appear first in this forum before reaching the public debate.

COP31 (United Nations Climate Change Conference)

📅 November 📍 Antalya, Turkey

In November 2026, COP31—the United Nations Climate Change Conference—will be held in Antalya, Turkey. The event will take place after several consecutive years of record global temperatures and increasingly precise IPCC reports on the evolution of the climate system.

Beyond political negotiation, COP31 will be a key meeting point for climate science. Advanced modeling, Earth observation, mitigation and adaptation technologies, and the use of artificial intelligence to anticipate extreme events will play a central role in the agenda.

Astronomical Phenomena

Total Solar Eclipse

📅 August 📍 Northern Spain

August 12, 2026, will be one of the most significant scientific dates of the year. Spain will experience a total solar eclipse visible from large areas of the northern part of the country—a phenomenon not seen under these conditions since 1905.

Beyond its visual impact, the eclipse will offer an exceptional scientific opportunity. Institutions such as the Instituto de Astrofísica de Canarias and several European research centers are preparing observation campaigns to study the solar corona, plasma dynamics, and the effects of the eclipse on Earth’s atmosphere. During the just over two minutes of totality, temperature drops and changes in animal behavior are expected, turning the event into a large-scale natural experiment.

CERN and Particle Physics in Active Pause

📅 2026 📍 Geneva, Switzerland

The European Organization for Nuclear Research (CERN) is the world’s largest particle physics laboratory and home to the Large Hadron Collider, an accelerator designed to recreate the most energetic conditions in the universe and study the fundamental particles that make up matter.

In 2026, the laboratory will enter what is known as Long Shutdown 3, a planned technical shutdown during which the accelerator is stopped to be upgraded and prepared for its next operational phase. Although the collider will be offline, work will continue in engineering, experimental design, and data analysis, laying the groundwork for the experiments that will define the next decade.

A Key Year for Science

Throughout 2026, other strategic conferences focused on technology transfer, industrial innovation, and applied science will also take place. These events will be essential for sustaining the spaces where research is shared, debated, and connected to real-world applications.

In this context, ARQUIMEA Research Center, within the framework of the QCIRCLE project, is an active part of the European scientific ecosystem. Through its ongoing work in areas such as artificial intelligence, robotics, biotechnology, and quantum technologies, its activity contributes to the same lines of research and reflection discussed at these major forums, reinforcing a shared environment of knowledge and collaboration.

The scientific calendar of 2026 outlines a network of interconnected spaces where visions align, advances are shared, and science is driven forward as a collective effort. These environments enable knowledge to progress in a coordinated way, connecting research, industry, and society, and laying the foundations for the developments that will shape the years to come.

The acceleration is particularly visible in areas such as data analysis, knowledge automation, and generative systems. The use of generative AI doubled in less than a year, and one in three companies now reports obtaining tangible value from these models in real-world operations. This pace of adoption is unprecedented in recent technological history and represents a structural shift comparable to electrification or the arrival of the internet.

However, this expansion is not neutral. As AI takes on functions of analysis, prediction, and recommendation in increasingly sensitive domains, a fundamental question arises: will we be able to adapt as a society to an environment where artificial intelligence does not merely assist, but actively shapes human decision-making? The answer depends not only on technical progress, but on our ability to integrate these systems with scientific rigor, ethical responsibility, and a clear understanding of their limitations.

Artificial intelligence

What distinguishes today’s artificial intelligence from previous technological waves is its role as a transversal layer. It does not simply optimize existing processes; it overlays almost every discipline based on data, rules, or patterns. From astrophysics to behavioral economics, AI acts as a cognitive infrastructure that redefines how knowledge is generated and validated.

In practice, this means that tasks which once required entire teams and long analytical cycles can now be completed in hours. In materials science, for example, machine learning models already predict the properties of new compounds before they physically exist in a laboratory. In some cases, prediction accuracy exceeds 80%, a level difficult to achieve even with traditional experimental methodologies.

This predictive capability does not eliminate the scientific method, but it does accelerate it and place it under tension. Hypotheses no longer always emerge from human intuition, but from correlations detected by systems capable of exploring possibility spaces far beyond the reach of an individual mind.

From support to delegation

As these systems become embedded in critical workflows, a key question emerges: at what point does support turn into delegation? In many professional environments, AI is no longer used solely to validate human decisions, but to actively propose them.

In sectors such as logistics, energy management, or financial planning, algorithms handle volumes of variables that are impossible to process manually. The result is tangible gains in efficiency, cost reduction, and, in many cases, greater operational stability. In smart electrical systems, for instance, AI enables the anticipation of demand peaks and real-time distribution adjustments, reducing failures and energy waste.

