Quantum computing for the factory floor

Quantum computers hold the promise of solving problems that classical computers simply cannot handle. In theory. In practice, however, they are incredibly delicate: A passing truck, a sudden hum from the air conditioning or even a light being switched on in an adjacent room can cause the system to react sensitively to environmental changes. That is why these devices have so far been confined to highly specialized laboratories, shielded like patients in intensive care. For companies aiming to integrate quantum computers into productive environments, this presents a significant challenge.

Hila Safi, alongside her colleagues, has pioneered a groundbreaking solution to this problem, earning her the Inventor of the Year Award 2025 in the “PhD” category. Their innovation is a digital twin designed to simulate precisely how a quantum computer would function and be integrated into a typical industrial setting. “With this digital twin, we can operate quantum computers in real environments – safely, stably and reliably,” explains the doctoral candidate at Regensburg University of Applied Sciences.

The problem: Too sensitive for the real world

Quantum computers rely on qubits – the fundamental quantum mechanical units of information. A qubit represents a physical system’s state, acting as an information carrier and leveraging quantum phenomena like superposition and entanglement.

These physical states are exceedingly fragile. Even minimal disturbances, such as electromagnetic fields or subtle changes in room structure, can corrupt calculations. While manageable in a controlled laboratory environment, these factors pose a significant challenge on a factory floor.

“A quantum computer’s reliable operation really depends on its surroundings,” Safi explains. “Even small vibrations or temperature changes can cause errors, which is why it’s so important to simulate and understand these effects early on.” Up until now, the industry has not found quantum systems reliable enough. Without strong and predictable results, their use in business is too risky. Many questions about where to place them, how stable they will be and their overall usefulness make it hard for companies to decide if and where to invest.

The solution: simulate first, then install

This is precisely where Safi’s innovative digital twin offers a breakthrough. It virtually replicates a quantum computer and its intended operating environment before physical installation. The model integrates data from environmental sensors, error statistics and simulations of potential interference sources with the known hardware characteristics.

This proactive approach enables crucial questions to be answered in advance: Can the quantum computer operate effectively within the industrial environment? What types of errors are anticipated? How significantly would they degrade computing quality? And what measures, such as enhanced shielding, alternative placement or adaptive calibration, would be required to stabilize the system?

For example, consider a production hall where transport robots are in use, manufacturing plants generate vibrations and power lines create electromagnetic interference fields. The digital twin simulates the precise impact of these factors on qubit stability, revealing where error rates would be tolerable and, crucially, where they would not. Furthermore, the twin remains active during operation: Should the environment change due to structural modifications or the introduction of new machinery, sensors will detect these alterations and assess their potential impact.

The benefit: Quantum computing becomes predictable

Thanks to the digital twin, companies finally have reliable data to guide their decisions about industrial quantum computing. They can now fully assess the risks before investing a lot of money and clearly understanding what is needed to make the system work stably.

As Safi explains, “In my research, I explore the co-development of quantum algorithms and hardware to address complex optimization and industrial challenges that are either intractable or highly inefficient for classical methods. This involves identifying problem classes uniquely suited for quantum computing and developing efficient modeling approaches.”

Her digital twin effectively bridges the critical gap between theoretical research and practical application. For quantum computers to successfully move out of specialized labs and into real industrial environments, they need to become just as stable, scalable and dependable as the IT systems we use every day.