Computing fertilizer

Daniel Pérez Lozano · 29-06-2021 10:00 · Winners "Your research in one image or 1000 words" - 1st Edition

Urine, commonly known as pee, is a waste product of our bodies that contains urea, a nitrogen-rich fertilizer used in agriculture nowadays. Although research has shown that pee can be used as fertilizer to improve crop growth, the logistics of collecting pee from every person in a city makes it impractical. Imagine having a separate toilet in your home connected to a pipe system that runs parallel to the current sewerage and collects the pee of an entire city to store it enormous tanks or “pee lakes”, given the volume that would be collected. Sounds very challenging.

Figure 1. Artistic impression of a parallel pipe system for urine fertilizer.

Luckily, there exist ways to produce big amounts of fertilizers that would save us from using our own pee. Between 1909 and 19010 Fritz Haber, Robert Le Rossignol and Carl Bosch developed a process to synthesize ammonia at an industrial scale. This process is known as the Haber–Bosh process and consists of transforming hydrogen and nitrogen into ammonia using a chemical reactor at high pressure and temperature. Then, ammonia can be used directly as a fertilizer or can be transformed into other fertilizers such as ammonium nitrate or urea.

Figure 2. Ammonia synthesis reaction.

This development, in combination with other developments in agriculture technology, made possible to feed billions by multiplying crop yields across the world: wheat yields in France and China were 5.8 and 3.8 times larger in 2000 compared to 19000. US corn yield rose from 1.6 tons per hectare in 1900 to 8.5 tons per hectare in 2000. Moreover, this increase in crop yield was reached without increasing the land used for agriculture by the same factor. However, the Haber – Bosh process has high environmental cost. Currently, it consumes around 1% of the world’s energy supply.

In the meanwhile, nature can transform hydrogen and nitrogen into ammonia without breaking a sweat. Some bacteria found in the soil, roots and algae can carry out the same process at ambient conditions: no high temperatures, no high pressure, no chemical reactors. These bacteria have the capability of synthesizing an enzyme called nitrogenase, which can fabricate ammonia. Despite we know the structure and composition of nitrogenase, nobody has been able to understand the exact mechanism. Experiments have not been able to provide enough details about the chemical mechanism, and chemical simulations with the needed degree of accuracy are far beyond our current computation capabilities. Figuring out how nature synthetizes ammonia would have enormous implications. For instance, the world’s energy consumption would decrease about 1%. This is equivalent to all the electricity that was consumed by Spain in 2019.

Figure 3. Nitrogenase enzyme.

Is there any possibility of unraveling the secrets behind nitrogenase?

In 2017, a group of researchers showed that it can be possible to simulate with enough accuracy how nitrogenase synthetizes ammonia if a quantum computer is used.

A quantum computer is a device that process information, just as a regular computer, but it uses the rules of quantum mechanics. This means that it can take advantage of quantum phenomena like quantum superposition, interference or quantum entanglement to make more efficient computations. Computations that would require thousands of years in a regular computer could be performed in hours or days in a quantum computer.

Regular computers use classical bits, 0s and 1s, to process information. For instance, when you use an Instagram filter what your phone really does is to process the information of your picture and modifies it accordingly with the effect of the filter. The 0s and 1s that codify each of the pixels of your picture are summed, multiplied and shuffled following the filter algorithm.

In the case of a quantum computer, classical bits are converted into quantum bits or qubits. In addition of using 0s and 1s to compute, a quantum computer can use a quantum superposition of 0s and 1s to process information. This means that a quantum bit can be in a state which is 0 and 1 at the same time, similarly to how a cat is both dead and alive in Schrödinger cat’s paradox. Thanks to the quantum phenomena, we can do more than summing, multiplying or shuffling the 0s and 1s that compose our selfie. If we had a quantum Instagram filter, we would be able to modify all pixels in our selfie simultaneously, contrary to classical Instagram filters where pixels are modified one by one or a bunch of them at a time.

Figure 4. Abstract representation of regular bits and quantum bits.

Simulating chemical reactions and processes such as how nitrogenase synthetizes ammonia is a natural fit to the applications of quantum computers, since chemistry is governed by the rules of quantum mechanics at its very core. However, the possibilities do not stop there. Quantum computing has applications in finance, cybersecurity or material science. Certain quantum algorithms provide a significant speed up over the current classical algorithms. This translates into the fact that problems that are nowadays intractable due to the amount of computing time they require will be very soon accessible.

Quantum computers are a reality nowadays, but they are not powerful enough yet to tackle the problems that will make them glow up. Companies like Google, IBM or IonQ have small quantum computers where they can run test algorithms that serve as a proof on concept and allow to understand better the requirements and challenges for scaling these systems to bigger sizes.

Figure 5. IBM quantum computer, Photo taken at 2018 ASCE (Credit: Graham Carlow).

As a quantum computing scientist, I have taken up the challenge. Together with my colleagues, we are looking for solutions to these big challenges. Our research focus on making the fabrication of quantum computers compatible with the tools that are typically used for manufacturing regular computers. However, regular computers are made of silicon, a semiconducting material, while quantum computers use niobium or aluminum, metals that can be turned into superconductors. Moreover, we are investigating new materials that can be used to improve the quality of current quantum computers, as well as processes that enable their fabrication at a large scale.

Doing research in quantum computing might seem more like solving engineering problems rather answering scientific questions. The way I see it is that we are enabling a powerful piece of hardware with the ability to help thousands of researchers answering their scientific questions, that, like in the case of nitrogenase and ammonia production, will help us develop a more sustainable future.

About the author:

Daniel Pérez Lozano was born in Madrid's Aluche neighborhood in 1990. After graduating in physical sciences from the Complutense University of Madrid, he moved to Leuven, Belgium, where he obtained his doctorate on superconducting materials at the Catholic University of Leuven. Following this, he moved to Goteborg, Sweden, to begin a post-doctorate at Chalmers University within the OpenSuperQ: A Quantum Computer for Europe project. After a year and a half of postdoc, he packed his bags again to return to Belgium where he is doing a second postdoc on quantum computing at IMEC, an R&D center for nano and digital technologies.

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