However, this capability introduces a progressive dependency. When systems perform well, their presence becomes almost invisible; when they fail, human intervention is no longer always immediate or straightforward. The boundary between supervision and delegation becomes blurred, particularly when decisions are made at speeds or scales that exceed human capacity.

Yet reducing this phenomenon to a risk alone would be incomplete. Controlled delegation also frees cognitive resources. By relieving professionals of constant optimization tasks or exhaustive analysis, AI creates space for strategic thinking, creativity, and higher-level decision-making. In scientific research, this dynamic is allowing teams to focus more on formulating meaningful questions rather than manually processing data.

Understanding the limit in order to advance

Discussing the challenges of artificial intelligence does not imply adopting a pessimistic view, but rather recognizing that any mature technology must acknowledge and manage its limits. In the case of AI, these challenges can be grouped into several main areas:

1. Bias inherited from data

Models learn from historical data, which reflect past human decisions. If those data contain social, economic, or cultural biases, AI systems tend to reproduce them. The advantage is that such biases can be detected and mitigated through audits, continuous evaluation, and improved data selection.

2. Opacity and difficulty of interpretation

Many advanced systems function as “black boxes,” particularly those based on deep learning. Understanding why a specific recommendation is produced is not always possible in a direct way. This necessitates the development of new methodologies for interpretability and validation that are better suited to current levels of complexity.

3. Technological dependency and erosion of judgment

Continuous automation can reduce human involvement in critical processes. The risk is not immediate replacement, but the gradual erosion of expert judgment. Designing systems with active supervision and human control points is essential to prevent this.

4. Scale, speed, and error propagation

AI operates at a scale and speed that exceed traditional control mechanisms. A failure can quickly propagate across multiple connected systems. The solution lies in more resilient architectures and clear protocols for detection and correction.

5. Legal framework and responsibility

Determining who is responsible for a decision supported by AI remains a challenge. The absence of clear rules can hinder adoption or generate distrust. Emerging regulatory frameworks aim precisely to provide legal certainty without blocking innovation.

Taken together, these issues constitute necessary points of attention. Identifying and addressing them systematically is part of the adaptation process.

Ethics and Regulation in Artificial Intelligence

Once the limits and risks of artificial intelligence are identified, ethics becomes an operational element. In contexts where intelligent systems influence increasingly sensitive decisions, clear rules are a necessary condition for sustained and reliable adoption.

In recent years, the approach has shifted from self-regulation toward binding legal frameworks. The European Union has led this transition with the development of the AI Act, a regulation that classifies AI systems according to their level of risk and establishes proportional obligations regarding transparency, governance, and human oversight. The aim is not to restrict innovation, but to create a common framework that reduces uncertainty and fosters trust.

This regulatory shift introduces enforceable standards in a field that until recently operated under diffuse rules. Requirements such as data traceability, technical documentation of models, and impact assessments are beginning to be integrated into the lifecycle of AI systems. According to the European Commission, around 60% of companies that develop or integrate AI solutions in the EU will need to adapt their internal processes to comply with these requirements, accelerating the sector’s overall maturity.

Beyond Europe, the regulatory landscape is diverse but convergent. While approaches vary across regions, there is growing consensus around core principles such as responsibility and transparency. Users tend to trust AI systems more when clear regulations define accountability, indicating that applied ethics not only protects rights, but also drives technological adoption.

In this context, ethics functions as a quality framework. It translates abstract values into verifiable criteria and reinforces the social legitimacy of artificial intelligence as a general-purpose technology, essential for scientific, economic, and social development in the years ahead.

Will we be able to adapt to AI?

The question is no longer whether artificial intelligence will continue to advance, but whether we will be able to accompany that progress with judgment and responsibility. All indications suggest that adaptation is possible, though it will not be automatic. It will depend less on the technology itself and more on how we choose to integrate it into our scientific, professional, and social processes.

The emerging scenario is not one of mass replacement, but of growing collaboration between people and intelligent systems. As AI assumes tasks of analysis and optimization, human value will concentrate on interpretation, contextual judgment, and strategic decision-making. Asking the right questions and supervising with expertise will be as important as developing increasingly powerful models.

This adaptation will require a cultural shift: moving from implicit trust in technology toward informed supervision. Continuous training, AI literacy, and the consolidation of ethical and regulatory frameworks will be key elements in making this coexistence sustainable.

In the medium term, artificial intelligence will become an almost invisible yet decisive infrastructure. Its true impact will not lie solely in efficiency, but in our ability to use it as an extension of human knowledge. The future will not be shaped by algorithms alone, but by the collective decisions we make about how and why we use them.

