Today we're going to talk about quantum computing applications in chemistry.
For many years, computational chemists have been using classical computational methods
to attempt to solve chemistry problems. My background is in software development,
but I'm particularly passionate about the applications of software within
chemistry and the particular advantages that quantum computing has the potential to bring.
Many computational chemists will use popular software packages such as Gaussian,
PySCF, and Psi4, these plus many others. All of these different packages provide a suite
of tools to enable computational chemist to do research into various different types of
chemistry problems, and they all use particular sets of data to be able to do this work. And
some of the important data points can include information about molecules ground state energy,
excited state energy, as well as potential energy surfaces and many more. But how do
we actually calculate this information for a given molecule? Let's take H2O, for example.
So in order to calculate the ground state energy of water from a classical perspective,
first we're going to need some information about the molecule itself. We want to know
the atoms that are involved as well as maybe some different coordinates in space.
And as well as this, we're also going to take a set of functions,
known as a base asset, which you can think of as essentially a mathematical representation of the
different orbitals within the H2O molecule. So in order to and then we take this information
and what we're going to do is we're going to plug it into a Schrodinger equation.
Schrodinger equation is a very important equation within chemistry and quantum quantum mechanics
generally, and it defines the quantum system itself. And this value E here represents the
energy of our quantum system. In this case, our quantum system is the h to a molecule.
So if we minimize this value of E. We can get the ground state energy.
So how do we actually go about finding this minimum value of E? Well, there are a few
different things we can do, but firstly what we're wanting to do is make some assumptions to simplify
the problem a bit. We can use the Bon Oppenheimer approximation as well as the Hartree-Fock
approximation.
And these assumptions essentially make it easier to compute solutions to this equation.
And once we've done that, our output will be a approximation of that ground state energy.
So so far, everything we've done up until this point, we can do classically with classical
computers. However, there are a few problems with this. The first one being that the accuracy of
this value will decrease as the complexity of our quantum system increases. As well as this,
if we want to do any further processing beyond Hartree-Fock, this becomes exponentially
more difficult and computationally very expensive very quickly. And this is why software packages
like the ones I mentioned before, will often leverage GPUs and high performance computers
in order to try to solve this equation for more complex molecules. But quantum computers have
the potential to simulate these more complex molecules more efficiently and to a higher
degree of accuracy than just Hartree-Fock processing. And this is because quantum
computers process information in a fundamentally different way than classical computers do.
So let's go back to our Schrodinger equation for a minute. We can essentially map this equation
onto qubits, onto quantum bits, and we can also incorporate some of these approximations as well.
So we can represent this in quantum circuit form.
And because we've done this now, we can leverage key quantum phenomena such as superposition
and entanglement.
Then if we're using Qiskit runtime,
we can take this quantum circuit and use it together with an estimater primitive,
as well as an optimizer, and we can take all of that and plug it into
a variational quantum eigensolver algorithm. This is a very important
quantum computing algorithm that enables us to calculate eigen eigenvalues efficiently.
So if we're and if we're using primitives, these are unique to IBM's Qiskit runtime.
They are predefined programs that help users to optimize their workloads and execute them
efficiently on quantum systems. The estimates are primitive in particular makes it easy to extract
solutions from this circuit and also give us incredibly fine grained control over the system,
hardware and optimization routines so we can generate the best results possible.
So once we've done all of that, we will get an output, which is again an approximation of the
ground state energy for our H2O molecule. But this value that we've calculated here is often
more precise than just doing Hartree-Fock calculations alone. And it doesn't consume
compute resources as fast. And this is why quantum computing researchers, particularly in the field
of chemistry, are incredibly excited about the potential for quantum computers to have a real
impact on the chemistry industry specifically, and with open source tools like Qiskit runtime
and Qiskit nature. Anyone can get started experimenting with this today and will leave
some links in the description for you to get going with that. Thank you very much. I hope you enjoyed
this video. Remember to like, subscribe, leave any questions that you have in the comments,
and remember to check out other quantum videos on this channel. [00:00:00][0.0]