Quantum computing’s answer to the global food security problem

June 15, 2021

Editor’s note: Joseph Byrum is an Aspen Institute Business & Society fellow and spent 25 years as an R&D executive in the agricultural industry.

The views expressed in this guest article are the author’s own and do not necessarily represent those of AFN.


Even in the 21st century, food security remains a problem. Regions of the world most impacted by weather extremes and natural disasters need access to more resilient crops and growing techniques.

Over the last several decades, technology has rescued hundreds of millions from the brink of starvation. Selective breeding created better genetics to make crops more resilient and bountiful. Better machinery, fertilizers, and soil management practices improved fertility. More advanced chemistry for crop protection products kept weeds and insect pressure from crippling production.

Data analytics and AI-backed crop intelligence solutions represent the latest advance that can assist farmers to make smarter decisions and take action to resolve various crop pressures before they have a chance to affect yield.

These solutions are available today. But another potential tool in the food security arsenal could dwarf the yield contributions of everything that has come before. It’s called quantum computing.

This is a difficult subject to describe concisely, as what happens on the subatomic scale remains a bit mysterious. The late, great theoretical physicist Richard Feynman once admitted, “I think it’s safe to say that no one understands quantum mechanics” – and he won a Nobel Prize for his pioneering work on the topic. The important thing is that the quirky behavior at the quantum level can be analyzed mathematically.

From bits to qubits

In a conventional computer processor, we use the well-understood behavior of electricity interacting with silicon to make calculations. Feynman proposed 30 years ago that we might be able, in an analogous way, to harness subatomic behavior to analyze information in a quantum computer.

A quantum computing processor would analyze information in a different way than your MacBook or desktop PC, which breaks information down into bits of 1s and 0s. The quantum equivalent of a bit is a ‘qubit,’ which exists in a state somewhere between  0 and 1 during processing, with qubits able to affect the state of other qubits even when they’re physically separated. These two properties, known as superposition (holding a range of values) and entanglement (one qubit affecting another) wouldn’t be possible under the laws of physics we’ve been accustomed to since they were laid out in detail by Isaac Newton centuries ago.

Thanks to the qubit, a quantum computer’s power scales exponentially. That is, two qubits contain the same amount of information as four bits in a conventional computer. If you have three qubits, that compares to eight ordinary bits. Now, add another three qubits to your processor, and you’ll have 128 conventional bits. The quantum advantage is quite apparent once that number increases to something like 400 qubits, which matches the number of conventional bits measured by a 121-digit number. That’s more than a googol, the extremely large number after which Google named itself.

A quantum computer isn’t just a conventional PC that has been supersized. Quantum machines are entirely different and can only run certain types of calculations. In particular, they’re well suited to analysis of highly complex systems involving multiple interacting variables – just the sort of thing that’s helpful to growers faced with understanding the phenomenally complex processes of nature.

Plant genomes

Quantum hardware should make it possible to look for answers to questions we’ve never asked, because our conventional calculators can’t handle the size of the mathematics involved. For example, with humans there are 8 million possible combinations of 23 chromosome pairs, and these chromosomes can contain thousands of genes. Take just one of those genes, and there are in the order of 70 trillion possible combinations of alleles. Our genome has an estimated 30,000 genes, so even attempting to assign a number to the number of possible combinations quickly becomes meaningless. From a practical standpoint, it may as well be infinite.

Now, you might think a plant is nowhere near as complex as a human. But the soybean has about 46,000 genes. Directly deciphering which gene combinations produce the desired traits in soybean is as difficult as directly determining which combinations in a human to alter to prevent disease. Unlocking the full potential of either the human or the plant’s genome exceeds the capabilities of existing computers.

At present, we have to work around that problem. In the case of plants, instead of identifying the exact gene to change to, say, improve drought resistance, we crossbreed promising varieties and conduct yield trials — thousands upon thousands of them — to see if the new variety expresses an incremental improvement in yield and drought resistance traits. It’s not as simple as growing one plant and seeing how it does. You need to grow rows upon rows of the new variety so that you have a statistically significant sample. If you just measure a single plant, it may have done better because it had a more favorable location in the field, rather than improved genetics. The process as a whole usually takes about five to seven years.

Quantum computing opens the door to the possibility of skipping the crossbreeding process and directly identifying the genes responsible for important traits. CRISPR, an incredibly powerful genetic editing tool, could then be used to create a new variety with the desired traits that could proceed straight to the trials stage. All this would happen in a fraction of the time needed to bring elite genetics to market right now.

Quantum hardware’s depth of analysis would likely advance our understanding of the genetic code of plants (and humans) far beyond what we know today. At some point, it could even be possible to achieve not just ‘higher’ yield for a plant, but the absolute maximum yield possible for a given set of conditions.

It’s a fascinating prospect, but nobody knows exactly what the quantum future will look like. We do have glimpses of what lies ahead, as primitive quantum computers are already available for experimental use. Some experts say we’re within five to 10 years of having hybrid machines that use combine quantum and conventional processing to achieve a massive increase in capabilities.

The super-powerful quantum computers with hundreds or thousands of qubits worth of problem-solving power will take longer to develop. At the current rate of progress, it is quite possible they will arrive in time to provide the yield gains that farmers need to win the battle for food security.

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