Artificial intelligence at ARQUIMEA Research Center

In this context, at ARQUIMEA Research Center we approach artificial intelligence as a transversal strategic capability. Through our Artificial Intelligence orbital, we research and develop solutions that combine advanced machine learning models with principles of safe autonomy, ensuring that intelligent systems operate in a reliable, traceable manner and under human supervision. Our approach focuses on integrating AI into complex and critical environments, from decision-making to system autonomy, always with a clear understanding of its limits, its impact, and its alignment with scientific, ethical, and safety criteria.

Climate change is a measurable reality: every year we emit more than 37 billion tons of CO₂, and 2024 was the first year in which the global temperature exceeded the 1.5°C threshold above pre-industrial levels. Overcoming this challenge requires a combination of structural and social changes, but also technological solutions capable of fundamentally transforming how we produce, consume, and manage resources.

This is where a new kind of innovation comes into play, one that seeks not only efficiency or profitability, but direct climate impact. From systems that capture carbon dioxide to algorithms that optimize energy use, science and engineering are beginning to rewrite the rules of sustainability.   

Almost 40% of the emission reductions required to achieve carbon neutrality by 2050 will depend on technologies that are not yet available at scale. The future of the global climate may therefore rely on innovations still being designed in laboratories and research centers.
Climate technology has thus emerged as a fast-growing ecosystem aimed at addressing the greatest urgency of our time.

What is Climate Technology?

The term climate technology refers to the set of technical, scientific, and industrial solutions developed to reduce the impact of climate change or facilitate adaptation to its effects. This definition encompasses both mitigation technologies, aimed at reducing greenhouse gas emissions, and adaptation technologies, which enhance the resilience of ecosystems, infrastructures, and communities to climate impacts.

However, beyond being a political or economic label, climate technology represents the intersection of environmental science, engineering, and digital innovation, where disciplines such as artificial intelligence, biotechnology, and nanotechnology are beginning to play a crucial role. Its purpose is twofold: to accelerate the decarbonization of productive systems and, at the same time, to rebuild natural processes that help restore climate balance.

From a technical standpoint, the concept can be divided into three main functional areas:

• Mitigation: includes the development of clean, low-emission technologies such as next-generation photovoltaic solar energy, green hydrogen, advanced energy storage systems, or direct air carbon capture (DAC), one of the most active research areas in environmental engineering.
• Adaptation: encompasses solutions that increase societies’ ability to respond to climate change, from genetically optimized crops that withstand extreme conditions to AI-based predictive models that anticipate weather events weeks in advance.
• Systemic transformation: covers the integration of cross-cutting technologies such as IoT sensors, smart grids, or large-scale climate analytics platforms that make it possible to model, simulate, and optimize complex systems, from power networks to urban ecosystems, to make them more efficient and sustainable.

This scientific and multidisciplinary approach positions climate technology as a new paradigm of climate-oriented innovation, in which applied research and data engineering play a central role.

Main Areas of Application

Climate technology operates across many sectors, but some concentrate the most relevant advances.

1. Energy and Storage    
   The energy transition is at the core of change. The challenge lies in storing that energy efficiently: new flow batteries, thermal systems, and green hydrogen solutions are proving that it is possible to balance electric grids powered by 100% renewable energy.
2. Agriculture and Biotechnology    
   Agriculture accounts for about 25% of global emissions, but it also offers one of the greatest mitigation opportunities. Precision technologies, IoT sensors, and genetically edited crops help reduce fertilizer use and optimize water resources. In 2024, researchers at Wageningen University developed wheat varieties that cut nitrous oxide emissions by 40%, combining molecular biology with artificial intelligence.
3. Carbon Capture and Reuse 
   Direct Air Capture (DAC) and geological carbon storage are becoming key components. The Mammoth project in Iceland will be the largest facility of its kind, capable of removing 36,000 tons of CO₂ per year, the equivalent of the annual emissions of 8,000 cars. Although still costly, this technology demonstrates that engineering can begin to reverse some of the accumulated damage.
4. Artificial Intelligence Applied to Climate        
   AI is consolidating as a cross-sectoral tool. From predicting extreme weather events to modeling ecosystems, algorithms make it possible to process massive amounts of climate data with unprecedented precision. In some cases, AI can reduce emissions in energy-intensive sectors by up to 10% through logistical optimization and predictive management.

Challenges

The development of climate technology faces a complex scenario that combines technical, economic, and structural constraints. Although innovations are multiplying, their large-scale adoption remains a challenge.

