If you ask researchers which quantum computing application they are most confident about, many will say chemistry. The reason goes back to the very origins of the field.
Feynman’s idea
In 1981, physicist Richard Feynman pointed out that nature follows quantum rules, so simulating it on ordinary computers is enormously difficult. His suggestion was to build computers that follow quantum rules themselves. That idea is one of the origins of quantum computing.
Why molecules are hard to simulate
How a molecule behaves depends on how its electrons interact. For many molecules, ordinary computers do this well, and methods such as density functional theory are used every day in chemistry and drug design.
But for some molecules, especially those with metal atoms and many closely interacting electrons, the calculations grow too fast for any ordinary computer to handle accurately.
A famous example: fertiliser
Making fertiliser with the century-old Haber–Bosch process uses an estimated 1–2% of the world’s energy. Some bacteria do the same job at everyday temperatures using an enzyme called nitrogenase, whose active centre is a cluster of metal atoms known as FeMoco. Understanding FeMoco could inspire cleaner fertiliser production, but it is too complex to simulate accurately today.
In 2017, researchers showed how a large, error-corrected quantum computer could study FeMoco, making it a benchmark goal for the field. Later work has reduced the estimated resources, but they remain far beyond today’s machines.
Other promising areas
- ◆Batteries: understanding the chemistry of new electrode and electrolyte materials.
- ◆Medicines: modelling how candidate drugs bind to proteins, especially where metals are involved.
- ◆Catalysts: designing materials that speed up industrial reactions and cut energy use.
- ◆Materials: exploring superconductors and magnets.
What’s possible today
Current machines have simulated only small molecules, such as hydrogen and lithium hydride, which ordinary computers already handle easily. Methods like the Variational Quantum Eigensolver (VQE), first demonstrated in 2014, share the work between quantum and ordinary computers to cope with noise. They are valuable for learning, but have not yet beaten the best classical methods on a useful problem.
task_altKey takeaways
- check_circleMolecules follow quantum rules, making them a natural fit for quantum computers.
- check_circleFeMoco, key to natural fertiliser production, is a benchmark target.
- check_circleToday’s demonstrations use small molecules; useful advantage needs error-corrected machines.