Identifies ‘Use Case’ for Quantum Computing in Agriculture

Just recently, published a graphic outlining their use case for quantum computers in agriculture on their website. In it, Dr. Quang Nguyen and his team outline the problem: different crops require different resources and nutrients in order to maximise their crop yields. Their solution? To create a system “that helps farmers apply the right crop input sources at the right rate, the right time, and in the right place.”

The team has done this by optimizing water and crop inputs with certain constraints. First, the land is divided into different blocks which each have different nutrient requirements. Second, each block must be assigned with a set amount of water and fertilizer in order to optimize their yields. Third, a “linking constraint” will require the amount of water in one row of blocks to be the same while each different block can have a different amount of nutrients. Finally, the total amount of nutrients and water must be kept below a certain amount.

Using D-Wave’s hybrid classical/quantum solvers, the team has been able to apply nine different prescriptions to fields with more than one million subfields. They found that as the problem became more and more complex, the run-time of a classical solver, Gurobi, was much higher than that of a quantum solver and the results of the quantum solver became better than the results of Gurobi. It is great that the company has taken such a huge step forward in the quest to apply quantum computing to agriculture and I look forward to the work that Dr. Nguyen and his team at will put out in the future.

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