One of the main issues is the asymmetry in investment and technological deployment. Nearly 80% of private capital allocated to climate tech is concentrated in North America and Europe, while regions highly vulnerable to climate change, such as Africa or Southeast Asia, receive less than 5%. This imbalance not only slows global emission reduction but also widens resilience gaps between developed and emerging economies.

From a technical perspective, the challenge lies in improving the energy efficiency and circularity of the technologies themselves. Many climate solutions, such as energy storage or green hydrogen production, depend on critical materials, lithium, cobalt, nickel, or rare earth, whose extraction has environmental and geopolitical impacts. The development of sustainable catalysts, alternative materials, and advanced recycling processes will be essential to close the technological cycle cleanly.

Infrastructure and systemic integration limitations also persist. Climate technologies do not operate in isolation: they require smart power grids, decarbonized supply chains, and coherent regulatory policies that incentivize adoption. Without a coordinated vision between science, industry, and government, innovation risks remaining trapped in pilot phases without achieving real impact.

Finally, climate data management and predictive modeling are becoming critical components. The amount of information generated by sensors, satellites, and atmospheric models demands systems capable of processing it with high temporal and spatial resolution. Artificial intelligence and high-performance computing (HPC) are already key tools for this purpose, but they also require transparency and traceability to avoid bias in environmental decision-making.

In short, the challenge is not only to invent new technologies but to make them sustainable, scalable, and globally accessible maintaining a balance between innovation, environmental impact, and climate justice.

Climate Technology at ARQUIMEA Research Center

At ARQUIMEA Research Center, climate technology research is developed from an interdisciplinary perspective that combines biotechnology, robotics, artificial intelligence, and quantum technologies. The goal is to generate solutions that directly contribute to climate change mitigation and adaptation by applying science to environmental regeneration and ecosystem protection.

Among its most innovative lines of work is the development of a microbial consortium capable of accelerating the regeneration of soils degraded by wildfires, improving their structure, fertility, and capacity to sustain plant life.

Another line of research applies multimodal image fusion and artificial intelligence to the automatic detection of cetaceans, integrating optical, thermal, and acoustic data to monitor marine species with high precision. This approach helps reduce the impact of human activities on marine environments and reinforces biodiversity conservation as part of climate action.

Both projects reflect ARQUIMEA Research Center’s commitment to applied climate innovation, where frontier science is directed toward restoring the balance between human activity and natural systems.

In this new context, the so-called “gray zone” between peace and open warfare is expanding, and navies are adapting their doctrines according to their size and capabilities. Navies with more modest budgets aim to deny enemy access to the sea by creating A2/AD (anti-access/area denial) bubbles, while larger fleets seek to exert control over the maritime domain by breaking through these A2/AD defenses.

This is where naval loitering munitions systems come into play. These systems fit naturally into both approaches. For smaller navies, they provide an effective asymmetric defense tool capable of repelling attacks or preventing amphibious landings. For larger navies, they enable harassment and restriction of enemy mobility, limiting access to open waters or strategic ports.

Moreover, they contribute to engagement efficiency by employing the right tool for each threat. Against drones or low-cost targets, using other autonomous systems is more efficient than relying on complex or scarce weaponry, ensuring the sustainability of naval efforts in prolonged conflicts.

These autonomous systems can remain in operational areas for extended periods, identify targets independently, and execute precision attacks. They represent a new generation of systems combining operational resilience, persistence, and cost efficiency. They not only extend the tactical reach of a fleet but also transform the way it operates, enabling control of the maritime environment with more flexible, scalable, and distributed means.

The Application of Loitering Munition in the Naval Domain

The maritime environment presents unique challenges that make the integration of loitering munitions particularly valuable in naval operations. Conditions such as constant movement, radar reflections, humidity, wind, and communication constraints create a hostile setting for any guidance or sensor system.

Naval loitering munitions act as a capability multiplier within modern fleets, integrating into platforms that operate as part of a distributed maritime defense architecture. These systems can be deployed from surface vessels — enhancing detection and strike capabilities beyond the horizon — operate from coastal units or advanced bases to extend surveillance perimeters in littoral defense missions, or be launched from unmanned vehicles, forming a network of sensors and autonomous systems capable of sharing information and reacting in a coordinated manner. The goal is for them to evolve from simple precision weapons into active nodes within the naval combat system.

In an environment where response speed and precision are critical, loitering munitions emerge as a key solution to protect fleets, secure maritime routes, and strengthen deterrence — fully aligned with the trend toward networked systems where sensors, platforms, and weapons cooperate in real time.

