The simulation problem
Every chemistry calculation — modelling a drug binding to a protein, predicting how a catalyst works, designing a new battery material — requires simulating electrons. Electrons obey quantum mechanics, not classical physics. Describing the exact quantum state of a molecule with n electrons requires tracking 2ⁿ probability amplitudes.
For a molecule with 50 electrons, a complete classical simulation would require more memory than exists on Earth. In practice, chemists use approximations — density functional theory (DFT) and coupled cluster methods — that work well for small molecules but become inaccurate or computationally prohibitive for large ones.
Quantum computers, which store information in qubits that can themselves exist in quantum superpositions, are a natural fit for this problem. For background on electron orbitals and atomic structure, see our atomic structure guide.
Priority targets
Three chemical problems are frequently cited as leading candidates for quantum advantage:
- Nitrogen fixation — the nitrogenase enzyme in certain bacteria converts atmospheric N₂ to ammonia at room temperature, using a complex iron-molybdenum cofactor. Understanding exactly how it works could lead to energy-efficient fertiliser production that currently consumes ~1.5% of global energy via the Haber–Bosch process.
- Drug–protein binding — accurately predicting how a drug molecule binds to a target protein would transform pharmaceutical development, reducing the enormous cost of failed clinical trials.
- Battery and solar cell materials — quantum simulation of electron correlations in materials could accelerate the design of next-generation energy storage and conversion systems.
Where the field stands
In 2022, Google demonstrated quantum simulation of a small superconducting molecule using 12 qubits. IBM's quantum roadmap targets 100,000 qubits by 2033. Microsoft is pursuing topological qubits that are inherently more error-resistant.
The current generation of NISQ (noisy intermediate-scale quantum) devices can run the VQE algorithm for molecules up to roughly the complexity of a small organic compound. Fault-tolerant hardware — needed to tackle industrially meaningful molecules — requires quantum error correction that still demands orders of magnitude more physical qubits than currently available.
For the broader context of how chemical bonds and molecular structure work, see our chemical bonding guide. For the search for exotic new atoms, see the element 119 article.