How Quantum Computing Could Offer Clearer Pathways to Discovery
(ScientificAmerican) Quantum computing offers the possibility of major advances in chemistry i
the coming years. Modeling energetic reactions on classical computers requires approximations, since they can’t model the quantum behavior of electrons over a certain system size. Each approximation reduces the value of the model and increases the amount of lab work that chemists have to do to validate and guide the model. Quantum computing, however, is now at the point where it can begin to model the energetics and properties of small molecules such as lithium hydride, LiH—offering the possibility of models that will provide clearer pathways to discovery than we have now.
Today, theoretical research and modeling chemical reactions to understand experimental results is commonplace, as the theoretical discipline became more sophisticated and bench chemists gradually began to incorporate these models into their work. The output of the models provides a useful feedback loop for in-lab discovery.
The limiting factor of these models, however, is the need to simplify. At each stage of the simulation, you have to pick a certain area where you want to make your compromise on accuracy in order to stay within the bounds of what the computer can practically handle.
The quantum approach is different. At its purest, quantum computing lets you model nature as it is; no approximations. In the oft-quoted words of Richard Feynman, “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.”
So far, we have extended the use of quantum computers to model energies related to the ground states and excited states of molecules. These types of calculations will lead us to be able to explore reaction energy landscapes and photo-reactive molecules.
Looking ahead, we’ve started laying the foundation for future modeling of chemical systems using quantum computers and have been exploring different types of calculations on different types of molecules soluble on a quantum computer today.
Author Jeannette Garcia does not envision a future where chemists simply plug algorithms into a quantum device and are given a clear set of data for immediate discovery in the lab. What is feasible—and may already be possible— would be incorporating quantum models as a step in the existing processes that currently rely on classical computers.
The next generation of chemists emerging from grad schools across the world brings a level of data fluency that would have been unimaginable in the 2000s. But the constraints on this fluency are physical: classically built computers simply cannot handle the level of complexity of substances as commonplace as caffeine. In this dynamic, no amount of data fluency can obviate the need for serendipity: you will be working in a world where you need luck on your side to make important advances. The development of— and embrace of—quantum computers is therefore crucial to the future practice of chemists.