Operational Advantages

Persistence and Extended Coverage

Loitering munitions can remain on mission for extended periods, patrolling vast maritime areas. Their ability to loiter allows continuous surveillance, real-time threat detection, target tracking, and reorientation when necessary. This contributes to the concept of a “transparent battlefield”, where nearly every element in the theater of operations can be detected and tracked within seconds through real-time information sharing.

Their persistence enables layered defense — acting as an intermediate link between detection systems and conventional strike platforms — and supports distributed or nodal fleet operations, where each unit operates autonomously but remains connected within a common network.

Precision and Reduced Collateral Damage

Thanks to local processing (edge computing) and Automatic Target Recognition (ATR) algorithms, loitering munitions can identify, classify, and track targets with great precision, reducing the risk of unintended impacts. These capabilities enable rapid action even in degraded or jammed communication environments, maintaining operational effectiveness without constant reliance on command links.

Operational Flexibility

Designed for integration within distributed architectures, loitering munitions can be launched from ships, UAVs, or surface vehicles and adapted for reconnaissance, protection, or strike missions. In contested logistics environments, this flexibility ensures sustained operations without dependence on large platforms, maintaining force endurance over time.

Reduced Human Risk

Their operational autonomy and man-on-the-loop control model — where the operator supervises the final decision — allow high-risk missions to be executed without exposing personnel. This capability is critical in prolonged operations or areas with dynamic, multi-layered threats.

Integration into Combat Networks

These systems can be integrated into C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance) architectures and communicate via LOS (Line of Sight) and BLOS (Beyond Line of Sight) links, both on the surface and underwater. Thus, they act as intelligent nodes within a distributed network, capable of receiving, processing, and sharing data in real time, reinforcing both tactical and strategic coordination across the fleet.

ARQUIMEA’s Naval Proposal

At ARQUIMEA, we develop loitering munition systems designed to meet the emerging challenges of the maritime domain. Our expertise in robotics, sensing, artificial intelligence, and autonomous systems allows us to offer versatile solutions that combine autonomy, precision, and adaptability across operational scenarios.

In the naval sphere, we have developed two families of loitering munition systems covering both surface and underwater environments, providing complementary solutions for different mission types.

On one hand, S-WISE leads our underwater range — a unique hybrid unmanned vehicle capable of operating both on the surface and underwater. It performs missions such as intelligence, surveillance, reconnaissance, and target neutralization. Its modular design and advanced control architecture ensure stable navigation even in adverse conditions, while onboard processing and vision systems enable intelligent mission management.

Complementing this capability, KRONOS is our surface loitering system, designed for patrol, protection, and rapid-response operations. KRONOS stands out for its precision, autonomy, and ease of deployment from multiple platforms, providing an effective solution for maritime defense and security.

The KRONOS family is structured into three variants: Mini, Light, and Heavy.

• KRONOS Mini and Light correspond to loitering munitions, optimized for surveillance, patrol, or rapid strike missions.
• KRONOS Heavy, on the other hand, acts as a carrier platform for Q-SLAM effectors or combined effector/sensor payloads — similar to the hybrid approach of S-WISE — capable of carrying and deploying other systems.

This scalable architecture allows system capabilities to be adapted to the environment and mission type while maintaining a common technological foundation.

Both systems are part of our integrated vision of future naval loitering munition systems, focused on creating interconnected ecosystems capable of coordinated operation above and below the sea.

The Future: Collaborative Systems and Intelligent Autonomy

The future of naval combat is moving toward a scenario where autonomy, cooperation, and decision speed will be decisive factors. Naval loitering munition is evolving from a precision weapon into an integrated element within intelligent defense ecosystems.

• Coordinated swarms and distributed decision-making: future systems will operate in intelligent swarms, sharing data and autonomously assigning targets.
• Integration with unmanned systems: combining loitering munitions with UAVs, USVs, and UUVs will enable more flexible and coordinated joint operations.
• Cognitive autonomy and assisted decision-making: advanced AI will allow systems to analyze environments and act in real time.
• Sustainability and modular industrial design: future developments will focus on modular, sustainable designs capable of adapting sensors, payloads, and software to each mission.

Naval loitering munition represents far more than a new category of weaponry — it symbolizes the evolution toward a more autonomous, connected, and adaptive defense model. Its ability to combine surveillance, precision, and inter-system cooperation is redefining how navies operate and prepare for emerging threats.

In an increasingly complex maritime environment, these technologies provide a decisive advantage: to anticipate, respond, and deter — with intelligence and efficiency.

At ARQUIMEA, we continue to drive the development of technologies that enhance deterrence, surveillance, and the protection of naval forces in an ever-evolving cooperative environment.

