Applications
Quantum Chemistry Simulation
Using quantum computers to model the behavior of molecules and materials, directly connected to Feynman's original motivation for the field.
Definition
Quantum chemistry simulation refers to using quantum computers to model how electrons behave within molecules and materials — a problem that grows exponentially difficult for classical computers as molecule size increases, but one quantum computers may handle more naturally since they're built on the same quantum mechanical principles.
In depth
Quantum chemistry simulation uses quantum computers to calculate the properties of molecules and materials from the laws of quantum mechanics. The central task is to find the lowest energy of the electrons in a molecule, given the positions of the atoms, since that energy determines bond lengths, reaction energies, and stability. The electrons repel each other, and the number of possible electron configurations grows exponentially with the number of orbitals, so exact classical solutions become impossible beyond small systems, and approximations such as density functional theory fail for strongly correlated cases.
The workflow starts with a classical calculation that chooses an active space of the most important orbitals, writes the Hamiltonian in second quantization, and maps electrons to qubits with a transformation such as Jordan-Wigner. Quantum phase estimation can then find the ground-state energy with controlled accuracy on a fault-tolerant machine, while the variational quantum eigensolver gives approximate answers on noisy devices. The goal is chemical accuracy, about 1.6 millihartree or one kilocalorie per mole. Resource estimates for catalysts such as the iron-molybdenum cofactor of nitrogenase and for cytochrome P450 point to thousands of logical qubits and millions of physical ones.
Hardware demonstrations have grown slowly. The first molecular energy calculation on a quantum device was done for helium hydride in 2014 on a photonic chip, and Google's 2020 work simulated a chain of hydrogen atoms with 12 qubits. Today's experiments cover small molecules that classical methods solve exactly, and so far show feasibility rather than advantage. The strongest long-term case is for transition-metal catalysts and strongly correlated materials, relevant to fertilizer, batteries, and drug metabolism.
Technical Definition
Near-term approaches typically use algorithms like VQE combined with techniques like active space selection to fit calculations within the qubit and circuit depth constraints of current NISQ hardware, focusing on the most chemically relevant electrons rather than simulating an entire molecule in full detail.
Visual Explanation: An Analogy
Think of quantum chemistry simulation like using a tool built from the same fundamental material as the thing you're trying to understand — using quantum mechanics (via a quantum computer) to model quantum mechanical behavior (electrons in molecules), rather than approximating it with purely classical mathematics.
Key facts
| Item | Detail |
|---|---|
| Goal | Ground-state energies to chemical accuracy, about 1.6 millihartree |
| Fault-tolerant method | Quantum phase estimation |
| Near-term method | Variational quantum eigensolver |
| Resource estimates | Thousands of logical qubits and millions of physical qubits for FeMoco and P450 |
| Milestones | HeH+ on a photonic chip (2014); 12-qubit hydrogen chain at Google (2020) |
Where the idea comes from
Aspuru-Guzik and coauthors proposed quantum computing for chemistry in 2005, and the first experiments came in 2010 and 2014. Resource estimates for industrial catalysts began with Reiher and coauthors in 2017.
Real-World Use Cases
- Central to the drug discovery applications discussed in our Healthcare industry coverage
- Equally relevant to materials science applications like battery and catalyst design
How it differs from related ideas
- Quantum simulation targets strongly correlated systems where classical approximations fail.
- Classical methods such as DFT and coupled cluster work well for many molecules and are cheaper.
- AI-based potentials are a growing classical competitor.
Frequently asked questions
What is quantum chemistry simulation?
Computing the energy and properties of molecules from quantum mechanics, which quantum computers are expected to do more accurately than classical methods for hard cases.
Has a quantum computer simulated a useful molecule?
Only small molecules so far, which classical computers can also solve. Useful industrial molecules need much larger machines.
What is chemical accuracy?
An energy error of about 1.6 millihartree, roughly one kilocalorie per mole, needed to predict reaction outcomes.
Related Terms
Keep exploring
- Algorithm: Variational Quantum Eigensolver (VQE)
- Algorithm: Quantum Phase Estimation (QPE)
- Algorithm: Trotter-Suzuki Decomposition
- Hardware: Quantinuum H2
- Hardware: Google Sycamore
- Industry: Quantum Computing for Pharmaceuticals and Drug Discovery
- Industry: Quantum Computing for the Chemical Industry
- Industry: Quantum Computing for Materials Science