Inspiration
Some quantum computing problems have exact solutions that are VERY hard to compute, perhaps even impossible. So how can we create approximations and justify that they are within some margin of error?
What it does
Enter Zero-Noise Extrapolation and Symmetry/Parity Verification, with which we can throw out intentionally noisy data, then extrapolate what a noiseless circuit would give us if it existed.
How we built it
Using the provided python library and AI for rapid prototyping, we scowered tons of research papers looking for methods to reduce noise through software, while running a higher order approximation.
Challenges we ran into
Many methods simply did not increase the accuracy compared to the baseline, simply due to the increase in 2q gate count, the true limiting factor.
Accomplishments that we're proud of
Achieved nearly noisless order 1 trotter decomposition levels of RMS, while running the comparatively more noisy order 3 ruth decomposition, improving upon the noisy baseline.
What we learned
We learned about various denoising techniques, how and why they works, as well as the drawbacks of each. Given our constraints its crucial to choose those that are feasible within our time frame and budget.
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