Although it promises to solve certain problems with radically superior efficiency, quantum computing is still in its early stages and faces practical limitations. This is where hybrid computing comes into play: an approach that combines the best of both paradigms to tackle challenges that neither classical nor quantum systems can solve alone.

As computing systems grow more complex, the world is generating volumes of data that are extremely costly to process within reasonable timeframes using traditional architectures. Problems like optimizing electrical grids, weather prediction, route planning for autonomous vehicles, or training AI models require massive computations involving thousands of interdependent variables. Solving them with classical algorithms alone can take hours or even days, whereas a hybrid approach is expected to reduce those times to seconds or minutes, thanks to quantum processors’ ability to explore multiple solutions simultaneously.

For example, in managing traffic in a large city, thousands of variables are analyzed every second—from vehicle flow to weather conditions—creating millions of possible combinations. Even the most powerful systems can only produce approximate solutions. In the future, a hybrid approach, where quantum and classical processors work in parallel, could optimize these scenarios, drastically reducing computation times and transforming urban mobility.

Hybrid computing is thus emerging as a natural response to the exponential growth of digital information and the need for faster, more accurate, and more energy-efficient solutions.

What is Hybrid Computing

Hybrid computing is a model that integrates classical architectures (based on bits) with quantum architectures (based on qubits) into a single workflow. While bits can only represent 0 or 1, qubits can exist in a superposition of 0 and 1 simultaneously, enabling them to process multiple possibilities at once.

The goal is to leverage the strengths of both paradigms: the stability, reliability, and scalability of classical computing, combined with quantum computing’s ability to handle complex combinatorial or probabilistic problems.

Put simply, it’s not about replacing one technology with the other, but about making them collaborate. In a hybrid system, structured tasks—such as data management or storage—are handled by classical systems, while the parts that require simultaneous exploration of solutions or the use of quantum correlations to detect hidden patterns are handled by quantum processors.

Hybrid computing requires the design of new algorithms, programming languages, and communication architectures that allow both systems to interact. It is not only a technological evolution but also a strategic transition into a new era of computing—one in which classical and quantum machines work as allies to solve problems that, until recently, seemed impossible.

Classification: Hybrid Arquitectures

Recent work defines a clear classification of hybrid architectures into two major categories: vertical and horizontal.

Vertical architectures focus on the integration of quantum hardware with the classical ecosystem (such as control systems, error correction, and interconnection hardware) in an application-agnostic way. Horizontal architectures, on the other hand, are more oriented toward logically dividing a specific algorithm between classical and quantum components.

This approach helps design more flexible systems: some applications may benefit from a more aggressive horizontal distribution, while others require strong vertical support to minimize latency, ensure coherence, and optimize overall performance.

Both models are complementary and, in practice, tend to coexist. Vertical architectures enable communication between systems, while horizontal architectures define how they cooperate during execution. Together, they represent the foundation of modern hybrid computing.

Use Cases

Hybrid computing is beginning to show tangible results in fields where data volume and complexity exceed classical computing limits:

• Quantum Chemistry and Molecular Design: In theoretical chemistry, hybrid algorithms are transforming the simulation of complex molecules. The approach combines the precision of quantum computing in modeling electronic interactions with the stability and optimization power of classical computing.
  A recent study showed that a hybrid model can predict the binding energy of molecular hydrogen with the accuracy of high-cost computational methods, but using significantly fewer resources. These advances open the door to designing new drugs and materials through more realistic and efficient simulations.

• Quantum Artificial Intelligence: In the AI space, the combination of classical and quantum processing is being explored as a new way to enhance the learning capabilities of models. In hybrid neural networks, quantum layers act as modules capable of handling highly correlated or complex data, complementing the strengths of classical deep learning.
  Researchers at MIT and the University of Toronto have shown that these networks can improve pattern recognition using fewer parameters and shorter training times, cutting computational costs by up to 30% for some classification tasks. Although still experimental, this line of research points toward a future where hybrid systems become standard in advanced machine learning.

These examples demonstrate how computing can merge two paradigms to address problems whose complexity once seemed insurmountable.

