Contributed Content: The Quest For The Ultimate Seed Begins with Quantum Computing
By Joseph Byrum
It takes hundreds of millions in research and development funding to create a world-beating germplasm pool that meets the individual needs of growers facing a variety of tough conditions. Not surprisingly, only a handful of companies have the financial clout to develop elite genetic traits. The seed industry’s top four firms spend an average 18 percent of their sales on R&D, matching the investment level of the big pharmaceutical firms.
This prerequisite represents a massive barrier to entry in this field, but the barrier could fall in the years ahead if the quantum computing revolution takes hold.
It’s unlikely that backyard plant breeders are going to rival the big guys any time soon. Developing seeds into a viable commercial product at scale is a massively complex and labor-intensive undertaking. That won’t change. But quantum computing promises to provide a developmental shortcut that will bring amazing new products to the market faster, and perhaps allow smaller players to step up their game in ways never before possible. This is all good news for farmers who will reap the benefits of higher yields, better harvests, and a stronger balance sheet — all this because of the qubit.
The “qubit” is quantum computing’s replacement for bits — the sets of 1’s and 0’s that underlie all of the calculations that ordinary computers make. What makes the qubit different is that it takes advantage of the strange behavior of subatomic particles to perform certain types of highly complex calculations at blazing-fast speeds. The process relies in particular on the unique properties of quantum physics called superposition and entanglement. The short version is that a qubit isn’t a one or a zero, but rather it holds a range of values between 1 and 0 while also being able to affect other qubits.
Physicists and mathematicians worked out how these special properties could be harnessed in computer programs that simulate how the real world works. In agriculture, for example, we know that if you want to estimate how a seed might perform, the answer depends on hundreds of variables. Temperature, weather, soil type, insect pressure, the timing of planting, input levels, and so on, are all factors that affect the outcome. The more qubits in your quantum computer, the more variables like this can be accounted for.
The machines we currently use in agriculture can perform this analysis, but in a crude way. Such a calculation relies on statistical averages, and as a consequence the answers can only be expressed as ranges of probability. While we might then know there’s a 60-70 percent likelihood that this seed will do well in a given set of conditions, a great deal of uncertainty remains. Quantum computing holds out the promise of running the numbers and actually solving the equation. It might come up with an answer that’s closer to 90 or 100 percent likely to succeed, with less potential for experimental error and less uncertainty. Improvement of that magnitude would shave years off the time it takes to bring new seeds to market.
As quantum computing technology matures, we will face the even more intriguing prospect of unlocking the mysteries of plant genetics for the first time. Each plant contains thousands of genes that determine its potential, and the way that multiple genes interact with one another is so fantastically complex that geneticists simply do not have the computing capacity needed to explore which genes control which functions that are responsible for producing the highest yields for various circumstances.
We currently engage in a process of trial-and-error to figure out which gene might express a desired trait, and that’s where we end up losing the most time. We’re nowhere near the point of understanding everything there is to know about a simple corn or soybean seed. With enough qubits, quantum computing hardware could try out all of the combinations. We’d identify the exact genes that need to change and come up with the code to unlock the highest yielding varieties expressing the desired sets of traits.
By knowing which genes to change, tools like CRISPR can finally be used to their full potential by modifying the plant’s genetic structure to create those desired traits directly, rather than through expensive and time-intensive breeding programs. If it all comes together, farming would never be the same again, because the ultimate seed will have been created. Or, more accurately, it would be a range of ultimate seeds. It’s likely that varieties would still have to be adapted to suit varying sets of conditions.
As amazing as this prospect may sound, there is a catch. The quantum computers available today only have a handful of qubits, which doesn’t offer enough number-crunching power to beat a conventional supercomputer. Worse yet, adding more qubits is among the greatest engineering challenges we face. The particles must be perfectly isolated from every form of interference. For example, subatomic particles are so sensitive that calculations can be thrown off if the temperature rises above 0.015K — that’s colder than interstellar space.
The biggest players in technology seem to think they can overcome the technical hurdles, and they’ve bet big on quantum computing. If they’re right, this will be one of the most accessible technology revolutions ever. The power of prototype quantum hardware is already on tap for anyone who takes the time to learn how to use it. Companies like Amazon and D-Wave offer free, or low-cost, access to quantum machines that use open-source programming tools and languages right now.
It’s worth watching to see how the industry adapts to the rapid level of change that is coming sooner than most might imagine.
Joseph Byrum holds a Ph.D. in quantitative genetics and an MBA from the University of Michigan. He has held executive positions in both agriculture and finance.
* All views, data, opinions and declarations expressed are solely those of the author(s) and not of Global AgInvesting, GAI News, or parent company HighQuest Group.
2 Page 57. Investment intensity at the top four Big Pharma firms in 2019 averaged 18.2%.