My team (Tasha Pais, Shivam Syal, Thomas Breydo) and I are developing a moonshot company leveraging quantum machine learning to find a 10x more efficient catalyst to use in direct air carbon capture, ultimately creating a more scalable and cost-effective process.

Introduction: Climate Change

Climate change is a natural cycle. However, excessive greenhouse gases released by humans are accelerating this process due to its heat trapping nature. A prevalent greenhouse gas is carbon dioxide, which is released through both natural processes (such as respiration) as well as human activities, such as deforestation and burning fossil fuels. The issue is that humans have created an imbalance in the atmospheric CO2 levels — its concentration has increased by 47% since the Industrial Revolution, making it a significant contributor to climate change.

Direct Air Capture and Carbon Conversion

A direct air capture facility

Since CO2 is a stable molecule (making it unreactive), the chemical reaction in DAC needs a catalyst for it to occur. This catalyst serves to speed up the reaction by “setting it up”.

source

The reaction above shows carbon dioxide and hydrogen being converted to methanol with the help of a ruthenium-based catalyst.

The issue is, metal oxide catalysts such as the ruthenium-based one above are incredibly energy intensive (requiring temperatures of over 300° C to operate). Not only is this inefficient reaction costly, but it also creates heat and carbon monoxide as a byproduct.

Finding more efficient catalysts requires lengthy trial-and-error in a lab. Additionally, classical computers are unable to accurately simulate these reactions because they are incapable of precisely calculating the Hamiltonian (energies) of many electrons. These types of problems can get very complex very quickly as computers must simultaneously calculate the interactions between electrons in multiple atoms. In fact, simulating such a molecular reaction on a classical computer can take more time than the age of the universe!

Luckily, quantum computers exist.

Quantum Computing

Because of this superposition property, quantum computers are excellent at performing many calculations simultaneously. This means it can easily solve the Schrödinger Equation, which is a physics equation whose solution gives the total energy of a system (the Hamiltonian).

A quantum machine learning algorithm called the Variational Quantum Eigensolver (VQE) can assist in finding the lowest energy configuration of an atom. Fundamentally, it can calculate the eigenvalues of a large Hamiltonian matrix. Here’s how it works:

  1. Information about a particle’s electron orbitals are encoded into qubits. From this, a circuit architecture (called the ansatz) prepares an initial “trial” wave function representing the particle. This will eventually serve as a guess for the ground state energy.
  2. Then, the ground state energy of this wavefunction is calculated and sent to a classical optimizer as an “expectation value”.
  3. The classical optimizer will adjust certain parameters in the quantum circuit to get a lower expectation value. Repeat!
source

Eventually, this process will iterate until the expectation value converges with the ground state energy, which is the smallest eigenvalue in the Hamiltonian.

So how does this relate to finding an ideal, efficient ruthenium catalyst?

source

In the diagram above, the steps on the right reflect the VQE. A classical computer prepares a Hamiltonian matrix in an active space, which is a certain section of the reaction that is the most relevant. This simplification is used to reduce computational speeds. Then, the quantum computer solves this Hamiltonian and passes on the expectation value to the classical computer. Now that the classical computer understands the energy levels of the catalyst, it will perform a kinetic analysis of the reaction and gauge the rate of the reaction. Next, it can modify the catalyst to make it more efficient, and repeat the process with the new catalyst structure. After a number of iterations, an “ideal” catalyst is found.

Feasibility

Quantum Computing Enthusiast | Innovator at The Knowledge Society | CS @ UPenn | Social Entrepreneurship