Key Challenges to Overcome

Despite its enormous potential, hybrid computing is still in a developmental stage and faces major technical and conceptual hurdles. Reaching maturity will require solving critical issues related to stability, communication, scalability, and standardization:

• Quantum noise and decoherence: Qubits are extremely sensitive to their environment and tend to lose coherence in milliseconds due to quantum noise and decoherence—meaning they lose the quantum properties that allow them to exist in a superposition of states. This introduces errors that degrade accuracy. More noise-tolerant algorithms and error correction techniques are needed to unlock the potential of quantum systems without compromising reliability.
• Communication latency and overhead: Every exchange of information between classical and quantum processors introduces delay. Reducing this back-and-forth latency is crucial for achieving noticeable quantum advantage.
• Specialized software development: Hybrid software ecosystems are still underdeveloped. While new programming languages and libraries are emerging, a more mature framework is needed to enable efficient and standardized hybrid algorithm design.
• Benchmarking and evaluation standards: There are still no standard metrics to reliably compare hybrid performance against pure classical or quantum alternatives.
• Scalability: Ensuring that a hybrid system works reliably at scale remains one of the biggest challenges. Integrating thousands of qubits, multiple QPUs, and vast volumes of data without performance degradation will be key to moving from experimental demonstrations to production-ready infrastructure.

Future Perspectives

Rather than a mere stepping stone, hybrid computing is emerging as a realistic path toward the broader adoption of quantum technologies in practical applications. Recent studies suggest that hybrid systems will continue to evolve even beyond the NISQ era (Noisy Intermediate-Scale Quantum)—a stage in which quantum processors still have limited qubit counts and high noise levels, yet are already capable of performing useful experiments.

The future lies in more stable hardware, low-latency interconnects, and increasingly efficient hybrid libraries. In this context, collaboration between classical and quantum computing will be critical to drive progress in AI, optimization, and scientific simulations.

ARQUIMEA Research Center

In this landscape of technological transformation, ARQUIMEA Research Center positions itself as one of the active Spanish players in promoting quantum technologies through the QCIRCLE project. From our headquarters, we are carrying out initiatives like EOLIQ, which focuses on developing hybrid (quantum–classical) AI models for time-series forecasting.

The center’s multidisciplinary approach contributes not only to scientific advancement but also to strengthening European technological development in a field that will be decisive for the next generation of digital infrastructure.

And there are good reasons behind this phenomenon. For example, a compound found in turmeric, curcumin, has shown anti-inflammatory and neuroprotective properties in preclinical studies, with potential to act on diseases such as Alzheimer’s and Parkinson’s. While solid human clinical trials are still needed, these findings help explain why the boundary between nutrition and pharmacology is becoming increasingly blurred. 

Nutraceuticals represent a promise of contemporary science: to intervene in human health through the everyday. But that promise coexists with legitimate uncertainties about their real effectiveness, regulation, limitations, and risks. Understanding their true scope means exploring not only what they are, but also how they work, what scientific evidence supports them, and what role they might play in the future of a medicine that is increasingly preventive, personalized, and evidence-based. 

What Are Nutraceuticals? 

Nutraceuticals occupy a middle ground between nutrition and pharmacology that has captured the interest of researchers, physicians, and regulators alike. They are bioactive compounds found in foods, such as antioxidants, fatty acids, polyphenols, amino acids, or probiotics, that, when isolated, concentrated, or formulated in specific doses, can have positive effects on specific physiological functions and help prevent disease. 

Unlike conventional foods, they are not consumed for their caloric or basic nutritional value, nor are they considered medicines. Their ambiguous nature, somewhere between food and therapy, is precisely what makes them such a unique and challenging category, both for research and regulation. In many cases, their action does not aim to replace biological processes, but rather to interact with them, supporting metabolic pathways, modulating inflammation, or boosting the immune system.  

From a biotechnological standpoint, nutraceuticals represent a way to redesign the relationship between diet and health at the molecular level. With tools like metabolomics, which allows researchers to analyze thousands of metabolites in a biological sample to understand how the body responds to a given compound; nutrigenomics, which studies how nutrients interact with our genes by turning specific metabolic functions on or off; or nanotechnology, which enables the encapsulation of active ingredients to enhance their absorption and efficacy, today, it’s possible to study how these compounds are absorbed, how they act in the body, and how they integrate into key cellular processes. This could ultimately lead to scientifically grounded therapeutic strategies tailored to individuals. 

In essence, nutraceuticals represent an effort to turn nutritional knowledge into a preventive or complementary intervention tool, based on molecular mechanisms that are now being identified with increasing precision. 

Mechanisms of Action: How do Nutraceuticals really work?  

Unlike essential nutrients, such as proteins, fats, or carbohydrates, whose primary roles are structural, energetic, or functional, nutraceuticals act more specifically on complex cellular processes. 

They function as biological modulators capable of interacting with biochemical pathways involved in inflammation, cellular aging, oxidative stress response, or even gene expression. 

