Battery technology is crucial to help humanity rid itself of fossil fuels and climate change. To improve batteries, we need a way to figure out the best materials to use to manufacture them.
Inspiration: We saw innovations in batteries from companies like Tesla and from partnerships with companies like IonQ and Hyundai and we realized that quantum computing could be used to help with innovation in this field.
What it does: It finds the highest score corresponding to different battery compositions.
How we built it: We used Qiskit and Python to create a dataset that Grover Search uses to find the greatest number.
Challenges we ran into: The main challenge was implementing the Grover Search algorithm.
Accomplishments that we're proud of: We created a dataset that Grover Search uses to find the greatest number from.
What we learned: It can be difficult to program a quantum computer, and a quantum-based programming language may help with efficiency in this.
What's next for Battery Composition Optimizer: testing on a quantum computer, creating an interactive user interface, converting the datasets into quantum states for the Grover's Search algorithm to reference.
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