#kp The main aim of molecular simulation, on any machine, is to **find a compound’s ground state—its most stable configuration.** This is no trivial task because it **requires simulating the interactions between all the particles, such as electrons, in the molecule.** And the bigger and more complex a molecule and its environment is, the more difficult this process becomes. Today’s **supercomputers can simulate fairly simple molecules, but** when researchers try to develop novel, complex compounds for better batteries and life-saving drugs, traditional computers can no longer maintain the **accuracy they have at smaller scales**. The solution has typically been to model experimental observations from the lab and then test the theory. The largest chemical problems researchers have been so far able to simulate classically, meaning on a standard computer, by exact diagonalization (or FCI, **full configuration interaction**) comprise around **22 electrons and 22 orbitals**, the size of an active space in the pentacene molecule. For reference, a single FCI iteration for pentacene takes ~1.17 hours on ~4096 processors and a full calculation would be expected to take around [nine days](https://arxiv.org/ftp/arxiv/papers/1707/1707.04346.pdf). For any larger chemical problem, **exact calculations** become prohibitively slow and memory-consuming, so that approximation schemes need to be introduced in classical simulations, which are not guaranteed to be accurate and affordable for all chemical problems. It’s important to note that reasonably accurate approximations to classical FCI approaches also continue to evolve and is an active area of research, so we can expect that accurate approximations to classical FCI calculations will also continue to improve over time. That’s where quantum computers come in. **Qubits themselves operate according to the laws of quantum mechanics, just like the molecules researchers are trying to simulate.** The hope is that in time quantum computers can greatly speed up the simulation process by precisely predicting the properties of a new molecule that can explain its behavior, such as reactivity. Programming qubits works by using unique properties of superposition and entanglement, allowing the potential for researchers to evaluate a expectation parameters – in a much more efficient way than a standard computer ever could. In September 2017, a paper by an IBM team titled ‘[Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets](https://www.ibm.com/blogs/research/2017/09/quantum-molecule/)on simulating hydrogen (H2), lithium hydride (LiH), and beryllium hydride (BeH2) molecules, made it onto the cover of _Nature_ magazine. The research described a **new hardware efficient ansatz to calculate the ground state of these molecules, by mapping the electronic structure of the orbitals onto a subset of a quantum processor, encoding from orbitals to qubits.** The results were groundbreaking and laid the foundations of simulating different molecules with quantum computers https://www.nature.com/articles/nature23879