# Quantum Chemistry

The accurate description of a molecule involves understanding the
distance between the atoms and the strength of their *atomic bonds*
(the sharing of electrons so that the atoms stick together in a
molecule), the position of the atoms, and the way they vibrate. Much
of this work involves understanding what happens as they absorb and
release energy, and finding the minimum amount of energy (known as the
*ground state energy*) in the molecule. Answering these questions
accurately involves equations with a number of terms that grows
*exponentially* in the number of electrons and atoms involved. This
makes the necessary calculations tedious for a classical computer, but
ideally suited to a quantum computer.

## The basic problem

The most basic problem that is to find the electronic ground state energy of a molecule, for a particular geometry. Intuitively, the ground state is one that doesn’t change over time, since there is no extra energy in the molecule.

To find this energy, we take the equations that represent the global
changes to the system over time, called the *Hamiltonian* of the
system (written , but not to be confused with the Hadamard gate
we presented last week), and look for a state that doesn’t change as
the Hamiltonian is applied. (This process is like knowing, for
example, that the Earth is spinning but not being certain where the
North Pole is, and finding it by searching for a place that doesn’t
move as the Earth rotates.) With that state, we can then find the
energy, which shows up as a constant factor in the equation.
Mathematically, with the state being , the equation is
written and is
called the *eigenvector* and is called the *eigenvalue*.

In a quantum algorithm, we can read off that when we measure
the state. The challenge, then, becomes to prepare the initial state
and apply the Hamiltonian efficiently. The
most basic approach to solving this problem uses an algorithm called
*quantum phase estimation*.

## Representing data

Our first problem in designing any algorithm is determining what our data representation is going to be. There are a variety of ways we can write down our current condition: either we can keep track of each particle (electron) in the system, or we can keep track of each point in space, for example. Which choice is ultimately more efficient depends on the details of the problem.

## Hybrid quantum-classical algorithms

Doing the above efficiently is a challenge, and requires that the
entire quantum computer be very high fidelity. Illustrated at the top
of this article is an alternative approach, iterating between quantum
and classical approaches, has been developed. The two most important
such algorithms are the *variational quantum eigensolver* (VQE) and
and the *quantum approximate optimization algorithm* (QAOA).

## Problems attacked

Richard Feynman himself performed important work developing algorithms and concepts, and in recent years the teams of Seth Lloyd (MIT), Alan Aspuru-Guzik (Harvard), and Ed Farhi (MIT), and Dave Wecker and the team of Krysta Svore (Microsoft) have worked to make the algorithms concrete. These are far from the only researchers in the area; dozens of researchers assembled at the White House last year for a workshop that produced a report on the field that is very readable and up to date. A link is provided in the Further Study article at the end of this course.

Simple demonstrations that the algorithm works have been done for the hydrogen molecule on several different real quantum computers. Just as the article was being written, the IBM Quantum Experience team published a paper demonstrating that their seven-qubit experimental quantum processor (a type of superconducting system; we will learn more about hardware next week) can solve several small problems using these techniques. They worked on molecules as large as beryllium hydride (BeH). Microsoft has stated that their vision is to better understand a molecule known as FeMoCo, which, as we described last week, has the potential to improve the production of agricultural fertilizers.

© Keio University