Conducting Chemistry on a Computer
🧪 Utilizing high power computers to simulate chemistry!
Have you ever heard of the protein folding problem, or wanted to test the ground state energy of a molecule, test for aging and lifetime in lithium storage cells or determine the absorbance of beryllium silicon? Well, there’s a single technology that can simulate all of this without the need for a single lab instrument, which allows us to 10x processes like developing more efficient solar cells, drug discovery, and chemical energy storage through capacitors. This technology is quantum machine learning.
So quantum computing is clearly a revolutionary field, but it’s intersection with artificial intelligence is what will breed a new means of simulating important materials as well as life saving medicines. The way this is done is through something called quantum artificial intelligence, or quantum machine learning in which we leverage machine learning algorithms, and enhance their output using a quantum computer, a system that leverages the laws of quantum physics.
Due to chemistry occurring in the quantum realm, it is more efficient to use a quantum computer, which draws fundamental laws directly from the quantum field, manipulating subatomic particles. One of the most practical applications of this is photonic quantum computing, which is a more niche brain of linear-optic quantum computing, which involves the use of light based circuits for quantum information processing, or QIP, which allows for the analysis of practically any data to be executed via a quantum computer. The subatomic particles concerned with photonic quantum computing are photons, which are essentially extremely small globules of light that give light its particle-wave duality phenomenon.
In leveraging different open source qunatum computing, numerous comapnies, including IBM, have been able to offer online circuit building and APIs to allow people who aren’t in the exclusive ecosystem to still have the priviledge of using quantum technology as a resource to develop quantum machine learning algorithms.
In my personal studies, I’ve been using quantum machine learning as a medium for drug discovery and material composition. Essentially, what I’m doing is simulating the chemical steps that would perform best under a give circumstance, like finding which materials is best for solar cells based on chemical absorbence, or which molecules would be best for composition of a drug to combat an infectious disease. However, check out my next articles if you want to get a sense of how this was done, I’ll be posting about it soon. Today, we’ll be talking about the basics of how to get started with this using something called the Variational Quantum Eigensolver (VQE).
Variational Quantum Eigensolver
On a high level, the VQE is a quantum-classical hybrid algorithm that employs use of our conventional computation and combines it with quantum computing in order to (sometimes, for simple uses) simulate and predict the ground state energy of a molecule.
As you probably know from basic chemistry, a ground state is a quantum mechanical system, which means that its a phenomena that occurs on the and is caused because of the quantum scale, or rather a physical happening that is becuase of a quantum mechanism. So, what better tool to use for quantum calculations than a quantum device, right? Correct! So the way this quantum chemistry algorithm works is by applying a product called the Ritz Variational Principle. This principle tests Ψ (called psi) on a quantum system. Ψ represents a trial wave function, or an ansatz, or a mathematical assumption that leads to developing a solution. The VQE makes use of the Ritz variational principle — which actually serves as its foundation — in which the quantum computer sets up the Ψ, but instead allocates Ψ to a molecule, effectively estimating the molecular Hamiltonian (total KE of a qauntum system), with the quantum circuits being tweaked concurrently to predict the ground state energy of the molecule, and allows for multiple iterations of the estimation. Some simple applications of this is with a water molecule:
So through some simple code, you can estimate the ground state energy of a molecule, which extremely important in determining how great of a candidate it is for medicine, or its reactivity, as it provided insights on energy jumps with photons, and also helps to determine the electron configurations. What molecule will you simulate?
My name is Okezue Bell, and I’m an innovator/entrepreneur in the quantum computing and AI spaces. I’m also currently making developments in foodtech and cellular agriculture, as well as biocomputing!
✉️ Email: okezuebell@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/okezue-a-...
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📱 GitHub: https://github.com/BellAI-Code
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