Broadly speaking, their activity can be grouped into three major areas:  

• Inflammation Regulation: Many nutraceuticals influence immune system balance by modulating, not suppressing, the inflammatory response. For example, they act on signaling molecules such as NF-κB, which are responsible for the production of inflammatory cytokines. By doing so, they help reduce chronic inflammatory states without impairing the body’s natural defenses. This property is especially relevant in the context of non-communicable diseases such as type 2 diabetes, arthritis, or neurodegenerative decline.
   

• Oxidative Stress Modulation: Oxidative stress occurs when there’s an imbalance between free radicals (reactive molecules that damage proteins, lipids, and DNA) and the body’s ability to neutralize them. Some nutraceuticals, such as vitamin C, quercetin, and curcumin, possess antioxidant properties that not only eliminate these radicals but also activate endogenous antioxidant defense pathways.
   

• Enzymatic and Genetic Activity Modulation: Nutraceuticals can also influence the function of key enzymes or the expression of genes related to inflammation, metabolism, or cellular aging. For instance, compounds like resveratrol (found in grape skin) have shown epigenetic effects: they can activate or silence genes without altering the DNA sequence. This opens doors to the prevention of chronic diseases and the emerging field of healthy longevity. 

While these findings are highly promising, particularly in preclinical studies, a key challenge remains: low bioavailability of many compounds makes their clinical use in humans more difficult. To overcome this, biotech solutions such as nanoemulsions, microencapsulation, or lipid-based carriers are being developed to enhance solubility, stability, and targeted delivery of active ingredients to tissues. 

Real-World Applications and Clinical Evidence 

Nutraceuticals have genuine potential when supported by rigorous clinical evidence. For instance, recent reviews of nutraceutical interventions in hypertension report modest yet significant reductions in both systolic and diastolic blood pressure. 

Another area of promise is their use as complementary therapies: they can improve treatment adherence and help mitigate side effects—especially when adjusted to individual genetic profiles, which opens the door to personalized nutrition. 

In dermatology, compounds such as vitamins A, C, and E, collagen peptides, and carotenoids have clinical support for maintaining skin health and improving visible skin quality. And in the cognitive realm, recent studies on polyphenol-rich products have shown improvements in key brain functions and activation of neuroprotective biomarkers. 

Regulation 

The legal framework surrounding nutraceuticals is one of the most complex and variable aspects of the field. At the global level, there is no single definition or harmonized classification: in some countries they are considered dietary supplements, while in others they are closer to pharmaceutical products, depending on their composition, use, or health claims. 

In the U.S., nutraceuticals are marketed as dietary supplements under the Dietary Supplement Health and Education Act (DSHEA) of 1994. This law states that manufacturers do not need prior FDA approval to release products, as long as they do not make therapeutic claims, i.e., claims to cure, treat, or prevent disease. However, they must ensure product safety, follow good manufacturing practices, and report any serious adverse effects. The FDA can take post-market actions if problems are identified, such as conducting inspections, issuing public warnings, or ordering recalls. 

European Union 

In the EU, nutraceuticals are not recognized as a separate legal category but fall under the framework for food supplements. This regulation sets general principles for permitted ingredients, labeling, and marketing conditions, but does not allow therapeutic claims (i.e., the product cannot claim to prevent, treat, or cure diseases). 

Any health claim on the label must be backed by scientific evidence and approved by the European Food Safety Authority (EFSA). This agency evaluates whether studies submitted by companies meet strict standards for quality and reproducibility. Only then is the claim authorized for commercial use. 

Additionally, each EU member state may apply additional controls over sale and distribution, especially concerning botanical products, where national differences still exist. For example, in Germany, certain extracts may only be used under medical supervision, while in other countries they are sold freely as supplements. 

European regulation prioritizes consumer safety and transparency but still faces significant challenges in market harmonization and oversight, particularly given the rise of online sales and the influx of imported products from countries with more lenient regulatory systems. 

ARQUIMEA Research Center 

In line with this forward-looking vision, ARQUIMEA Research Center is participating in the NATUR-EXT project, an initiative aimed at harnessing renewable biological resources as a source of high-value compounds for industry, within a framework of circular economy and sustainability. One of the main goals is the development of procedures for the extraction and validation of emerging compounds of pharmaceutical and nutraceutical interest. 

The center’s contribution focuses on research into Salicornia europaea, a halophytic plant rich in high-value fatty acids. The work combines optimized cultivation techniques with the development of sustainable extraction methods, applying principles of green chemistry to maximize resource efficiency and minimize environmental impact. In doing so, the project opens up new possibilities for the discovery and validation of compounds with promising applications across various fields of science and health.