Hybrid computing: what it is and how it works

We live in an era dominated by classical processing: servers, networks, and CPUs power most of the digital tasks around us. But at the same time, a new technological wave is emerging, quantum computing.

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.