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Quantum Computing: A Game Changer for Agriculture?
A team of researchers led by the Animal Breeding and Genomics group (ABG) of Wageningen University & Research (WUR) has published a landmark review on a technology often associated with physics labs and encryption: quantum computing.
Their comprehensive study, featured in Computers and Electronics in Agriculture, explores how this emerging field could impact agricultural and life sciences.
Unlike traditional computers that use bits, quantum computers use qubits - units that can exist in multiple states simultaneously and can be entangled with one another. “Quantum computers don’t simply perform existing operations faster, they allow us to approach problems in fundamentally new ways,” explains Torsten Pook, lead author of the study. “It’s like using a boat to cross the Strait of Gibraltar instead of driving around it in a race car.”
Quantum computing for agricultural challenges
The researchers evaluated a wide range of agricultural challenges that quantum computing could help address. These include optimising global food supply chains, interpreting satellite imagery for land use, and predicting breeding values in animal breeding programmes. The potential applications are vast.
Still, the team remains cautious about short-term expectations. Current quantum hardware is noisy and primarily limited to small-scale problems. However, with the Dutch government investing €200 million annually in quantum technology, rapid advancements are anticipated. Michael Aldridge, one of the co-authors of the study, emphasises the broader vision: “Quantum computing won’t replace traditional systems - it will complement them. What’s exciting is not just faster computing, but the entirely new questions we’ll be able to ask.”
Applications in animal breeding
Developing quantum algorithms requires both deep domain expertise and specialised knowledge in quantum mechanics. “Interdisciplinary collaboration with quantum experts from the Quantum Application Lab and TNO is absolutely crucial to our project’s success,” says Pook. Next, the team aims to dive deeper into concrete applications in animal breeding and to develop quantum algorithms for predicting breeding values that can handle increasingly large datasets and to monitor animals on lameness, sickness and aggressive behaviour through video analysis.
The full study is available as an open-access article here.